Patent Publication Number: US-2021185515-A1

Title: Neural network configuration for wireless communication system assistance

Description:
RELATED APPLICATIONS 
     This application claims the benefit of priority to U.S. Provisional Application No. 62/948,703 entitled “NEURAL NETWORK CONFIGURATION FOR WIRELESS COMMUNICATION SYSTEM ASSISTANCE” filed Dec. 16, 2019, and U.S. Provisional Application No. 62/959,072 entitled “FLEXIBLE CONFIGURATION OF FUNCTION BLOCK PARAMETERS AMONG DC/CC/SUL/BWP” filed Jan. 9, 2020, the entire contents of both of which are incorporated herein by reference for all purposes. 
    
    
     FIELD OF TECHNOLOGY 
     The following relates generally to wireless communications and more specifically to neural network configuration for wireless communication system assistance. 
     BACKGROUND 
     Wireless communications systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be capable of supporting communication with multiple users by sharing the available system resources (e.g., time, frequency, and power). Examples of such multiple-access systems include fourth generation (4G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple-access communications system may include one or more base stations or one or more network access nodes, each simultaneously supporting communication for multiple communication devices, which may be otherwise known as user equipment (UE). In some examples, a UE may perform various functions (e.g., process one or more signals) using one or more neural network blocks. 
     SUMMARY 
     The described techniques relate to improved methods, systems, devices, and apparatuses that support neural network configuration for wireless communication system assistance. Generally, the described techniques provide for communicating capability information (e.g., regarding neural network blocks capable of being utilized by a user equipment (UE) and a base station). A base station may configure one or more neural network block parameters, and may transmit the neural network block parameters to the UE. The UE may configure or reconfigure a neural network block according to the neural network block parameters, and may process one or more signals generated or received by the UE using the neural network block and the neural network block parameters. The UE may also provide feedback information (e.g., when requested by the base station) indicating how well the neural network block is performing with the configuration. The base station may use the feedback information when providing neural network block parameters to the UE and other served UEs, to configure or reconfigure the neural network block for improved performance. 
     A method of wireless communications at a UE is described. The method may include transmitting, to a base station, capability information indicating one or more neural network blocks supported by the UE, receiving, from the base station, one or more neural network block parameters based on the transmitting of the capability information, and processing one or more signals generated by the UE using a first neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     An apparatus for wireless communications at a UE is described. The apparatus may include a processor, memory coupled with the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to transmit, to a base station, capability information indicating one or more neural network blocks supported by the UE, receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information, and process one or more signals generated by the UE using a first neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     Another apparatus for wireless communications at a UE is described. The apparatus may include means for transmitting, to a base station, capability information indicating one or more neural network blocks supported by the UE, receiving, from the base station, one or more neural network block parameters based on the transmitting of the capability information, and processing one or more signals generated by the UE using a first neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     A non-transitory computer-readable medium storing code for wireless communications at a UE is described. The code may include instructions executable by a processor to transmit, to a base station, capability information indicating one or more neural network blocks supported by the UE, receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information, and process one or more signals generated by the UE using a first neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     Various aspects may include transmitting, to a base station, capability information indicating one or more neural network blocks supported by the UE, receiving, from the base station, control signaling with one or more neural network block parameters based at least in part on the transmitting of the capability information, processing one or more signals generated by the UE using a first neural network block of the one or more neural network blocks and the one or more neural network block parameters; and transmitting, to the base station, an acknowledgement message indicating that the control signaling has been successfully received. 
     In some aspects, the one or more neural network block parameters may further include one or more adjustment parameters to the first neural network block used to process the one or more signals by the UE, and the method may further include adjusting the first neural network block according to the one or more adjustment parameters, wherein processing the one or more signals using the first neural network block may be based at least in part on the adjusting. 
     In some aspects, the control signaling may include a resource allocation message including the one or more neural network block parameters for configuring network components. In some aspects, the network components may include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or a combination thereof. In some aspects, the one or more cell groups may include a master cell group, a secondary cell group, a supplementary cell group, or a combination thereof. 
     Some aspects may further include receiving, from the base station, the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are received over the physical downlink control channel, and configuring the first neural network block in place of a default network function block of the UE based at least in part on receiving the one or more neural network block parameters, wherein processing the one or more signals using the first neural network block is based at least in part on the configuring. 
     Some aspects may further include transmitting a configuration of the configured first neural network block to the base station over a physical uplink control channel or a physical uplink shared channel. 
     Some aspects may further include receiving configuration information over a physical downlink control channel, wherein the configuration information includes an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. 
     Various aspects may include receiving, from a UE, UE capability information indicating that the UE supports one or more neural network blocks, configuring, based on the received UE capability information, control signaling with one or more neural network block parameters for a first neural network block of one or more neural network blocks supported by the UE, transmitting the control signaling to the UE, and receiving, from the UE, an acknowledgment message indicating that the control signaling has been successfully received by the UE. 
     In some aspects, transmitting the control signaling further may include transmitting, to the UE, the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are transmitted over the physical downlink control channel according to the control signaling. 
     In some aspects, transmitting the control signaling further may include transmitting configuration information for the first neural network block and the one or more neural network block parameters over a physical downlink control channel, wherein the configuration information includes instructions for the UE to configure the first neural network block in place of a default network function block of the UE. 
     Some aspects may further include receiving a configuration of a configured first neural network block from the UE over a physical uplink control channel or a physical uplink shared channel, wherein the configured first neural network block has been configured by the UE based at least in part on the one or more neural network block parameters. Some aspects may further include adjusting the first neural network block according to the one or more adjustment parameters, wherein processing the one or more signals using the first neural network block may be based on the adjusting. 
     Some aspects may further include receiving a resource allocation message for a physical downlink shared channel, and monitoring the physical downlink shared channel for the one or more neural network block parameters based on receiving the resource allocation message, wherein receiving the one or more neural network block parameters may be based on monitoring the physical downlink shared channel. In some aspects, the resource allocation message may include a downlink control information (DCI) message, a media access control (MAC) control element (MAC-CE) or radio resource control (RRC) message, and the one or more neural network block parameters may be received as part of a downlink data message. 
     In some aspects, the one or more adjustment parameters include an activation indication for a node of the first neural network block, a deactivation indication of the node of the first neural network block, a weight value for the node of the first neural network block, an adjustment to a weight value for a submodule, or a bias value for the node of the first neural network block, or a combination thereof. 
     Some aspects may further include performing a first operation on the one or more signals using a first submodule of the first neural network block based on a first weight value indicated by the one or more adjustment parameters, and performing a second operation on the one or more signals using a second submodule of the first neural network block based on a second weight value indicated by the one or more adjustment parameters, where processing the one or more signals using the first neural network block may be based on performing the first operation and the second operation. 
     Some aspects may further include identifying the first neural network block having one or more configuration options, where the one or more neural network blocks supported by the UE may include the first neural network block. 
     Some aspects may further include receiving, from the base station, a resource allocation message for a physical downlink shared channel, receiving configuration information for the first neural network block and the one or more neural network block parameters over the physical downlink shared channel based on receiving the resource allocation message, and configuring the first neural network block in place of a default network function block of the UE based on receiving the configuration information and the one or more neural network block parameters, where processing the one or more signals using the first neural network block may be based on the configuring. In some aspects, the resource allocation message may include a radio resource control message, or a downlink control information message. 
     In some aspects, the configuration information may include an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. 
     Some aspects may further include identifying the default network function block, where the one or more neural network blocks supported by the UE may include the default network function block. 
     Some aspects may further include receiving control information from the base station that indicates a new network function block to be the default network function block, where identifying the default network function block may be based on receiving the control information. 
     Some aspects may further include monitoring for the configuration information based on receiving the resource allocation message, where receiving the configuration information may be based on the monitoring. 
     Some aspects may further include transmitting, by the UE, a request message to use a second neural network block different than the first neural network block, and processing the one or more signals using the second neural network block based on transmitting the request message. 
     Some aspects may further include receiving, from the base station, an acknowledgment message based on the transmitting of the request message, where processing the one or more signals using the second neural network block may be based on receiving the acknowledgement message. 
     In some aspects, receiving the one or more neural network block parameters further may include identifying a set of neural network blocks stored by the UE, where the one or more neural network blocks supported by the UE may include the set of neural network blocks, receiving an indication of the first neural network block of the set of neural network blocks, and identifying the first neural network block of the set of neural network blocks based on receiving the indication of the first neural network block, wherein processing the one or more signals using the first neural network block may be based on identifying the first neural network block. 
     Some aspects may further include receiving a downlink message that may include the one or more neural network block parameters, wherein receiving the one or more neural network block parameters may be based on receiving the downlink message. 
     In some aspects, the downlink message may include a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     Some aspects may further include initiating, upon receiving the one or more neural network block parameters, a timer, wherein processing the one or more signals using the first neural network block may be based on an expiration of the timer. 
     Some aspects may further include initiating, upon receiving the one or more neural network block parameters, a counter of symbols, a counter of slots, or a combination thereof, wherein processing the one or more signals using the first neural network block may be based on the counter of symbols, the counter of slots, or the combination thereof satisfying a threshold. 
     Some aspects may further include initiating, upon processing the one or more signals using the first neural network block, a timer, determining that the timer may have expired, and processing, based on determining that the timer may have expired, the one or more signals using a default neural network block different than the first neural network block. 
     Some aspects may further include receiving, from the base station, one or more additional neural network block parameters, and transmitting, to the base station, a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. 
     Some aspects may further include determining a priority status of the one or more additional neural network block parameters, wherein transmitting the negative acknowledgement message may be based on the priority status. 
     Some aspects may further include receiving, from the base station, a request for feedback information about a performance of the first neural network block, and transmitting, based on processing the one or more signals using the first neural network block and the request, a report including the feedback information about the performance of the first neural network block to the base station. In some aspects, the request may be communicated using a downlink control information (DCI) message, a media access control (MAC) control element (MAC-CE) message, or a radio resource control message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel. 
     In some aspects, the feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In some aspects, the first neural network block may be configured to perform channel estimation for the one or more signals, channel state information compression for the one or more signals, or a combination thereof. 
     Some aspects may further include receiving, from the base station, second capability information indicating that the base station supports at least one neural network block, where transmitting the capability information to the base station may be based on receiving the second capability information. In some aspects, the second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     In some aspects, the one or more neural network block parameters may include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     In some aspects, the one or more neural network block parameters further may include one or more adjustment parameters to the first neural network block used to process one or more signals by the UE. 
     In some aspects, the resource allocation message may include a downlink control information message or radio resource control message, and the one or more neural network block parameters may be transmitted as part of a downlink data message. 
     In some aspects, the one or more adjustment parameters include an activation indication for one or more nodes of the first neural network block, a deactivation indication for the one or more nodes of the first neural network block, a weight value for the one or more nodes of the first neural network block, or a bias value for the one or more nodes of the first neural network block, or a combination thereof. 
     In some aspects, the one or more adjustment parameters to the first neural network block include a first adjustment to a first weight value for a first node of a submodule of the first neural network block or a second adjustment to a second weight value for a second node of a submodule of the first neural network block, or both. 
     Some aspects may further include transmitting, to the UE, a resource allocation message for a physical downlink shared channel, and transmitting configuration information for the first neural network block and the one or more neural network block parameters over the physical downlink shared channel based on transmitting the resource allocation message, wherein the configuration information includes instructions for the UE to configure the first neural network block in place of a default network function block of the UE. 
     In some aspects, the resource allocation message may include a radio resource control message, or a downlink control information message. 
     In some aspects, the configuration information may include an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. 
     In some aspects, one or more neural network block types supported by the UE may include the default network function block. 
     Some aspects may further include transmitting control information to the UE that indicates a new network function block to be the default network function block. 
     Some aspects may further include receiving, from the UE, a request message to use a second neural network block different than the first neural network block, and transmitting, to the UE, an acknowledgement message based on the receiving of the request message. 
     In some aspects, transmitting the one or more neural network block parameters further may include transmitting an indication of the first neural network block of a set of neural network blocks stored by the UE, where the one or more neural network blocks supported by the UE may include the set of neural network blocks. 
     Some aspects may further include operations, features, means, or instructions for transmitting a downlink message that may include the one or more neural network block parameters, where the one or more neural network block parameters transmitted as part of the downlink message. 
     In some aspects, the downlink message may include a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     Some aspects may further include transmitting an indication of a timer and an instruction for the UE to initiate the timer upon receiving the one or more neural network block parameters. 
     Some aspects may further include transmitting an indication of a counter of symbols, a counter of slots, or a combination thereof, and an instruction for the UE to initiate the counter of symbols, the counter of slots, of the combination thereof upon receiving the one or more neural network block parameters. 
     Some aspects may further include transmitting, to the UE, one or more additional neural network block parameters, and receiving, from the UE, a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. 
     Some aspects may further include receiving the negative acknowledgement message may be based on a priority status of the one or more additional neural network block parameters. 
     Some aspects may further include transmitting, to the UE, a request for feedback information about a performance of the first neural network block, and receiving, based on the request for the feedback information about the performance of the first neural network block, a report including the feedback information about the performance of the first neural network block. In some aspects, the request may be communicated using a DCI message, MAC-CE message, or an RRC message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel In some aspects, the feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In some aspects, the first neural network block may be configured to perform channel estimation for one or more signals, channel state information compression for the one or more signals, or a combination thereof. 
     Some aspects may further include transmitting, to the UE, second capability information indicating that the base station supports at least one neural network block, wherein receiving the capability information from the UE may be based on transmitting the second capability information. In some aspects, the second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     In some aspects, the one or more neural network block parameters include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     Further aspects include a UE and a base station including a processor configured to perform operations of any of the aspect methods summarized above. Further aspects include a non-transitory computer-readable medium having stored thereon processor-executable instructions configured to cause a processor perform operations of any of the aspect methods summarized above. Further aspects include a computer program product comprising processor-executable instructions configured to cause a processor perform operations of any of the aspects of methods summarized above. Further aspects include an apparatus including means for performing functions of any of the aspect methods summarized above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated herein and constitute part of this specification, illustrate exemplary embodiments of the claims, and together with the general description given above and the detailed description given below, serve to explain the features of the claims. 
         FIG. 1  illustrates an example of a wireless communications system that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 2  illustrates an example of a wireless communications system that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 3  illustrates an example of a neural network block that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 4  illustrates an example of an architecture that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 5  illustrates an example of a process flow that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIGS. 6 and 7  show block diagrams of devices that support neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 8  shows a block diagram of a communications manager that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 9  shows a diagram of a system including a device that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIGS. 10 and 11  show block diagrams of devices that support neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 12  shows a block diagram of a communications manager that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIG. 13  shows a diagram of a system including a device that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIGS. 14 through 19  show process flow diagrams illustrating methods that support neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. 
         FIGS. 20 through 24  show process flow diagrams illustrating methods for communicating neural network configuration information using a control channel for wireless communication system assistance in accordance with aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. References made to particular examples and embodiments are for illustrative purposes, and are not intended to limit the scope of the claims. 
     A wireless communications system may support the use of neural networks. For example, a user equipment (UE) may perform one or more functions by applying one or more neural network blocks to a signal, such as a baseband signal. A neural network may be made up of a plurality of neural network blocks, each of which may include any number and combination of neural networks or layers of a neural network, in which the output of one neural network block may provide the input to a next neural network block and/or an output of the neural network. Using neural network blocks may increase reliability of some communications and improve signal throughput. For example, a UE may determine or estimate channel conditions without an accurate or real-time knowledge of channel conditions by using a neural network block to perform channel estimation. Training or developing a neural network block may be time-intensive, may result in a high overhead signaling cost, and may use a large amount of computational resources. 
     A base station may assist one or more UEs to configure, train, and utilize one or more neural network blocks to fit the local environment of the UEs. Each neural network block may be configured and trained to perform one or more functions. For example, a UE may perform channel estimation on a signal (e.g., processing one or more baseband signals) using a channel estimation neural network block. Each neural network block may be characterized by a number of neural network block parameters. Neural network block parameters may include a number of layers, a number of nodes in each layer, a mapping between the respective nodes of each layer, an activation function for one or more of the nodes or submodules of the neural network block, a deactivation function for one or more of the nodes or submodules of the neural network block, one or more weight values, bias values, or the like. Base stations and UEs may communicate capability information, feedback information, neural network block parameters for a neural network block, or other signals, or combinations thereof to support neural network blocks used by the UE to perform various functions. 
     In some examples, a UE and a base station may communicate capability information. For example, the UE may transmit capability information indicating neural network blocks supported by the UE. The base station may configure, based on the UE capability information, the base station capability information, or both, one or more neural network block parameters for the UE. The base station may transmit the one or more neural network block parameters to the UE. 
     The base station may transmit one or more neural network block parameters to the UE to configure neural network blocks used by the UE. The neural network block parameters may include an addition or a release of one or more nodes of the neural network block, one or more additional input values for the neural network block or one or more additional output values for the neural network block, an activation or deactivation of one or more supplementary submodules of the neural network block, one or more weight values for nodes or primary submodules and supplementary submodules of the neural network block, or the like. The base station may transmit such neural network block parameters for reconfiguration via a downlink control information (DCI) message, or a media access control (MAC) control element (CE), or the like. In some examples, (e.g., the neural network block parameters include weight values and bias values for one or more supplementary submodules of the neural network block), the base station may schedule (e.g., via radio resource control (RRC) signaling, a DCI message, or the like) a downlink transmission over a physical downlink shared channel (PDSCH), and may transmit the neural network block parameters to the UE over the PDSCH as scheduled. 
     The base station may transmit one or more neural network block parameters to the UE to configure a new neural network block at the UE. Such configuration information could include each of the neural network block parameters described above. In some examples, the configuration information may include a complete model or map of each input value, node, and output value, including weight values, bias values, and mapping between each node, for the new neural network block. In such examples, the base station may schedule (e.g., via RRC signaling, a DCI message, or the like) a PDSCH, and may transmit the neural network block parameters to the UE over the PDSCH as scheduled. 
     The UE may apply the neural network block parameters to a neural network block. That is, the UE may update the neural network block according to the received neural network block parameters. For instance, the UE may reconfigure the neural network block according to the neural network block parameters, configure a new neural network block according to the neural network block parameters, or select one of a set of pre-trained neural network blocks according to the neural network block parameters. 
     Having updated the neural network block, the UE may process a signal (e.g., a baseband signal) generated by the UE from one or more over-the-air (OTA) wireless signals using the neural network block. For example, if the neural network block is a channel estimation neural network block, the UE may receive one or more reference signals, and phase shift and modulate the reference signals to generate a digital domain baseband signal. In this example, the UE may extract relevant data from the digital domain baseband signal, and may process the digital domain baseband signal using the neural network block. In such examples, the UE may generate one or more outputs from the neural network block, such as unprocessed data or processed training data, which may represent channel estimation results for signal distortion correction, represent error corrections for detected distortions, or include a combination thereof. As used herein, processed data may be data output by the neural network block. As used herein, unprocessed data may be data that is not processed by the neural network block, and is consistent with data input to the neural network block (effectively bypassing the neural network block). Unprocessed data may be stored in memory or the UE for use in neural network training or refinement, and/or may be transmitted to the base station for use in updating neural networks and other purposes. 
     Particular aspects of the subject matter described herein may be implemented to realize one or more advantages. The described techniques may support the use of one or more neural network blocks which may result in increased throughput, increased reliability of signaling, increased system efficiency and improved user experience. As such, supported techniques may include improved network operations and, in some examples, may promote device and network efficiencies, among other benefits. 
     Aspects of the disclosure are initially described in the context of wireless communications systems. Aspects of the disclosure are further illustrated by and described with reference to neural network blocks, architectures, and process flows. Aspects of the disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and process flow diagrams that relate to neural network configuration for wireless communication system assistance. 
       FIG. 1  illustrates an example of a wireless communications system  100  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The wireless communications system  100  may include one or more base stations  105 , one or more UEs  115 , and a core network  130 . In some examples, the wireless communications system  100  may be a Long Term Evolution (LTE) network, an LTE-Advanced (LTE-A) network, an LTE-A Pro network, or a New Radio (NR) network. In some examples, the wireless communications system  100  may support enhanced broadband communications, ultra-reliable (e.g., mission critical) communications, low latency communications, communications with low-cost and low-complexity devices, or any combination thereof. 
     The base stations  105  may be dispersed throughout a geographic area to form the wireless communications system  100  and may be devices in different forms or having different capabilities. The base stations  105  and the UEs  115  may wirelessly communicate via one or more communication links  125 . Each base station  105  may provide a coverage area  110  over which the UEs  115  and the base station  105  may establish one or more communication links  125 . The coverage area  110  may be an example of a geographic area over which a base station  105  and a UE  115  may support the communication of signals according to one or more radio access technologies. 
     The UEs  115  may be dispersed throughout a coverage area  110  of the wireless communications system  100 , and each UE  115  may be stationary, or mobile, or both at different times. The UEs  115  may be devices in different forms or having different capabilities. Some example UEs  115  are illustrated in  FIG. 1 . The UEs  115  described herein may be able to communicate with various types of devices, such as other UEs  115 , the base stations  105 , or network equipment (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network equipment), as shown in  FIG. 1 . 
     The base stations  105  may communicate with the core network  130 , or with one another, or both. For example, the base stations  105  may interface with the core network  130  through one or more backhaul links  120  (e.g., via an S1, N2, N3, or another interface). The base stations  105  may communicate with one another over the backhaul links  120  (e.g., via an X2, Xn, or another interface) either directly (e.g., directly between base stations  105 ), or indirectly (e.g., via core network  130 ), or both. In some examples, the backhaul links  120  may be or include one or more wireless links. 
     One or more of the base stations  105  described herein may include or may be referred to by a person having ordinary skill in the art as a base transceiver station, a radio base station, an access point, a radio transceiver, a NodeB, an eNodeB (eNB), a next-generation NodeB or a giga-NodeB (either of which may be referred to as a gNB), a Home NodeB, a Home eNodeB, or other suitable terminology. 
     A UE  115  may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology. A “device” may also be referred to as a unit, a station, a terminal, or a client, among other examples. A UE  115  may also include or may be referred to as a personal electronic device, such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, a UE  115  may include or be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communications (MTC) device, among other examples, which may be implemented in various objects such as appliances, or vehicles, meters, among other examples. 
     The UEs  115  described herein may be able to communicate with various types of devices, such as other UEs  115  that may sometimes act as relays as well as the base stations  105  and the network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, among other examples, as shown in  FIG. 1 . 
     The UEs  115  and the base stations  105  may wirelessly communicate with one another via one or more communication links  125  over one or more carriers. The term “carrier,” or “component carrier,” may refer to a set of radio frequency spectrum resources having a defined physical layer structure for supporting the communication links  125 . For example, a carrier used for a communication link  125  may include a portion of a radio frequency spectrum band (e.g., a bandwidth part (BWP)) that is operated according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each carrier may carry acquisition signaling (e.g., synchronization signals, system information), control signaling that coordinates operation for the carrier, user data, or other signaling. The wireless communications system  100  may support communication with a UE  115  using carrier aggregation or multi-carrier operation. A UE  115  may be configured with multiple downlink component carriers and one or more uplink component carriers according to a carrier aggregation configuration. Carrier aggregation may be used with both frequency division duplexing (FDD) and time division duplexing (TDD) component carriers. 
     In some examples (e.g., in a carrier aggregation configuration), a carrier may also have acquisition signaling or control signaling that coordinates operations for other carriers. A carrier may be associated with a frequency channel (e.g., an evolved universal mobile telecommunication system terrestrial radio access (E-UTRA) absolute radio frequency channel number (EARFCN)) and may be positioned according to a channel raster for discovery by the UEs  115 . A carrier may be operated in a standalone mode where initial acquisition and connection may be conducted by the UEs  115  via the carrier, or the carrier may be operated in a non-standalone mode where a connection is anchored using a different carrier (e.g., of the same or a different radio access technology). 
     In some examples, dual connectivity can be implemented among UEs  115 . Similar to carrier aggregation, dual connectivity can utilize radio resources within multiple carriers to improve UE throughput. A carrier may be a component carrier associated with one or more cell groups (e.g., master cell group, secondary cell group). Dual Connectivity may enable UEs  115  to simultaneously transmit and receive data on multiple component carriers from two cell groups via master eNodeB and secondary eNodeB. In some examples, dual connectivity and carrier aggregation may be implemented simultaneously. The configuration between master cell group and secondary cell group is independent, such that each cell group can be configured with a number of unique component carriers each having a number of uniquely defined BWPs. 
     The communication links  125  shown in the wireless communications system  100  may include uplink transmissions from a UE  115  to a base station  105 , or downlink transmissions from a base station  105  to a UE  115 . Carriers may carry downlink or uplink communications (e.g., in an FDD mode) or may be configured to carry downlink and uplink communications (e.g., in a TDD mode). 
     A carrier may be associated with a particular bandwidth of the radio frequency spectrum, and in some examples the particular bandwidth may be referred to as a “system bandwidth” of the carrier or the wireless communications system  100 . For example, the particular bandwidth may be one of a number of determined bandwidths for carriers of a particular radio access technology (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 megahertz (MHz)). Devices of the wireless communications system  100  (e.g., the base stations  105 , the UEs  115 , or both) may have hardware configurations that support communications over a particular carrier bandwidth or may be configurable to support communications over one of a set of carrier bandwidths. In some examples, the wireless communications system  100  may include base stations  105  or UEs  115  that support simultaneous communications via carriers associated with multiple carrier bandwidths. In some examples, each served UE  115  may be configured for operating over portions (e.g., a sub-band, a BWP) or all of a carrier bandwidth. 
     Signal waveforms transmitted over a carrier may be made up of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system employing MCM techniques, a resource element may consist of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier, where the symbol period and subcarrier spacing are inversely related. The number of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both). Thus, the more resource elements that a UE  115  receives and the higher the order of the modulation scheme, the higher the data rate may be for the UE  115 . A wireless communications resource may refer to a combination of a radio frequency spectrum resource, a time resource, and a spatial resource (e.g., spatial layers or beams), and the use of multiple spatial layers may further increase the data rate or data integrity for communications with a UE  115 . 
     One or more numerologies for a carrier may be supported, where a numerology may include a subcarrier spacing ( 4 ) and a cyclic prefix. A carrier may be divided into one or more BWPs having the same or different numerologies. In some examples, a UE  115  may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time and communications for the UE  115  may be restricted to one or more active BWPs. 
     The time intervals for the base stations  105  or the UEs  115  may be expressed in multiples of a basic time unit which may, for example, refer to a sampling period of T s =1/(Δf max ·N f ) seconds, where Δf max  may represent the maximum supported subcarrier spacing, and N f  may represent the maximum supported discrete Fourier transform (DFT) size. Time intervals of a communications resource may be organized according to radio frames each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023). 
     Each frame may include multiple consecutively numbered subframes or slots, and each subframe or slot may have the same duration. In some examples, a frame may be divided (e.g., in the time domain) into subframes, and each subframe may be further divided into a number of slots. Alternatively, each frame may include a variable number of slots, and the number of slots may depend on subcarrier spacing. Each slot may include a number of symbol periods (e.g., depending on the length of the cyclic prefix prepended to each symbol period). In some wireless communications systems  100 , a slot may further be divided into multiple mini-slots containing one or more symbols. Excluding the cyclic prefix, each symbol period may contain one or more (e.g., N f ) sampling periods. The duration of a symbol period may depend on the subcarrier spacing or frequency band of operation. 
     A subframe, a slot, a mini-slot, or a symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communications system  100  and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communications system  100  may be dynamically selected (e.g., in bursts of shortened TTIs (sTTIs)). 
     Physical channels may be multiplexed on a carrier according to various techniques. A physical control channel and a physical data channel may be multiplexed on a downlink carrier, for example, using one or more of time division multiplexing (TDM) techniques, frequency division multiplexing (FDM) techniques, or hybrid TDM-FDM techniques. A control region (e.g., a control resource set (CORESET)) for a physical control channel may be defined by a number of symbol periods and may extend across the system bandwidth or a subset of the system bandwidth of the carrier. One or more control regions (e.g., CORESETs) may be configured for a set of the UEs  115 . For example, one or more of the UEs  115  may monitor or search control regions for control information according to one or more search space sets, and each search space set may include one or multiple control channel candidates in one or more aggregation levels arranged in a cascaded manner. An aggregation level for a control channel candidate may refer to a number of control channel resources (e.g., control channel elements (CCEs)) associated with encoded information for a control information format having a given payload size. Search space sets may include common search space sets configured for sending control information to multiple UEs  115  and UE-specific search space sets for sending control information to a specific UE  115 . 
     Each base station  105  may provide communication coverage via one or more cells, for example a macro cell, a small cell, a hot spot, or other types of cells, or any combination thereof. The term “cell” may refer to a logical communication entity used for communication with a base station  105  (e.g., over a carrier) and may be associated with an identifier for distinguishing neighboring cells (e.g., a physical cell identifier (PCID), a virtual cell identifier (VCID), or others). In some examples, a cell may also refer to a geographic coverage area  110  or a portion of a geographic coverage area  110  (e.g., a sector) over which the logical communication entity operates. Such cells may range from smaller areas (e.g., a structure, a subset of structure) to larger areas depending on various factors such as the capabilities of the base station  105 . For example, a cell may be or include a building, a subset of a building, or exterior spaces between or overlapping with geographic coverage areas  110 , among other examples. 
     A macro cell generally covers a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by the UEs  115  with service subscriptions with the network provider supporting the macro cell. A small cell may be associated with a lower-powered base station  105 , as compared with a macro cell, and a small cell may operate in the same or different (e.g., licensed, unlicensed) frequency bands as macro cells. Small cells may provide unrestricted access to the UEs  115  with service subscriptions with the network provider or may provide restricted access to the UEs  115  having an association with the small cell (e.g., the UEs  115  in a closed subscriber group (CSG), the UEs  115  associated with users in a home or office). A base station  105  may support one or multiple cells and may also support communications over the one or more cells using one or multiple component carriers. 
     In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)) that may provide access for different types of devices. 
     In some examples, a base station  105  may be movable and therefore provide communication coverage for a moving geographic coverage area  110 . In some examples, different geographic coverage areas  110  associated with different technologies may overlap, but the different geographic coverage areas  110  may be supported by the same base station  105 . In other examples, the overlapping geographic coverage areas  110  associated with different technologies may be supported by different base stations  105 . The wireless communications system  100  may include, for example, a heterogeneous network in which different types of the base stations  105  provide coverage for various geographic coverage areas  110  using the same or different radio access technologies. 
     The wireless communications system  100  may support synchronous or asynchronous operation. For synchronous operation, the base stations  105  may have similar frame timings, and transmissions from different base stations  105  may be approximately aligned in time. For asynchronous operation, the base stations  105  may have different frame timings, and transmissions from different base stations  105  may, in some examples, not be aligned in time. The techniques described herein may be used for either synchronous or asynchronous operations. 
     Some UEs  115 , such as MTC or IoT devices, may be low cost or low complexity devices and may provide for automated communication between machines (e.g., via Machine-to-Machine (M2M) communication). M2M communication or MTC may refer to data communication technologies that allow devices to communicate with one another or a base station  105  without human intervention. In some examples, M2M communication or MTC may include communications from devices that integrate sensors or meters to measure or capture information and relay such information to a central server or application program that makes use of the information or presents the information to humans interacting with the application program. Some UEs  115  may be designed to collect information or enable automated behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security sensing, physical access control, and transaction-based business charging. 
     Some UEs  115  may be configured to employ operating modes that reduce power consumption, such as half-duplex communications (e.g., a mode that supports one-way communication via transmission or reception, but not transmission and reception simultaneously). In some examples, half-duplex communications may be performed at a reduced peak rate. Other power conservation techniques for the UEs  115  include entering a power saving deep sleep mode when not engaging in active communications, operating over a limited bandwidth (e.g., according to narrowband communications), or a combination of these techniques. For example, some UEs  115  may be configured for operation using a narrowband protocol type that is associated with a defined portion or range (e.g., set of subcarriers or resource blocks (RBs)) within a carrier, within a guard-band of a carrier, or outside of a carrier. 
     The wireless communications system  100  may be configured to support ultra-reliable communications or low-latency communications, or various combinations thereof. For example, the wireless communications system  100  may be configured to support ultra-reliable low-latency communications (URLLC) or mission critical communications. The UEs  115  may be designed to support ultra-reliable, low-latency, or critical functions (e.g., mission critical functions). Ultra-reliable communications may include private communication or group communication and may be supported by one or more mission critical services such as mission critical push-to-talk (MCPTT), mission critical video (MCVideo), or mission critical data (MCData). Support for mission critical functions may include prioritization of services, and mission critical services may be used for public safety or general commercial applications. The terms ultra-reliable, low-latency, mission critical, and ultra-reliable low-latency may be used interchangeably herein. 
     In some examples, a UE  115  may also be able to communicate directly with other UEs  115  over a device-to-device (D2D) communication link  135  (e.g., using a peer-to-peer (P2P) or D2D protocol). One or more UEs  115  utilizing D2D communications may be within the geographic coverage area  110  of a base station  105 . Other UEs  115  in such a group may be outside the geographic coverage area  110  of a base station  105  or be otherwise unable to receive transmissions from a base station  105 . In some examples, groups of the UEs  115  communicating via D2D communications may utilize a one-to-many (1:M) system in which each UE  115  transmits to every other UE  115  in the group. In some examples, a base station  105  facilitates the scheduling of resources for D2D communications. In other cases, D2D communications are carried out between the UEs  115  without the involvement of a base station  105 . 
     In some systems, the D2D communication link  135  may be an example of a communication channel, such as a sidelink communication channel, between vehicles (e.g., UEs  115 ). In some examples, vehicles may communicate using vehicle-to-everything (V2X) communications, vehicle-to-vehicle (V2V) communications, or some combination of these. A vehicle may signal information related to traffic conditions, signal scheduling, weather, safety, emergencies, or any other information relevant to a V2X system. In some examples, vehicles in a V2X system may communicate with roadside infrastructure, such as roadside units, or with the network via one or more network nodes (e.g., base stations  105 ) using vehicle-to-network (V2N) communications, or with both. 
     The core network  130  may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network  130  may be an evolved packet core (EPC) or 5G core (5GC), which may include at least one control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) and at least one user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a Packet Data Network (PDN) gateway (P-GW), or a user plane function (UPF)). The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management for the UEs  115  served by the base stations  105  associated with the core network  130 . User IP packets may be transferred through the user plane entity, which may provide IP address allocation as well as other functions. The user plane entity may be connected to the network operators IP services  150 . The operators IP services  150  may include access to the Internet, Intranet(s), an IP Multimedia Subsystem (IMS), or a Packet-Switched Streaming Service. 
     Some of the network devices, such as a base station  105 , may include subcomponents such as an access network entity  140 , which may be an example of an access node controller (ANC). Each access network entity  140  may communicate with the UEs  115  through one or more other access network transmission entities  145 , which may be referred to as radio heads, smart radio heads, or transmission/reception points (TRPs). Each access network transmission entity  145  may include one or more antenna panels. In some configurations, various functions of each access network entity  140  or base station  105  may be distributed across various network devices (e.g., radio heads and ANCs) or consolidated into a single network device (e.g., a base station  105 ). 
     The wireless communications system  100  may operate using one or more frequency bands, typically in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally, the region from 300 MHz to 3 GHz is known as the ultra-high frequency (UHF) region or decimeter band because the wavelengths range from approximately one decimeter to one meter in length. The UHF waves may be blocked or redirected by buildings and environmental features, but the waves may penetrate structures sufficiently for a macro cell to provide service to the UEs  115  located indoors. The transmission of UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers) compared to transmission using the smaller frequencies and longer waves of the high frequency (HF) or very high frequency (VHF) portion of the spectrum below 300 MHz. 
     The wireless communications system  100  may also operate in a super high frequency (SHF) region using frequency bands from 3 GHz to 30 GHz, also known as the centimeter band, or in an extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz), also known as the millimeter band. In some examples, the wireless communications system  100  may support millimeter wave (mmW) communications between the UEs  115  and the base stations  105 , and EHF antennas of the respective devices may be smaller and more closely spaced than UHF antennas. In some examples, this may facilitate use of antenna arrays within a device. The propagation of EHF transmissions, however, may be subject to even greater atmospheric attenuation and shorter range than SHF or UHF transmissions. The techniques disclosed herein may be employed across transmissions that use one or more different frequency regions, and designated use of bands across these frequency regions may differ by country or regulating body. 
     The wireless communications system  100  may utilize both licensed and unlicensed radio frequency spectrum bands. For example, the wireless communications system  100  may employ License Assisted Access (LAA), LTE-Unlicensed (LTE-U) radio access technology, or NR technology in an unlicensed band such as the 5 GHz industrial, scientific, and medical (ISM) band. When operating in unlicensed radio frequency spectrum bands, devices such as the base stations  105  and the UEs  115  may employ carrier sensing for collision detection and avoidance. In some examples, operations in unlicensed bands may be based on a carrier aggregation configuration in conjunction with component carriers operating in a licensed band (e.g., LAA). Operations in unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, among other examples. 
     A base station  105  or a UE  115  may be equipped with multiple antennas, which may be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. The antennas of a base station  105  or a UE  115  may be located within one or more antenna arrays or antenna panels, which may support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly, such as an antenna tower. In some examples, antennas or antenna arrays associated with a base station  105  may be located in diverse geographic locations. A base station  105  may have an antenna array with a number of rows and columns of antenna ports that the base station  105  may use to support beamforming of communications with a UE  115 . Likewise, a UE  115  may have one or more antenna arrays that may support various MIMO or beamforming operations. Additionally or alternatively, an antenna panel may support radio frequency beamforming for a signal transmitted via an antenna port. 
     The base stations  105  or the UEs  115  may use MIMO communications to exploit multipath signal propagation and increase the spectral efficiency by transmitting or receiving multiple signals via different spatial layers. Such techniques may be referred to as spatial multiplexing. The multiple signals may, for example, be transmitted by the transmitting device via different antennas or different combinations of antennas. Likewise, the multiple signals may be received by the receiving device via different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream and may carry bits associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers may be associated with different antenna ports used for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO), where multiple spatial layers are transmitted to the same receiving device, and multiple-user MIMO (MU-MIMO), where multiple spatial layers are transmitted to multiple devices. 
     Beamforming, which may also be referred to as spatial filtering, directional transmission, or directional reception, is a signal processing technique that may be used at a transmitting device or a receiving device (e.g., a base station  105 , a UE  115 ) to shape or steer an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining the signals communicated via antenna elements of an antenna array such that some signals propagating at particular orientations with respect to an antenna array experience constructive interference while others experience destructive interference. The adjustment of signals communicated via the antenna elements may include a transmitting device or a receiving device applying amplitude offsets, phase offsets, or both to signals carried via the antenna elements associated with the device. The adjustments associated with each of the antenna elements may be defined by a beamforming weight set associated with a particular orientation (e.g., with respect to the antenna array of the transmitting device or receiving device, or with respect to some other orientation). 
     A base station  105  or a UE  115  may use beam sweeping techniques as part of beam forming operations. For example, a base station  105  may use multiple antennas or antenna arrays (e.g., antenna panels) to conduct beamforming operations for directional communications with a UE  115 . Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be transmitted by a base station  105  multiple times in different directions. For example, the base station  105  may transmit a signal according to different beamforming weight sets associated with different directions of transmission. Transmissions in different beam directions may be used to identify (e.g., by a transmitting device, such as a base station  105 , or by a receiving device, such as a UE  115 ) a beam direction for later transmission or reception by the base station  105 . 
     Some signals, such as data signals associated with a particular receiving device, may be transmitted by a base station  105  in a single beam direction (e.g., a direction associated with the receiving device, such as a UE  115 ). In some examples, the beam direction associated with transmissions along a single beam direction may be determined based on a signal that was transmitted in one or more beam directions. For example, a UE  115  may receive one or more of the signals transmitted by the base station  105  in different directions and may report to the base station  105  an indication of the signal that the UE  115  received with a highest signal quality or an otherwise acceptable signal quality. 
     In some examples, transmissions by a device (e.g., by a base station  105  or a UE  115 ) may be performed using multiple beam directions, and the device may use a combination of digital precoding or radio frequency beamforming to generate a combined beam for transmission (e.g., from a base station  105  to a UE  115 ). The UE  115  may report feedback that indicates precoding weights for one or more beam directions, and the feedback may correspond to a configured number of beams across a system bandwidth or one or more sub-bands. The base station  105  may transmit a reference signal (e.g., a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS)), which may be precoded or unprecoded. The UE  115  may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., a multi-panel type codebook, a linear combination type codebook, a port selection type codebook). Although these techniques are described with reference to signals transmitted in one or more directions by a base station  105 , a UE  115  may employ similar techniques for transmitting signals multiple times in different directions (e.g., for identifying a beam direction for subsequent transmission or reception by the UE  115 ) or for transmitting a signal in a single direction (e.g., for transmitting data to a receiving device). 
     A receiving device (e.g., a UE  115 ) may try multiple receive configurations (e.g., directional listening) when receiving various signals from the base station  105 , such as synchronization signals, reference signals, beam selection signals, or other control signals. For example, a receiving device may try multiple receive directions by receiving via different antenna subarrays, by processing received signals according to different antenna subarrays, by receiving according to different receive beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of an antenna array, or by processing received signals according to different receive beamforming weight sets applied to signals received at multiple antenna elements of an antenna array, any of which may be referred to as “listening” according to different receive configurations or receive directions. In some examples, a receiving device may use a single receive configuration to receive along a single beam direction (e.g., when receiving a data signal). The single receive configuration may be aligned in a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have a highest signal strength, highest signal-to-noise ratio (SNR), or otherwise acceptable signal quality based on listening according to multiple beam directions). 
     The wireless communications system  100  may be a packet-based network that operates according to a layered protocol stack. In the user plane, communications at the bearer or Packet Data Convergence Protocol (PDCP) layer may be IP-based. A Radio Link Control (RLC) layer may perform packet segmentation and reassembly to communicate over logical channels. A Medium Access Control (MAC) layer may perform priority handling and multiplexing of logical channels into transport channels. The MAC layer may also use error detection techniques, error correction techniques, or both to support retransmissions at the MAC layer to improve link efficiency. In the control plane, the Radio Resource Control (RRC) protocol layer may provide establishment, configuration, and maintenance of an RRC connection between a UE  115  and a base station  105  or a core network  130  supporting radio bearers for user plane data. At the physical layer, transport channels may be mapped to physical channels. 
     The UEs  115  and the base stations  105  may support retransmissions of data to increase the likelihood that data is received successfully. Hybrid automatic repeat request (HARQ) feedback is one technique for increasing the likelihood that data is received correctly over a communication link  125 . HARQ may include a combination of error detection (e.g., using a cyclic redundancy check (CRC)), forward error correction (FEC), and retransmission (e.g., automatic repeat request (ARQ)). HARQ may improve throughput at the MAC layer in poor radio conditions (e.g., low signal-to-noise conditions). In some examples, a device may support same-slot HARQ feedback, where the device may provide HARQ feedback in a specific slot for data received in a previous symbol in the slot. In other cases, the device may provide HARQ feedback in a subsequent slot, or according to some other time interval. 
     A base station  105  may configure one or more neural network block parameters, and may transmit the neural network block parameters to a UE  115 . The UE  115  may configure or reconfigure a neural network block according to the neural network block parameters, and may process one or more signals (e.g., baseband signals) generated by the UE  115  using the neural network block and the neural network block parameters. The UE  115  may also provide feedback information (e.g., when requested by the base station) indicating how well the neural network block is performing with the configuration. For example, if a channel estimate neural network block fails to accurately or reliably determine channel estimates (e.g., exhibiting phase or center frequency offset errors), the decoding success rate of the phase-compensated and carrier frequency compensated channel output may be lower than a certain threshold and multiple retransmissions may be triggered that are abnormal given some signal-to-noise-ratio (SNIR) conditions, which the UE  115  may characterize in a performance metric that the UE  115  reports in feedback information to the base station  105 . Similarly, output errors of other types of neural network blocks may be recognized or determined by the UE  115  based on measurable device performance issues caused by or related to neural network block outputs, and characterized by the UE  115  in performance metrics associated with specific neural network blocks that may be included in feedback information reported to the base station  105 . Base station  105  may use the feedback information when providing neural network block parameters to the UE  115  and other served UEs  115 , to configure or reconfigure the neural network block for improved performance. Processing signals using a neural network block may result in improved throughput, increased system efficiency, and improved user experience. 
       FIG. 2  illustrates an example of a wireless communications system  200  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. In some examples, wireless communications system  200  may implement aspects of wireless communications system  100 . 
     Wireless communications system  200  may support the use of neural networks. For example, a UE  115  (e.g., UE  115 - a , UE  115 - b , etc.) may perform one or more functions (e.g., process one or more signals) using one or more neural network blocks to process a signal (e.g., a baseband signal). Using neural network blocks may increase reliability of some communications and improve signal throughput. For example, UE  115 - a  may determine or estimate channel conditions without an accurate or real-time knowledge of channel conditions by using a neural network block to perform channel estimation. Training or developing a neural network block may be time-intensive, may result in a high overhead signaling cost, and may use a large amount of computational resources. 
     Base station  105 - a  may assist one or more UEs  115  (e.g., UE  115 - a  and UE  115 - b ) to configure, train, and utilize one or more neural network blocks to fit the local environment of the UEs  115 . If base station  105 - a  assists the UEs  115  in training and applying one or more neural network blocks, then the UEs  115  may be able to utilize higher complexity algorithms or online neural network algorithms. The UEs  115  may be able to offload the computation to the base stations. UEs  115  may be able to provide channel estimation information in diverse and versatile environments. Base station assistance may also reduce the complexity of one or more models or neural network blocks at a UE  115 . Each neural network block may be configured and trained to perform one or more functions. For example, UE  115 - a  may perform channel estimation procedures using a channel estimation neural network block. Each neural network block may be characterized by a number of neural network block parameters. Neural network block parameters may include a number of layers, a number of nodes in each layer, a mapping between the respective nodes of each layer, an activation function for one or more of the nodes or submodules of the neural network block, a deactivation function for one or more of the nodes or submodules of the neural network block, one or more weight values, bias values, or the like. Base station  105 - a , UE  115 - a , and UE  115 - b  may communicate capability information, feedback information, neural network block parameters for a neural network block, or other signals, or combinations thereof to support neural network blocks used by each UE  115  to perform various functions. 
     In some examples, each UE  115  and base station  105 - a  may communicate capability information. For example, base station  105 - a  may transmit, to UE  115 - a  via downlink  215 - a , capability information regarding one or more neural network blocks capable of being implemented by base station  105 - a . UE  115 - a  may transmit capability information to base station  105 - a  via uplink  210 - a  regarding neural network blocks capable of being implemented by UE  115 - a . The one or more neural network blocks implemented by the UE may include a single neural network block that is configurable to perform one or more different functions, a default neural network block or traditional function block (e.g., a function block for performing one or more functions by the modem of the UE that is not a neural network block), a list of pre-trained neural network blocks from which base station  105 - a  can select a neural network block, or the like. Base station  105 - a  may configure, based on the UE capability information received from UE  115 - a , or base station capability information transmitted by base station  105 - a , or both, one or more neural network block parameters for UE  115 - a . Base station  105 - a  may transmit the one or more neural network block parameters to UE  115 - a  via downlink  215 - a.    
     A base station may receive capability information from a UE  115 - a , and may transmit, via downlink  215 - a , one or more neural network block parameters to be implemented by the UE  115 - a . In some examples, base station  105 - a  may transmit neural network block parameters to reconfigure a neural network block with dynamic configuration options that is capable of being implemented by the UE  115 - a . In some examples, base station  105 - a  may transmit neural network block parameters to configure a new neural network block in place of a default neural network block. In some examples, base station  105 - a  may transmit neural network block parameters selecting one of a set of neural network blocks capable of being implemented by UE  115 - a.    
     Base station  105 - a  may transmit neural network block parameters to UE  115 - a  to reconfigure a neural network block with dynamic configuration options that is capable of being implemented by UE  115 - a . For instance, base station  105 - a  may transmit one or more neural network block parameters to UE  115 - a  via downlink  215 - a . UE  115 - a  may configure the supported neural network block using the neural network block parameters. The neural network block parameters may describe adding or releasing one or more nodes of the configurable neural network block, adding one or more additional input values for the configurable neural network block or adding one or more additional output values for the configurable neural network block, activating or deactivating one or more supplementary submodule of the neural network block, adding, removing or updating one or more weight values for nodes or primary submodules and supplementary submodules of the configurable neural network block, or the like. Base station  105 - a  may transmit such neural network block parameters for reconfiguration of the configurable neural network block on downlink  215 - a  via a DCI message, or a MAC-CE, or the like. In some examples, (e.g., the neural network block parameters include weight values and bias values for one or more supplementary submodules of the neural network block), base station  105 - a  may schedule (e.g., via RRC signaling, a DCI message, or the like) a downlink transmission over a PDSCH, and may transmit the neural network block parameters to UE  115 - a  over the PDSCH on downlink  215 - a  as scheduled. 
     Base station  105 - a  may transmit neural network block parameters to configure a new neural network block in place of a default neural network block. For instance, base station  105 - a  may transmit one or more neural network block parameters to UE  115 - a  via downlink  215 - a  to configure a new neural network block at UE  115 - a . Such configuration information could include adding one or more nodes to the new neural network block, adding one or more input values for the new neural network block or adding one or more output values for the new neural network block, activating or deactivating a primary or supplementary submodule of the new neural network block, one or more weight values for nodes or primary submodules and supplementary submodules of the new neural network block, or the like. In some examples, the configuration information may include a complete model or map of each input value, node, and output value, including weight values, bias values, and mapping between each node, for the new neural network block. In such examples, base station  105 - a  may schedule (e.g., via RRC signaling, a DCI message, or the like) one or more resources of a PDSCH, and may transmit the neural network block parameters to UE  115 - a  over the PDSCH as scheduled. 
     Base station  105 - a  may transmit neural network block parameters selecting one of a set of neural network blocks capable of being implemented by UE  115 - a . Base station  105 - a  may transmit one or more neural network block parameters to UE  115 - a  that may include an indication of one of the pre-trained neural network blocks. Base station  105 - a  may indicate the one or more parameters (e.g., using a small number of bits) in a DCI message, a MAC-CE message, or via RRC signaling. 
     UE  115 - a  may update a neural network block operating using the received neural network block parameters. For instance, UE  115 - a  may reconfigure the neural network block according to the neural network block parameters, configure a new neural network block according to the neural network block parameters, or select one of a set of pre-trained neural network blocks according to the neural network block parameters. 
     Having updated the neural network block, UE  115 - a  may process a signal (e.g., a baseband signal or a digital domain baseband signal) generated by UE  115 - a  from one or more over-the-air (OTA) wireless signals using the updated neural network block. For example, if the neural network block is a channel estimation neural network block, UE  115 - a  may receive one or more reference signals, combine and down convert signals, and generate a digital domain baseband signal. In this example, the UE  115 - a  may extract relevant data from the digital domain baseband signal, and may process the digital domain baseband signal using the neural network block. In such examples, UE  115 - a  may generate one or more outputs from the neural network block (e.g., unprocessed data, or processed training data) which may represent channel estimation results for signal distortion correction, or may represent error corrections for detected distortions, or a combination thereof. 
     In some examples, the neural network block may be considered critical (e.g., may have a high priority). In such examples, UE  115 - a  might immediately (e.g., upon receiving and decoding the neural network block parameters) process the baseband signals using the neural network block operating using the received neural network block parameters. In such examples, UE  115 - a  may send an acknowledgement (ACK) message to the base station indicating successful reception of the neural network block parameters. If UE  115 - a  does not receive neural network block parameters for a critical neural network block, then UE  115 - a  may transmit a negative acknowledgement (NACK) to base station  105 - a . If a neural network block is not considered critical, then UE  115 - a  may use a timer or a counter to determine whether the neural network block parameters or default parameters should be used. The UE  115 - a  may activate or deactivate the neural network block according to the timer or the counter. For example, UE  115 - a  may activate the neural network block and initiate the timer, and may revert to a default or previously used neural network block upon expiration of the timer. In such examples, UE  115 - a  may receive the parameters and initiate the timer, and may activate (e.g., apply) the neural network block for the digital domain baseband signals upon expiration of the timer. In some examples, UE  115 - a  may activate a counter (e.g., a symbol counter, a slot counter, or the like) upon receiving the neural network block parameters. In such examples, when UE  115 - a  determines that the counter has expired (e.g., the counter has counted a number of symbols, slots, or the like), UE  115 - a  may activate (e.g., apply) the neural network block for the digital domain baseband signal. 
     UEs  115  in wireless communications system  200  may support base station assistance of neural network block training and application by providing feedback data reporting to base station  105 - a . For example, UE  115 - a  may report feedback data to base station  105 - a . UE  115 - a  may report the feedback information immediately, or base station  105 - a  may schedule UE  115 - a  to report the feedback data dynamically, such as using a dynamic resource allocation, or periodically, such as using a semi-persistent resource allocation. That is, base station  105 - a  may schedule an uplink transmission on uplink  210 - a , and UE  115 - a  may transmit feedback data on a physical uplink control channel (PUCCH) or physical uplink shared channel (PUSCH). Base station  105 - a  may schedule the feedback report by transmitting a DCI message, or via RRC signaling. The feedback data may include unprocessed data or processed data. For example, the feedback data may be unprocessed channel quality measurements made by UE  115 - a . The feedback data may be processed data, and may include channel information gathered by UE  115 - a  (e.g., by receiving one or more reference signals) prior to or after performing a channel estimation or prior to or after frequency-tracking corrections are made. The feedback information may include channel information gathered by UE  115 - a  including a pilot signal only, or including a complete reference signal. The feedback information may include time-domain signal reporting including a cyclic prefix or a time domain signal from which UE  115 - a  has stripped the cyclic prefix. UE  115 - b  may similarly communicate with base station  105 - a , and may train and use one or more neural network blocks. UE  115 - b  may also report feedback information. Base station  105 - a  may gather the feedback information from multiple UEs  115 . During subsequent iterations with each UE  115 , base station  105 - a  may adjust, update, or confirm one or more neural network block parameters based on the feedback information to improve the output values of the neural network blocks, resulting in increased throughput for the UE, improved channel measurements without real-time knowledge of channel conditions (e.g., for a rapidly changing environment), increased system efficiency, and improved user experience. 
     Neural network blocks may perform one or more functions. A neural network block may perform the functions of default wireless communications network hardware, firmware, or software to replace, or free up those resources for additional processing. For example, a neural network block may be a channel estimation block, a channel state information (CSI) compression block, a modulation block, a demodulation block, a coding block, a decoding block, a serial-to-parallel/parallel-to-serial block, an (inverse) Fast Fourier Transform block, digital-to-analog/analog-to-digital block, packet detection block, cyclic redundancy check packet block, carrier frequency offset block, or the like. These various hardware and software blocks may be referred to as default network function blocks. In some examples, a neural network block can perform functions of multiple blocks simultaneously. For example, a neural network block can provide packet detection functionality for multiple UEs, effectively replacing default packet detection hardware and software. As another example, a neural network block can perform multiple functions, such as operating as a channel estimation block and demodulation block in a single instance, effectively replacing the associated hardware and software for channel estimation and demodulation. A neural network block, as described herein, may refer to illustrative examples where the neural network block is, for instance, a channel estimation block. However, a neural network block as described herein may refer to any neural network block and may be similarly used (e.g., for CSI compression, modulation, demodulation, coding, decoding, etc.). A neural network block may include one or more input nodes, one or more hidden nodes included in one or more layers, and one or more output nodes, as described in greater detail with reference to  FIG. 3 . 
       FIG. 3  illustrates an example of a neural network block  300  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. In some examples, neural network block  300  may implement aspects of wireless communications system  100  and wireless communications system  200 . 
     A UE  115  may support one or more neural network blocks  300 . Neural network block  300  may be based on or inspired by biological neural networks. Neural network block  300  may be a compilation of one or more algorithms that perform operations on one or more input values to produce one or more output values. 
     Neural network block  300  may include a number of layers. Each layer may include a number of nodes (which may be referred to as neurons). For example, an input layer may include one or more input nodes  305  (e.g., input node  305 - a , input node  305 - b , and input node  305 - c ). Input nodes  305  may receive one or more input values. The input values may be current or previous measurements, historical data, fixed or configured parameter values (e.g., as indicated by a base station  105 ), or the like. For example, neural network block  300  may be a channel estimation neural network block. In such examples, input values received by input nodes  305  may include historical channel estimation values, an average of previously measured channel estimation values, current measurements of channel estimation values, signals (e.g., baseband signals), or the like. Neural network block  300  may also include one or more layers of hidden nodes  310 . Neural network block  300  illustrates a single layer of hidden nodes  310  (e.g., including hidden node  310 - a , hidden node  310 - b , hidden node  310 - c , and hidden node  310 - d ). However, neural network block  300  may include multiple hidden node layers, each hidden node layer including one or more hidden nodes  310 . Hidden nodes  310  may perform one or more operations on input values received from input nodes  305  (e.g., multiplication, weighted multiplication, activation or deactivation operations, summations, weighted summations, etc.). Neural network block  300  may also include one or more output nodes  315  (e.g., output node  315 - a  and output node  315 - b ). Each output node  315  may receive data that has been manipulated or processed by the hidden nodes  310 , and may generate an output value. 
     A node of neural network block  300  may perform one or more mathematical operations. Operations may rely on weight values, bias values, or the like. For example, an activation function may include multiplying a set of one or more input values with a set of one or more weight values, summing the result, and adding a bias value to the sum. Weight values may be used to affect output values (e.g., positive weights may increase the value of an output and negative weights may decrease the value of an output). Weights may be utilized to increase or decrease the effect of a particular input value, set of data, source of data, or the like. A bias may be a constant value (e.g., a constant vector) that is added to the product of inputs multiplied by weights. The bias value may be used to offset a particular result or output, and may be used to reduce variance. That is, a bias may shift the output of an activation function towards negative or positive value. 
     Each node of neural network block  300  may perform an activation function. For input nodes  305  of the input layer, the input values may be raw data or processed data (e.g., unprocessed numerical values) provided by a base station or configured at the UE  115 . Each input node  305  may multiply one or more input values with one or more weights, and add a bias value. The bias value for each input node  305  may be the same or different. Similarly, the weight values for each input node  305  may be the same, or may be different. Each input node  305  may generate an output based on its respective activation function, and may provide that output to each hidden node  310 . For instance, input node  305 - a  may provide its output to hidden node  310 - a , hidden node  310 - b , hidden node  310 - c , and hidden node  310 - d . Input node  305 - b  and input node  305 - c  may similarly provide their respective outputs to each hidden node  310 . Each hidden node may also perform an activation function on its received inputs (e.g., the outputs provided by input nodes  305 ). For example, hidden node  310 - a  may be configured with three weight values and a bias value (e.g., via one or more neural network block parameters as described in greater detail with reference to  FIG. 5 ). Hidden node  310 - a  may multiple the first of the three weight values by the input received from input node  305 - a , the second of the three weight values by the input received from input node  305 - b , and the third of the three weight values by the input received from input node  305 - c . Hidden node  310 - a  may sum the results of the multiplication, and may then add the bias value to the result, thereby generating an output. Hidden node  310 - a  may send the generated output to each output node  315 . Each hidden node  310  may similarly perform an activation function on each input received from input nodes  305 , and may send the resulting output to each output node  315 . Each output node  315  may perform a respective activation function on the received inputs (e.g., the output values from hidden nodes  310 ). The resulting outputs may be generated as output data. 
     Neural network block  300  may be characterized by one or more neural network block parameters. Neural network block parameters may include one or more of a number of layers, a number of nodes in each layer, a connection mapping between the layers, activation functions for each node, and one or more hyperparameters (e.g., weight values and bias values for each node in neural network block  300 ). Neural network block parameters may further include an indication of a previously trained neural network block  300 , one or more submodules within neural network block  300 , or the like. 
     In some examples, the output data may represent channel estimation information. In some examples, the output data may represent distortion corrections for OTA signaling (e.g., reference signals), as described in greater detail with respect to  FIG. 4 . 
       FIG. 4  illustrates an example of an architecture  400  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. In some examples, architecture  400  may implement aspects of wireless communications system  100 , wireless communications system  200 , and neural network block  300 . In some aspects, architecture  400  may be an example of the transmitting device (e.g., a first wireless device, UE  115 , or base station  105 ) and/or a receiving device (e.g., a second wireless device, UE  115 , or base station  105 ) as described herein. 
     Broadly,  FIG. 4  is a diagram illustrating example hardware components of a wireless device in accordance with certain aspects of the disclosure. The illustrated components may include those that may be used for antenna element selection and/or for beamforming for transmission of wireless signals. The illustrated components may be used to receive millimeter waves, as described with reference to  FIG. 4 . In other examples (e.g., an LTE system) the hardware components may be streamlined to receive radio frequency waves (e.g., may include a single antenna element and may not include phase shifters, intermediate frequencies, etc.) There are numerous architectures for antenna element selection and implementing phase shifting, only one example of which is illustrated here. The architecture  400  includes a modem (modulator/demodulator)  402 , a digital to analog converter (DAC)  404 , a first mixer  406 , a second mixer  408 , and a splitter  410 . The architecture  400  also includes a plurality of first amplifiers  412 , a plurality of Tx phase shifters  414 , a plurality of second amplifiers  416 , and an antenna array  418  that includes a plurality of antenna elements  420 . Transmission lines or other waveguides, wires, traces, or the like are shown connecting the various components to illustrate how signals to be transmitted may travel between components. Boxes  422 ,  424 ,  426 , and  428  indicate regions in the architecture  400  in which different types of signals travel or are processed. Specifically, box  422  indicates a region in which digital domain baseband signals travel or are processed, box  424  indicates a region in which analog domain baseband signals travel or are processed, box  426  indicates a region in which analog domain intermediate frequency (IF) signals travel or are processed, and box  428  indicates a region in which analog domain radio frequency (RF) signals travel or are processed. The architecture also includes a local oscillator A  430 , a local oscillator B  432 , and a neural network block  434 . In some examples, neural network block  434  may be part of (e.g., may be an aspect of) modem  402 . 
     Each of the antenna elements  420  may include one or more sub-elements (not shown) for radiating or receiving RF signals. For example, a single antenna element  420  may include a first sub-element cross-polarized with a second sub-element that can be used to independently transmit cross-polarized signals. The antenna elements  420  may include patch antennas or other types of antennas arranged in a linear, two dimensional, or other pattern. A spacing between antenna elements  420  may be such that signals with a desired wavelength transmitted separately by the antenna elements  420  may interact or interfere (e.g., to form a desired beam). For example, given an expected range of wavelengths or frequencies, the spacing may provide a quarter wavelength, half wavelength, or other fraction of a wavelength of spacing between neighboring antenna elements  420  to allow for interaction or interference of signals transmitted by the separate antenna elements  420  within that expected range. 
     The modem  402  processes and generates digital domain baseband signals and may also control operation of the DAC  404 , first and second mixers  406 ,  408 , splitter  410 , first amplifiers  412 , Tx phase shifters  414 , and/or the second amplifiers  416  to transmit signals via one or more or all of the antenna elements  420 . The modem  402  may process signals and control operation in accordance with a communication standard such as a wireless standard discussed herein. The DAC  404  may convert digital domain baseband signals received from the modem  402  (and that are to be transmitted) into analog baseband signals. The first mixer  406  upconverts analog domain baseband signals to analog domain IF signals within an IF using a local oscillator A  430 . For example, the first mixer  406  may mix the signals with an oscillating signal generated by the local oscillator A  430  to “move” the analog domain baseband signals to the IF. In some cases, some processing or filtering (not shown) may take place at the IF. The second mixer  408  upconverts the analog domain IF signals to analog domain RF signals using the local oscillator B  432 . Similarly to the first mixer, the second mixer  408  may mix the signals with an oscillating signal generated by the local oscillator B  432  to “move” the analog domain IF signals to the RF, or the frequency at which signals will be transmitted. The modem  402  may adjust the frequency of local oscillator A  430  and/or the local oscillator B  432  so that a desired IF and/or RF frequency is produced and used to facilitate processing and transmission of a signal within a desired bandwidth. 
     In the illustrated architecture  400 , signals upconverted by the second mixer  408  are split or duplicated into multiple signals by the splitter  410 . The splitter  410  in architecture  400  splits the RF signal into a plurality of identical or nearly identical RF signals, as denoted by its presence in box  428 . In other examples, the split may take place with any type of signal including, for example, with digital domain baseband signals, analog domain baseband signals, and/or analog domain IF signals. Each of these signals may correspond to an antenna element  420  and the signal travels through and is processed by amplifiers  412 ,  416 , Tx phase shifters  414 , and/or other elements corresponding to the respective antenna element  420  to be provided to and transmitted by the corresponding antenna element  420  of the antenna array  418 . In one example, the splitter  410  may be an active splitter that is connected to a power supply and provides some gain so that RF signals exiting the splitter  410  are at a power level equal to or greater than the signal entering the splitter  410 . In another example, the splitter  410  is a passive splitter that is not connected to power supply and the RF signals exiting the splitter  410  may be at a power level lower than the RF signal entering the splitter  410 . 
     After being split by the splitter  410 , the resulting RF signals may enter an amplifier, such as a first amplifier  412 , or a phase shifter  414  corresponding to an antenna element  420 . The first and second amplifiers  412 ,  416  are illustrated with dashed lines because one or both of them might not be necessary in some implementations. In one implementation, both the first amplifier  412  and second amplifier  416  are present. In another implementation, neither the first amplifier  412  nor the second amplifier  416  is present. In other implementations, one of the two amplifiers  412 ,  416  is present but not the other. By way of example, if the splitter  410  is an active splitter, the first amplifier  412  may not be used. By way of further example, if the Tx phase shifter  414  is an active phase shifter that can provide a gain, the second amplifier  416  might not be used. The amplifiers  412 ,  416  may provide a desired level of positive or negative gain. A positive gain (positive dB) may be used to increase an amplitude of a signal for radiation by a specific antenna element  420 . A negative gain (negative dB) may be used to decrease an amplitude and/or suppress radiation of the signal by a specific antenna element  420 . Each of the amplifiers  412 ,  416  may be controlled independently (e.g., by the modem  402  and/or neural network block  434 ) to provide independent control of the gain for each antenna element  420 . For example, the modem  402  and/or the neural network block  434  may have at least one control line connected to each of the splitter  410 , first amplifiers  412 , Tx phase shifters  414 , and/or second amplifiers  416  which may be used to configure a gain to provide a desired amount of gain for each component and thus each antenna element  420 . 
     The Tx phase shifter  414  may provide a configurable phase shift or phase offset to a corresponding RF signal to be transmitted. The Tx phase shifter  414  could be a passive phase shifter not directly connected to a power supply. Passive phase shifters might introduce some insertion loss. The second amplifier  416  could boost the signal to compensate for the insertion loss. The Tx phase shifter  414  could be an active phase shifter connected to a power supply such that the active phase shifter provides some amount of gain or prevents insertion loss. The settings of each of the Tx phase shifters  414  are independent meaning that each can be set to provide a desired amount of phase shift or the same amount of phase shift or some other configuration. The modem  402  and/or the neural network block  434  may have at least one control line connected to each of the Tx phase shifters  414  and which may be used to configure the Tx phase shifters  414  to provide a desired amounts of phase shift or phase offset between antenna elements  420 . 
     In the illustrated architecture  400 , RF signals received by the antenna elements  420  are provided to one or more of third amplifier  456  to boost the signal strength. The third amplifier  456  may be connected to the same antenna arrays  418 , e.g., for TDD operations. The third amplifier  456  may be connected to different antenna arrays  418 . The boosted RF signal is input into one or more of phase shifter  454  to provide a configurable phase shift or phase offset for the corresponding received RF signal. The Rx phase shifter  454  may be an active phase shifter or a passive phase shifter. The settings of each of the Rx phase shifters  454  are independent, meaning that each can be set to provide a desired amount of phase shift or the same amount of phase shift or some other configuration. The modem  402  and/or the neural network block  434  may have at least one control line connected to each of the Rx phase shifters  454  and which may be used to configure the Rx phase shifters  454  to provide a desired amount of phase shift or phase offset between antenna elements  420 . 
     The outputs of the Rx phase shifters  454  may be input to one or more second amplifiers  452  for signal amplification of the Rx phase shifted received RF signals. The fourth amplifiers  452  may be individually configured to provide a configured amount of gain. The fourth amplifiers  452  may be individually configured to provide an amount of gain to ensure that the signal input to combiner  450  have the same magnitude. The amplifiers  452  and/or  456  are illustrated in dashed lines because they might not be necessary in some implementations. In one implementation, both the fourth amplifier  452  and the third amplifier  456  are present. In another implementation, neither the fourth amplifier  452  nor the third amplifier  456  are present. In other implementations, one of the amplifiers  452 ,  456  is present but not the other. 
     In the illustrated architecture  400 , signals output by the phase shifters  454  (via the fourth amplifiers  452  when present) are combined in combiner  450 . The combiner  450  in architecture combines the RF signal into a signal, as denoted by its presence in box  428 . The combiner  450  may be a passive combiner, e.g., not connected to a power source, which may result in some insertion loss. The combiner  450  may be an active combiner, e.g., connected to a power source, which may result in some signal gain. When combiner  450  is an active combiner, it may provide a different (e.g., configurable) amount of gain for each input signal so that the input signals have the same magnitude when they are combined. When combiner  450  is an active combiner, it may not need the fourth amplifier  452  because the active combiner may provide the signal amplification. 
     The output of the combiner  450  is input into mixers  448  and  446 . Mixers  448  and  446  generally down convert the received RF signal using inputs from local oscillators  472  and  470 , respectively, to create intermediate signals or baseband signals that carry the encoded and modulated information. The output of the mixers  448  and  446  are input into an analog-to-digital converter (ADC)  444  for conversion to digital signals. The digital signals output from ADC  444  is input to modem  402  for baseband processing, e.g., decoding, de-interleaving, etc. 
     The architecture  400  is given by way of example only to illustrate an architecture for transmitting and/or receiving signals. It will be understood that the architecture  400  and/or each portion of the architecture  400  may be repeated multiple times within an architecture to accommodate or provide an arbitrary number of RF chains, antenna elements, and/or antenna panels. Furthermore, numerous alternate architectures are possible and contemplated. For example, although only a single antenna array  418  is shown, two, three, or more antenna arrays may be included each with one or more of their own corresponding amplifiers, phase shifters, splitters, mixers, DACs, ADCs, and/or modems. For example, a single UE may include two, four or more antenna arrays for transmitting or receiving signals at different physical locations on the UE or in different directions. Furthermore, mixers, splitters, amplifiers, phase shifters and other components may be located in different signal type areas (e.g., different ones of the boxes  422 ,  424 ,  426 ,  428 ) in different implemented architectures. For example, a split of the signal to be transmitted into a plurality of signals may take place at the analog domain RF, analog domain IF, analog domain baseband, or digital domain baseband frequencies in different examples. Similarly, amplification, and/or phase shifts may also take place at different frequencies. For example, in some contemplated implementations, one or more of the splitter  410 , amplifiers  412 ,  416 , or Tx phase shifters  414  may be located between the DAC  404  and the first mixer  406  or between the first mixer  406  and the second mixer  408 . In one example, the functions of one or more of the components may be combined into one component. For example, the Tx phase shifters  414  may perform amplification to include or replace the first and/or or second amplifiers  412 ,  416 . By way of another example, a phase shift may be implemented by the second mixer  408  to obviate the need for a separate Tx phase shifter  414 . This technique is sometimes called local oscillator (LO) phase shifting. In one implementation of this configuration, there may be a plurality of IF to RF mixers (e.g., for each antenna element chain) within the second mixer  408  and the local oscillator B  432  would supply different local oscillator signals (with different phase offsets) to each IF to RF mixer. 
     The modem  402  and/or the neural network block  434  may control one or more of the other components  404 - 472  to select one or more antenna elements  420  and/or to form beams for transmission of one or more signals. For example, the antenna elements  420  may be individually selected or deselected for transmission of a signal (or signals) by controlling an amplitude of one or more corresponding amplifiers, such as the first amplifiers  412  and/or the second amplifiers  416 . Beamforming includes generation of a beam using a plurality of signals on different antenna elements where one or more or all of the plurality signals are shifted in phase relative to each other. The formed beam may carry physical or higher layer reference signals or information. As each signal of the plurality of signals is radiated from a respective antenna element  420 , the radiated signals interact, interfere (constructive and destructive interference), and amplify each other to form a resulting beam. The shape (such as the amplitude, width, and/or presence of side lobes) and the direction (such as an angle of the beam relative to a surface of the antenna array  418 ) can be dynamically controlled by modifying the phase shifts or phase offsets imparted by the Tx phase shifters  414  and amplitudes imparted by the amplifiers  412 ,  416  of the plurality of signals relative to each other. 
     In some examples, the UE  115  may receive a reference signal. The reference signal may be passed through a third amplifier  456  to amplify the reference signal, and Rx phase shifter  454  to align the phase of one or more reference signals received across the multiple antenna elements  420 , and a fourth amplifier  452 . The combiner  450  may combine signals received at various antenna elements  420  to generate the complete and aligned reference signal, and may pass the reference signal to mixers  448  and  446 . Mixers  448  and  446  may down convert the received reference signal using inputs from local oscillators  472  and  470 , respectively, to create intermediate or baseband signals that carry encoded and modulated data of the reference signal. The UE  115  may input the output of the mixers  448  and  446  into ADC  444  for conversion to digital domain signals. The digital domain signals output from ADC  444  are input to modem  402  for baseband processing, e.g., decoding, de-interleaving, etc. The decoded digital domain signal may then be forwarded to a processor for additional processing. 
     In some examples (e.g., in a long-term evolution (LTE) system) a UE  115  may receive signals using a single receive chain. That is, UE  115  may receive a signal (e.g., a reference signal) at a single antenna element  420 , and pass the signal through a third amplifier  456 . No Rx phase shifter  454  and no combiner  450  may be necessary in such examples, (e.g., because the signal was received at a single antenna element  420 ) Similarly, UE  115  may not pass the received signal through both a mixer  448  and a mixer  446  (e.g., because a signal in an LTE system may not be a mmW signal, and therefore may not need to be mixed to an intermediate signal and then to a baseband signal). Having passed the signal through a single filter, the UE  115  may pass the mixed signal through the ADC  444 , generating a digital domain baseband signal for the modem  402 . Or, in such examples (e.g., in an LTE system), the UE  115  may transmit signals using a single transmit chain. That is, UE  115  may generate a signal for transmission. Modem  402  may prepare a digital domain signal (e.g., a bitstream) for transmission, and may pass the digital domain signal through the DAC  404 . The UE  115  may then pass the analog domain baseband signal through a first mixer  406 . A single mixer (e.g., first mixer  406 ) may be sufficient to transmit the signal (e.g., because the signal is not an mmW signal). The first mixer  406  may upconvert the baseband signal to an appropriate frequency. The UE  115  may not have need of a splitter  410  because it utilizes a single transmit chain. Similarly, UE  115  may have no need of a Tx phase shifter  414  (because no phase alignment across multiple antenna elements  420  may be needed for a single transmission chain). The UE  115  may pass the signal to a first amplifier  412 , and may transmit the signal using a single antenna element  420 . 
     In some examples, a UE  115  (e.g., as described with reference to  FIGS. 1 to 3 ) may perform channel estimation. For example, the UE  115  may be configured to know one or more transmission parameters for one or more reference signals transmitted by a base station  105 . Transmission parameters for the reference signals may include phase, modulation, waveform, or the like. The UE  115  may receive a reference signal using one or more antenna elements  420 . Because modem  402  may be aware of the transmission parameters of the reference signal, modem  402  may be able to determine any differences between the received reference signal and the configured transmission parameters. These differences may be identified as distortions occurring to OTA signals over the channel on which the reference signal was received. The UE  115  (e.g., via modem  402 ) may generate one or more corrections that can be used to address the identified distortion, resulting in improved reception of subsequent signals and successful processing of the received reference signal. 
     In a non-limiting example in which processed signals are baseband signals, the neural network block  434  may perform (e.g., at modem  402 ) one or more operations on baseband signals. In this example, the modem  402  may extract relevant data from the reference signal (e.g., a subset of the total number of bits included in the digital domain baseband signal) and forward the relevant data on to a processor for further processing. However, neural network block  434  may perform its operation on baseband signals (e.g., prior to data extraction by modem  402  or further processing by a processor). In some examples, neural network block  434  may perform channel estimation. That is, neural network block  434  may receive the reference signal in modem  402 , and may process the reference signal using the neural network block  434 . Neural network block  434  may generate one or more outputs, which may indicate error correction to address distortions (e.g., sampling time, finding carrier frequency offset (CFO), phase correction, or the like) to OTA signals on the channel In some examples, processing baseband signals using neural network blocks (e.g., instead of traditional algorithms), may result in improved channel estimation. For example, gathered data may be provided to the base station  105 , which may be used to update one or more neural network block parameters over time. Thus, the neural network block may be trained over time to learn behaviors (e.g., learn patterns and channel conditions in particular environments, scenarios, etc.). By processing baseband signals using neural network blocks, the neural network blocks may be able to accurately predict or determine channel conditions. 
     In some examples, (e.g., a highly mobile UE  115 , a UE  115  that is located within a moving vehicle, etc.) channel conditions may rapidly change. Performing channel estimation as described herein using neural network block  434  may result in improved channel estimation (e.g., even when channel conditions are not available or known in real-time), and improved throughput and user experience. To perform such channel estimation by neural network block  434 , the UE  115  and base station  105  may communicate capability information, neural network block parameters, feedback information, and the like, as described in greater detail with reference to  FIG. 5 . 
       FIG. 5  illustrates an example of a process flow  500  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. In some examples, process flow  500  may implement aspects of wireless communications system  100 , wireless communications system  200 , neural network block  300 , and architecture  400 . 
     At  515 - a  and  515 - b , base station  105 - b  and UE  115 - c  may negotiate the availability of various neural network blocks for the UE  115 - c , the base station  105 - b , or both. For example, at  515 - a , base station  105 - b  may transmit capability information to UE  115 - c . The capability information may indicate one or more neural network blocks capable of being implemented by base station  105 - b . At  515 - b , UE  115 - c  may transmit capability information to base station  105 - b . The capability information may include one or more neural network blocks capable of being implemented by UE  115 - c . Thus, base station  105 - b  may determine one or more neural network blocks that are capable of being implemented by both base station  105 - b  and UE  115 - c . In some examples, capability information from base station  105 - b  may be included in a master information block (MIB), a system information block (SIB), a DCI message, a MAC-CE message, an RRC message, or the like. In some examples, base station  105 - b  may indicate (e.g., via the capability information at  515 - a ), that it provides related services such as particular neural network blocks (e.g., using one bit of a MIB, SIB, MAC-CE, DCI, etc. for a particular neural network block), and UE  115 - c  may indicate (e.g., at  515 - b ) whether it supports the same service (e.g., the same neural network block). 
     In block  520 , base station  105 - b  may configure one or more neural network block parameters for a neural network block of the one or more neural network blocks capable of being implemented by UE  115 - c  based on the capability information exchanged at  515 . 
     At  530 , base station  105 - a  may transmit the neural network block parameters to UE  115 - c . The neural network block parameters may include one or more adjustment parameters for a neural network block capable of being implemented by UE  115 - c , or configuration information for a new neural network block, or an indication of one of a set of neural network blocks, as described in greater detail below. 
     In block  535 , UE  115 - c  may process one or more signals (e.g., baseband signals) using a neural network block and the neural network block parameters received at  530 . For example, UE  115 - c  may receive one or more reference signals, as described in greater detail with reference to  FIG. 4 . UE  115 - c  may generate a baseband signal, and may process, at the modem of UE  115 - c , the baseband signal. In some examples, UE  115 - c  may process real-time received baseband signals using the neural network block. For example, UE  115 - c  may determine channel distortions for one or more channels, and may address or correct the channel distortions by inputting measurement data, historical data, or input values indicated by the one or more neural network block parameters. The neural network block may generate a set of output values that represent corrections to channel distortion for a channel In some examples, UE  115 - c  may not have access to real-time channel quality measurements, or may be located or moving through a highly variant environment. In such examples, UE  115 - c  may process one or more baseband signals using the neural network block. UE  115 - c  may generate, by processing the baseband signals using the neural network block, one or more output values. The one or more output values may represent learned channel conditions under current conditions, or channel conditions based on learned behaviors from other UEs  115  in the same or similar locations, scenarios, or the like. In such examples, the neural network block may generate one or more output values that represent channel distortion values, corrections to channel distortions for the channel, or a combination thereof. 
     At  540 , if UE  115 - c  has successfully received the neural network block parameters at  530 , UE  115 - c  may transmit an ACK message to base station  105 - b . However, (e.g., if the neural network block used to process the baseband signals are considered critical), if UE  115 - c  does not successfully receive the neural network block parameters at  530 , then UE  115 - c  may transmit a NACK message at  545 . 
     The process described with reference to  FIG. 5  may vary depending on supported neural network blocks, neural network block types, and neural network block parameters, as described below. 
     In some examples, UE  115 - c  may support a neural network block with one or more dynamic configuration options. UE  115 - c  may report the configurable neural network block in its capability information at  515 - b.    
     In block  520 , base station  105 - b  may configure one or more neural network block parameters for the configurable neural network block reported at  515 - b . The neural network block parameters may include one or more adjustment parameters, which may apply to one or more nodes of the neural network block to reconfigure the neural network block. As described in greater detail with reference to  FIG. 3 , a node in a neural network block may be referred to as a neuron, a node, a block, or the like). The adjustment parameters may include an activation indication for one or more nodes of the neural network block, a deactivation indication of one or more nodes of the neural network block, an activation indication of one or more layers of the neural network block, a deactivation indication of one or more layers of the neural network block, a weight value for one or more nodes of the neural network block, or a bias value for one or more nodes of the neural network block, or a combination thereof. In some examples, the neural network block parameters may include weight values, bias values, or the like, for supplementary submodules of the neural network block. For example, the adjustment parameters may indicate first adjustment parameters (e.g., weight values, bias values, a connection map, or the like) for a primary submodule and second adjustment parameters (e.g., weight values, bias values, a connection map, or the like) for a secondary submodule. 
     In some examples, the one or more adjustment parameters of the neural network block parameters configured by the base station  105 - b  may be used to adjust neural network blocks corresponding to various network components. For example, master cell group, secondary cell group, component carriers, BWPs, uplink layers, supplementary uplink layers, and the like, may each be associated with or operated by one or more neural network blocks having the same or different neural network configurations characterized by neural network block parameters. In such examples, the neural network block parameters may include configuration information for a new neural network block to replace (permanently or temporarily) the default neural network block providing functionality for any network component. In such examples, the neural network block parameters may include an indication of one or more added algorithms for the neural network block, one or more released algorithms from the default network function block, or a combination thereof. The neural network block parameters (e.g., the configuration information) may further include an activation indication for one or more nodes of the neural network block, a deactivation indication for one or more nodes of the neural network block, an activation indication for one or more layers of the neural network block, a deactivation indication for the one or more layers of the neural network block, a weight value for one or more nodes of the neural network block, or a bias value for one or more nodes of the neural network block, or a combination thereof. In some examples, the neural network block parameters may include weight values, bias values, or the like, for supplementary submodules of the neural network block. 
     A base station  105 - b  may replace, or override, an existing neural network block with a new neural network block, add additional configurable parameters to an existing neural network block, or take the place of a default network function block. Replacing a default network function block or a previously implemented neural network block with a new neural network block may increase operating rates and efficiency. By providing UE  115 - c  with a supported neural network block, resources may be reserved by performing default network block functionality without requiring additional training of the neural network block. 
     Overriding existing functional blocks may be implemented in a hierarchical manner. For example, a master cell group may be associated with four component carriers operating at various frequencies according to the default network function block. For example, one of the component carriers may be operating across a frequency range FR1 of 410 MHz to 7125 MHz, and the remaining three component carriers may be operating across a frequency range FR2 of 24.25 GHz to 52.6 GHz. Thus, when determining a neural network to replace, or override, the default network function block, a base station  105 - b  may search for, within a repository of neural networks, a suitable neural network that is customized to three of the component carriers operating at frequency range FR2. 
     A base station  105 - b  may transmit the suitable neural network block to UE  115 - c  to cause the default network function block or the existing neural network block associated with the master cell group to be overridden. As a result, the master cell group functionality may be replaced, which may then replace the functionality of each carrier component associated with that master cell group. Similarly, implementing a new neural network block to perform functions of a majority of component carriers may also adjust the functionality of each BWP associated with each component carrier in a hierarchical manner. 
     Configurable parameters, or adjustment parameters, may be added to existing neural network blocks, or added to one or more new neural network blocks when replacing a default network block or an existing neural network block. Additional configurable parameters may be provided in block  520 . In addition to the new neural network block, a base station  105 - b  may also transmit additional neural network block parameters at  530  to further train or refine a neural network in block  535  for a specific application in the UE  115 - c . Taking the example above with the master cell group and the four associated component carriers, the newly implemented neural network block may not be suitable for the desired functionality of the carrier component operating at FR1. Thus, the base station  105 - b  may provide additional neural network block parameters to be associated with FR1 carrier components directly to further refine or train the neural network block so that the carrier component operating at FR1 can function according to the requested needs of the UE  115 - c.    
     Additional adjusted parameters may be defined and transmitted by a base station  105 - b  to refine or train any portion of the implemented neural network. In the example of the master cell group and four associated component carriers, although the implemented neural network block may be suitable for three of the four component carriers, each of the component carriers may have different compression rates. Assuming that the neural network block to be implemented does not define or loosely defines the compression rates, a base station  105 - b  may transmit, at  530 , neural network block parameters in addition to those defining the neural network block to be implemented. These additional neural network block parameters not originally associated with the neural network block may adjust the neural network block in block  535  to account for the various compression rates of the component carriers. Thus, the suitable neural network block selected by base station  105 - b , based in part on the supported neural network blocks of UE  115 - c , may be further refined for each specific application. 
     In some examples, network components (e.g., master cell group, component carrier, BWP, supplementary uplink) may be configured directly on an individual basis. For example, a UE  115 - c  may be associated with three BWPs, each needing to operate at specific parameters for a given application. A base station  105 - b  may receive capability information from the UE  115 - c  at  515 - b , and may determine that a hierarchical approach (i.e. overriding the previously existing neural network defining the functionality of the master cell group and component carrier associated with the BWPs to adjust the functionality of the BWPs) is inefficient. Thus, the base station  105 - b  may configure one or more neural network blocks having neural network block parameters to individually override portions of the existing neural network, such that the functionality of the BWPs is overridden or adjusted, while preserving the functionality of the master cell group and the component carrier. 
     In some examples, two distinct neural network blocks, each having neural network block parameters, may be transmitted to the UE  115 - c  at  530 . The two sets of neural network block parameters may be transmitted at  530  simultaneously, or consecutively. 
     Base station  105 - b  may send the adjustment parameters at  530 . In such examples, base station  105 - b  may transmit a DCI, or a MAC-CE including the adjustment parameters at  530 . In some examples, base station  105 - b  may reserve PDSCH resources for transmitting the adjustment parameters. For example, if the adjustment parameters include neural network block parameters for multiple submodules of the neural network block, then the amount of information needed to convey the adjustment parameters may exceed those available in a DCI or MAC-CE. Instead, base station  105 - b  may transmit a resource allocation message at  525  (e.g., an RRC message, or a DCI message), reserving PDSCH resources for transmitting the adjustment parameters. At  530 , base station  105 - b  may transmit the adjustment parameters on the reserved PDSCH resources. 
     In block  535 , UE  115 - c  may process one or more signals, e.g., baseband signals, using the neural network block and the adjustment parameters. That is, UE  115 - c  may apply the one or more adjustment parameters to the neural network block, and process the signals using the reconfigured neural network block. In some examples (e.g., where the adjustment parameters include first parameters and second parameters for multiple submodules), UE  115 - c  may perform a first operation on the signals using a first node or set of nodes having a first weight value indicated by the adjustment parameters, and may perform a second operation on the signals using a second node or set of nodes having a second weight value indicated by the adjustment parameters. For instance, UE  115 - c  may adjust the weight values, bias values, or a connection map for a primary submodule according to the adjustment parameters, and may adjust the weight values, bias values, or a connection map for a secondary submodule. UE  115 - c  may perform weighted multiplication in each of the primary submodule and the secondary submodule, and may perform a weighed sum of the result according to the adjustment parameters. 
     If the neural network block is critical, (e.g., time-sensitive), then UE  115 - c  may activate in block  535  (e.g., use for processing current baseband signals) the reconfigured (e.g., adjusted) neural network block immediately upon receiving the one or more network block parameters at  530 . If UE  115 - c  successfully receives the neural network block parameters at  530 , then UE  115 - c  may send an ACK message at  540  (e.g., an ACK corresponding to the DCI message, MAC-CE, or PDSCH message). However, if UE  115 - c  fails to receive the neural network block parameters at  530 , then UE  115 - c  may transmit a NACK message at  545  (e.g., a NACK corresponding to the DCI message, MAC-CE message, or PDSCH message). In some examples, the priority (e.g., critical status) of a neural network block may be preconfigured, signaled via higher layer signaling, communicated in the capability information at  515 , or otherwise indicated by base station  105 - b.    
     If the neural network block is not critical (e.g., has a normal or non-critical priority), then the timing for using (e.g., applying) the neural network block may be indicated by base station  105 - b  (e.g., via RRC signaling). For example, an RRC message (e.g., at  530 ,  525 , or otherwise (not shown)) may indicate a timer, or a transmission time interval (TTI) counter (e.g., a symbol counter, slot counter, or the like). The timer may be an inactive timer or an active timer. In some examples, UE  115 - c  in block  535  may initiate the timer upon receiving the neural network block parameters. Upon expiration of the timer, UE  115 - c  may activate in block  535  (e.g., begin using) the reconfigured neural network block. In some examples, UE  115 - c  may initiate the timer upon receiving the neural network block parameters or upon using the reconfigured neural network block, and upon expiration of the timer, may default to a previously used or default neural network block. Similarly, UE  115 - c  may receive a TTI counter (e.g., via RRC signaling). The TTI counter may indicate a number of TTIs (e.g., slots, symbols, mini-slots, or the like) after which to activate the reconfigured neural network block, deactivate the reconfigured neural network block, revert to a previously used or default neural network block or function block, or the like. In such examples, UE  115 - c  may count the indicated number of TTIs and perform the indicated function. In some examples, UE  115 - c  may transmit an ACK message at  540  (e.g., a PUCCH message, or higher layer (e.g., RRC) signal). 
     In some examples, UE  115 - c  may support a default function block or neural network block. UE  115 - c  may report the default neural network block in its capability information at  515 - b . In some examples, UE  115 - c  may identify the default block based on RRC signaling or DCI. For example, upon establishing a connection (e.g., via a random access procedure), UE  115 - c  and base station  105 - b  may negotiate at least one default neural network block or network function block. In some examples, during reconfiguration (e.g., via another RRC message or a DCI message), base station  105 - b  may assign a new default neural network block or network function block. 
     Base station  105 - b  may configure one or more neural network block parameters based on the capability information received at  515 - b . In such examples, the neural network block parameters may include configuration information for a new neural network block to replace (permanently or temporarily) the default neural network block. In such examples, the neural network block parameters may include an indication of one or more added algorithms for the neural network block, one or more released algorithms from the default network function block, or a combination thereof. The neural network block parameters (e.g., the configuration information) may further include an activation indication for one or more nodes of the neural network block, a deactivation indication for the one or more nodes of the neural network block, an activation indication for one or more layers of the neural network block, a deactivation indication for one or more layers of the neural network block, a weight value for one or more nodes of the neural network block, or a bias value for one or more nodes of the neural network block, or a combination thereof. In some examples, the neural network block parameters may include weight values, bias values, or the like, for supplementary submodules of the neural network block. 
     In such examples, base station  105 - b  may transmit a resource allocation message at  525  to reserve PDSCH resources for transmitting the one or more neural network block parameters. The resource allocation message may be a dynamic resource allocation message (e.g., a DCI message) or a semi-persistent resource allocation message (e.g., an RRC message). The resource allocation message may reserve sufficient resources (e.g., a set of resources across a set of two or more slots) for transmitting the neural network block parameters for configuring a new neural network block at UE  115 - c . At  530 , base station  105 - b  may transmit the neural network block parameters on the PDSCH as scheduled at  525 . 
     In block  535 , UE  115 - c  may process one or more signals, e.g., baseband signals, using the newly configured neural network block. In some examples, UE  115 - c  may also receive control information (e.g., on a physical downlink control channel (PDCCH)) from base station  105 - b  indicating a new network function block or neural network block to be the default network function block. UE  115 - c  may include the default neural network function block in the capability information at  515 - b  based on the control information. In some examples, UE  115 - c  may transmit a request message to use a second neural network block that is different than the currently used neural network block. UE  115 - c  may continue to use the current neural network block until it receives an ACK message from base station  105 - b . Upon receiving the ACK message, UE  115 - c  may switch from the current neural network block to the newly requested neural network block, and may continue to process one or more signals using the newly requested neural network block in block  535 . 
     If the neural network block is critical, (e.g., time-sensitive), then UE  115 - c  may activate in block  535  (e.g., use for processing current baseband signals) the reconfigured (e.g., adjusted) neural network block immediately upon receiving the one or more neural network block parameters at  530 . If UE  115 - c  successfully receives the neural network block parameters at  530 , then UE  115 - c  may send an ACK message at  540  (e.g., an ACK corresponding to the DCI message, MAC-CE, or PDSCH message). However, if UE  115 - c  fails to receive the neural network block parameters at  530 , then UE  115 - c  may transmit a NACK message at  545  (e.g., a NACK corresponding to the DCI message, MAC-CE message, or PDSCH message). If base station  105 - b  receives the NACK message at  545 , then base station  105 - b  may schedule more PDSCH resources for retransmitting the neural network block parameters, as described above with reference to  525  and  530 . In some examples, the priority (e.g., critical status) of a neural network block may be preconfigured, signaled via higher layer signaling, communicated in the capability information at  515 , or otherwise indicated by base station  105 - b.    
     If the neural network block is not critical (e.g., has a normal or non-critical priority), then the timing for using (e.g., applying) the neural network block may be indicated by base station  105 - b  (e.g., via RRC signaling). In some examples, an RRC message (e.g., at  530 ,  525 , or another RRC message (not shown)), may include an information element (IE) for releasing (e.g., IE: “Torelease:”) a list of one or more function blocks (e.g., one or more algorithms), an IE for adding (e.g., IE: “Toadd:”) a list of one or more function blocks (e.g., one or more algorithms). 
     In some examples, an RRC message (e.g., at  530 ,  525 , or otherwise (not shown)) may indicate a timer, or a transmission time interval (TTI) counter (e.g., a symbol counter, slot counter, or the like). The timer may be an inactive timer or an active timer. In some examples, UE  115 - c  may initiate the timer upon receiving the neural network block parameters. Upon expiration of the timer, UE  115 - c  may activate in block  535  (e.g., begin using) the reconfigured neural network block. In some examples, UE  115 - c  may initiate the timer upon receiving the neural network block parameters or upon using the reconfigured neural network block, and upon expiration of the timer, may default to a previously used or default neural network block. Similarly, UE  115 - c  may receive a TTI counter (e.g., via RRC signaling). The TTI counter may indicate a number of TTIs (e.g., slots, symbols, mini-slots, or the like) after which to activate the reconfigured neural network block, deactivate the reconfigured neural network block, revert to a previously used or default neural network block or function block, or the like. In such examples, UE  115 - c  may count the indicated number of TTIs and perform the indicated function. In some examples, UE  115 - c  may transmit an ACK message at  540  (e.g., a PUCCH message, or higher layer (e.g., RRC) signaling). 
     In some examples, UE  115 - c  may support a set of one or more pre-trained neural network blocks. UE  115 - c  may indicate the set of neural network blocks in its capability information to base station  105 - b  at  515 - b.    
     In block  520 , base station  105 - b  may configure neural network block parameters for UE  115 - c . The neural network block parameters may include one or more bits (e.g., in a DCI message, a MAC-CE message, or an RRC message) indicating one of the set of neural network blocks. 
     At  530 , UE  115 - c  may receive the neural network block parameters, and in block  535  UE  115 - c  may process one or more signals, e.g., baseband signals, using the indicated neural network block of the set of neural network blocks. 
     In some examples, UE  115 - c  may provide feedback data indicating performance of the neural network block. For example, base station  105 - b  may transmit a request to UE  115 - c  requesting the feedback information, and UE  115 - c  may transmit a feedback report based on processing baseband signals using the neural network block. The feedback data may be processed data, unprocessed data, complete measurements, estimations, or predictions of channel quality, partial measurements, estimations, or predictions of channel quality, or a combination thereof, as described in greater detail with reference to  FIG. 2 . In some examples, base station  105 - b  may request and receive such feedback information from UE  115 - c , and various other UEs  115  (not shown). Base station  105 - b  may utilize this additional information in configuring neural network block parameters (e.g., in block  520  of a subsequent iteration with UE  115 - c ). That is, the processing of baseband signals using the neural network block in block  535  can be improved with updated, reconfigured, or new neural network blocks (e.g., based on feedback information from UE  115 - c  or other UEs  115 ), then base station  105 - b  may adjust the neural network block parameters accordingly to improve the accuracy and efficiency of the neural network block, thus improving system efficiency, throughput, and user experience. 
     In some examples, a base station  105 - b  may configure control signaling with one or more neural network block parameters after receiving UE capability information at  515 - b . Based on capability information received from a UE  115 - c , the base station  105 - b  may configure control signaling to include one or more neural network block parameters for configuring a first neural network block. Control signaling may be used by the base station  105 - b  to configure various network aspects (e.g., data path configurations) associated with communications with UE  115 - c . The control signaling may be a resource allocation message as described herein. The control signaling may include control information that may be transmitted to the UE  115 - c  as part of the control signaling (e.g., on a PDCCH), as opposed to being transmitted across a data path. The control information may include an indication of a new network function block or neural network block to be the default network function block, in addition to the neural network block parameters. The control signaling operating as a resource allocation message may include a downlink control information message, a media access control element, or radio resource control message, as described in various examples. 
     In some examples, the ACK message transmit by UE  115 - c  at  540  may include verification that the UE  115 - c  received the control signaling. The ACK message may include an indication that the control signaling and corresponding control information including neural network block parameters was successful in configuring the neural network block. In some examples, the NACK message transmit by the UE  115 - c  at  545  may include verification that the UE  115 - c  did not successfully receive a portion or all of the control signaling. The NACK message may include an indication that the control signaling and corresponding control information including neural network block parameters was not successful in configuring the neural network block. 
     At  550 , the UE  115 - c  may register the newly configured neural network block, and transmit the configuration to the base station  105 - b . Assuming the newly configured neural network block includes additional training from adjusted neural network block parameters, the UE  115 - c  may transmit the configuration of the newly configured neural network block to the base station  105 - b . The base station  105 - b  may receive the configuration of the newly configured neural network block, and store the corresponding parameters in a repository. Thus, the base station  105 - b  may use the received configuration or the corresponding neural network block parameters as a template for further iterations of the processes described in  FIG. 5 . The template may be used in processes in which a UE, similar to the UE  115 - c , requests a specific neural network block that is the same as or similar to the received neural network block characterized by the template of neural network block parameters. This may reduce the number of resources used to train previously available, less refined, neural network blocks by reducing the amount of data transferred at  530 . In some examples, the base station  105 - b  may receive a configuration of the configured first neural network block from the UE  115 - c  over a physical uplink control channel or a physical uplink shared channel. 
       FIG. 6  shows a block diagram  600  of a device  605  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The device  605  may be an example of aspects of a UE  115  as described herein. The device  605  may include a receiver  610 , a communications manager  615 , and a transmitter  620 . The device  605  may also include a processor. Each of these components may be in communication with one another (e.g., via one or more buses). The communications manager  615  may manage the communications via the receiver  610  and the transmitter  620 . Thus, the communications manager  615  may receive, via the receiver  610 , one or more signals, and may transmit, via the transmitter  620 , one or more signals. 
     The receiver  610  may receive, under the direction or control of the communications manager  615 , information such as packets, user data, or control information associated with various information channels (e.g., control channels, data channels, and information related to neural network configuration for wireless communication system assistance, etc.). Information may be passed on to other components of the device  605 . The receiver  610  may be an example of aspects of the transceiver  920  described with reference to  FIG. 9 . The receiver  610  may utilize a single antenna or a set of antennas. 
     The communications manager  615  may transmit, to a base station, capability information regarding one or more neural network blocks supported by the UE, receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information, process one or more baseband signals generated by the UE using a neural network block of the one or more neural network blocks and the one or more neural network block parameters, and transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. The communications manager  615  may be an example of aspects of the communications manager  910  described herein. In some cases, the one or more baseband signals received by the UE may refer (or correspond) to one or more baseband signals generated by the UE using one or more radio frequency signals received by the UE. 
     The communications manager  615 , or its sub-components, may be implemented in hardware, code (e.g., software or firmware) executed by a processor, or any combination thereof. If implemented in code executed by a processor, the functions of the communications manager  615 , or its sub-components may be executed by a general-purpose processor, a DSP, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described in the present disclosure. 
     The communications manager  615 , or its sub-components, may be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations by one or more physical components. In some examples, the communications manager  615 , or its sub-components, may be a separate and distinct component in accordance with various aspects of the present disclosure. In some examples, the communications manager  615 , or its sub-components, may be combined with one or more other hardware components, including but not limited to an input/output (I/O) component, a transceiver, a network server, another computing device, one or more other components described in the present disclosure, or a combination thereof in accordance with various aspects of the present disclosure. 
     The transmitter  620  may transmit signals generated by other components of the device  605 . In some examples, the transmitter  620  may be collocated with a receiver  610  in a transceiver module. For example, the transmitter  620  may be an example of aspects of the transceiver  920  described with reference to  FIG. 9 . The transmitter  620  may utilize a single antenna or a set of antennas. 
     In some examples, the communications manager  615  may be implemented as an integrated circuit or chipset for a mobile device modem, and the receiver  610  and transmitter  620  may be implemented as analog components or mixed components (e.g., amplifiers, filters, antennas) coupled with the mobile device modem to enable wireless transmission and reception over one or more bands. 
     The communications manager  615  as described herein may be implemented to realize one or more potential advantages. One implementation may allow the use of a neural network block without excessive power or overhead signaling expenditures, while saving computation resources at the device due to base station assistance. Such base station assisted neural network block training and implementation may result in improved throughput, increased efficiency, and improved user experience. 
     Based on techniques for efficiently communicating a maximum number of layers for a device as described herein, a processor of a UE  115  (e.g., controlling the receiver  610 , the transmitter  620 , or a transceiver  920  as described with respect to  FIG. 9 ) may increase system efficiency and decrease unnecessary processing at a device. 
       FIG. 7  shows a block diagram  700  of a device  705  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The device  705  may be an example of aspects of a device  605 , or a UE  115  as described herein. The device  705  may include a receiver  710 , a communications manager  715 , and a transmitter  740 . The device  705  may also include a processor. Each of these components may be in communication with one another (e.g., via one or more buses). 
     The receiver  710  may receive information such as packets, user data, or control information associated with various information channels (e.g., control channels, data channels, and information related to neural network configuration for wireless communication system assistance, etc.). Information may be passed on to other components of the device  705 . The receiver  710  may be an example of aspects of the transceiver  920  described with reference to  FIG. 9 . The receiver  710  may utilize a single antenna or a set of antennas. 
     The communications manager  715  may be an example of aspects of the communications manager  615  as described herein. The communications manager  715  may include a capability information manager  720 , a neural network block parameter manager  725 , a neural network block manager  730 , and an acknowledgement manager  735 . The communications manager  715  may be an example of aspects of the communications manager  910  described herein. 
     The capability information manager  720  may transmit, to a base station, capability information regarding one or more neural network blocks supported by the UE. 
     The neural network block parameter manager  725  may receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information. 
     The neural network block manager  730  may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     The acknowledgement manager  735  may transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. 
     The transmitter  740  may transmit signals generated by other components of the device  705 . In some examples, the transmitter  740  may be collocated with a receiver  710  in a transceiver module. For example, the transmitter  740  may be an example of aspects of the transceiver  920  described with reference to  FIG. 9 . The transmitter  740  may utilize a single antenna or a set of antennas. 
       FIG. 8  shows a block diagram  800  of a communications manager  805  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The communications manager  805  may be an example of aspects of a communications manager  615 , a communications manager  715 , or a communications manager  910  described herein. The communications manager  805  may include a capability information manager  810 , a neural network block parameter manager  815 , a neural network block manager  820 , an acknowledgement manager  825 , an adjustment parameter manager  830 , a resource allocation manager  835 , a monitoring manager  840 , a configuration information manager  845 , a timing manager  850 , and a feedback information manager  855 . Each of these modules may communicate, directly or indirectly, with one another (e.g., via one or more buses). 
     The capability information manager  810  may transmit, to a base station, capability information regarding one or more neural network blocks supported by the UE. In some examples, the capability information manager  810  may identify the neural network block having one or more configuration options, where the one or more neural network blocks supported by the UE include the neural network block. 
     In some examples, the capability information manager  810  may identify a default network function block, where the one or more neural network blocks supported by the UE includes the default network function block. 
     In some examples, the capability information manager  810  may receive control information from the base station that indicates a new network function block to be the default network function block, where identifying the default network function block is based on receiving the control information. 
     In some examples, the capability information manager  810  may identify a set of neural network blocks stored by the UE, where the one or more neural network blocks supported by the UE includes the set of neural network blocks. 
     In some examples, the capability information manager  810  may receive, from the base station, second capability information indicating that the base station supports at least one neural network block, where transmitting the capability information to the base station is based on receiving the second capability information. 
     In some cases, the capability information manager  810  may manage capability information transmitted in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     The neural network block parameter manager  815  may receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information. In some examples, the neural network block parameter manager  815  may receive configuration information for the neural network block and the one or more neural network block parameters over the physical downlink shared channel based on receiving the resource allocation message. In some examples, the neural network block parameter manager  815  may receive an indication of the neural network block of the set of neural network blocks. In some examples, the neural network block parameter manager  815  may receive a downlink message that includes the one or more neural network block parameters, where receiving the one or more neural network block parameters is based on receiving the downlink message. 
     In some examples, the neural network block parameter manager  815  may receive, from the base station, one or more additional neural network block parameters. 
     In some cases, one or more neural network block parameters are received as part of a downlink data message. In some cases, the downlink message includes a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. In some cases, the one or more neural network block parameters include one or more input values, a number of layers of the neural network block, a number of nodes for one or more layers of the neural network block, a connection map across the one or more layers of the neural network block, one or more activation functions for one or more nodes of the neural network block, one or more weight values for the one or more nodes of the neural network block, an adjustment to one or more weight values for one or more submodules, or one or more bias values for the one or more nodes of the neural network block, or a combination thereof. 
     The neural network block manager  820  may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     In some examples, the neural network block manager  820  may perform a first operation on signals, e.g., baseband signals using a first node of the neural network block based on a first weight value indicated by the one or more adjustment parameters. 
     In some examples, the neural network block manager  820  may perform a second operation on signals, e.g., baseband signals, using a second node of the neural network block based on a second weight value indicated by the one or more adjustment parameters, where processing the one or more signals using the neural network block is based on performing the first operation and the second operation. 
     In some examples, the neural network block manager  820  may transmit, by the UE, a request message to use a second neural network block different than the neural network block. 
     In some examples, the neural network block manager  820  may process signals, e.g., baseband signals, using the second neural network block based on transmitting the request message. 
     In some examples, the neural network block manager  820  may identify the neural network block of the set of neural network blocks based on receiving the indication of the neural network block, where processing the one or more signals, e.g., baseband signals, using the neural network block is based on identifying the neural network block. 
     In some examples, the neural network block manager  820  may process, based on determining that the timer has expired, the one or more signals, e.g., baseband signals, using a default neural network block different than the neural network block. In some cases, the neural network block may be configured to perform channel estimation for one or more baseband signals, channel state information compression for the one or more baseband signals, or a combination thereof. 
     The acknowledgement manager  825  may transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. In some examples, the acknowledgement manager  825  may receive, from the base station, an acknowledgment message based on the transmitting of the request message, where processing signals, e.g., baseband signals, using the second neural network block is based on receiving the acknowledgement message. In some examples, the acknowledgement manager  825  may transmit, to the base station, a negative acknowledgement message indicating that the one or more neural network block parameters failed to be successfully decoded. In some examples, the acknowledgement manager  825  may determine a priority status of the one or more additional neural network block parameters, where transmitting the negative acknowledgement message is based on the priority status. 
     The adjustment parameter manager  830  may adjust the neural network block according to the one or more adjustment parameters, where processing the one or more signals, e.g., baseband signals, using the neural network block is based on the adjusting. In some cases, the one or more adjustment parameters include an activation indication for one or more nodes of the neural network block, a deactivation indication of the one or more nodes of the neural network block, a weight value for the one or more nodes of the neural network block, or a bias value for the one or more nodes of the neural network block, or a combination thereof. 
     The resource allocation manager  835  may receive a resource allocation message for a physical downlink shared channel In some examples, the resource allocation manager  835  may receive, from the base station, a resource allocation message for a physical downlink shared channel In some cases, the resource allocation message includes a downlink control information message or radio resource control message. In some cases, the resource allocation message includes a radio resource control message, or a downlink control information message. 
     The monitoring manager  840  may monitor the physical downlink shared channel for the one or more neural network block parameters based on receiving the resource allocation message, where receiving the one or more neural network block parameters is based on monitoring the physical downlink shared channel In some examples, the monitoring manager  840  may monitor for the configuration information based on receiving the resource allocation message, where receiving the configuration information is based on the monitoring. 
     The configuration information manager  845  may configure the neural network block in place of a default network function block of the UE based on receiving the configuration information and the one or more neural network block parameters, where processing the one or more signals, e.g., baseband signals, using the neural network block is based on the configuring. In some cases, the configuration information includes an indication of one or more added algorithms for the neural network block, one or more released algorithms from the default network function block, or a combination thereof. 
     The timing manager  850  may initiate, upon receiving the one or more neural network block parameters, a timer, where processing the one or more signals, e.g., baseband signals, using the neural network block is based on an expiration of the timer. In some examples, the timing manager  850  may initiate, upon receiving the one or more neural network block parameters, a counter of symbols, a counter of slots, or a combination thereof, where processing the one or more signals using the neural network block is based on the counter of symbols, the counter of slots, or the combination thereof satisfying a threshold. In some examples, the timing manager  850  may initiate, upon processing the one or more signals using the neural network block, a timer. In some examples, the timing manager  850  may determine that the timer has expired. 
     The feedback information manager  855  may receive, from the base station, a request for feedback information about a performance of the neural network block. In some examples, the feedback information manager  855  may transmit, based on processing the one or more signals, e.g., baseband signals, using the neural network block and the request, a report including the feedback information about the performance of the neural network block to the base station. In some cases, the request is communicated using a DCI message, a MAC-CE message, or an RRC message. In some cases, the feedback information is communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel In some cases, the feedback information includes processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
       FIG. 9  shows a diagram of a system  900  including a device  905  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The device  905  may be an example of or include the components of device  605 , device  705 , or a UE  115  as described herein. The device  905  may include components for bi-directional voice and data communications including components for transmitting and receiving communications, including a communications manager  910 , an I/O controller  915 , a transceiver  920 , an antenna  925 , memory  930 , and a processor  940 . These components may be in electronic communication via one or more buses (e.g., bus  945 ). 
     The communications manager  910  may transmit, to a base station, capability information regarding one or more neural network blocks supported by the UE, receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information, process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the one or more neural network block parameters, and transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. 
     The I/O controller  915  may manage input and output signals for the device  905 . The I/O controller  915  may also manage peripherals not integrated into the device  905 . In some cases, the I/O controller  915  may represent a physical connection or port to an external peripheral. In some cases, the I/O controller  915  may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, or another known operating system. In other cases, the I/O controller  915  may represent or interact with a modem, a keyboard, a mouse, a touchscreen, or a similar device. In some cases, the I/O controller  915  may be implemented as part of a processor. In some cases, a user may interact with the device  905  via the I/O controller  915  or via hardware components controlled by the I/O controller  915 . 
     The transceiver  920  may communicate bi-directionally, via one or more antennas, wired, or wireless links as described above. For example, the transceiver  920  may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver  920  may also include a modem to modulate the packets and provide the modulated packets to the antennas for transmission, and to demodulate packets received from the antennas. 
     In some cases, the wireless device may include a single antenna  925 . However, in some cases the device may have more than one antenna  925 , which may be capable of concurrently transmitting or receiving multiple wireless transmissions. 
     The memory  930  may include RAM and ROM. The memory  930  may store computer-readable, computer-executable code  935  including instructions that, when executed, cause the processor to perform various functions described herein. In some cases, the memory  930  may contain, among other things, a basic input output system (BIOS), which may control basic hardware or software operation such as the interaction with peripheral components or devices. 
     The processor  940  may include an intelligent hardware device, (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor  940  may be configured to operate a memory array using a memory controller. In other cases, a memory controller may be integrated into the processor  940 . The processor  940  may be configured to execute computer-readable instructions stored in a memory (e.g., the memory  930 ) to cause the device  905  to perform various functions (e.g., functions or tasks supporting neural network configuration for wireless communication system assistance). 
     The code  935  may include instructions to implement aspects of the present disclosure, including instructions to support wireless communications. The code  935  may be stored in a non-transitory computer-readable medium such as system memory or other type of memory. In some cases, the code  935  may not be directly executable by the processor  940  but may cause a computer (e.g., when compiled and executed) to perform functions described herein. 
       FIG. 10  shows a block diagram  1000  of a device  1005  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The device  1005  may be an example of aspects of a base station  105  as described herein. The device  1005  may include a receiver  1010 , a communications manager  1015 , and a transmitter  1020 . The device  1005  may also include a processor. Each of these components may be in communication with one another (e.g., via one or more buses). 
     The receiver  1010  may receive information such as packets, user data, or control information associated with various information channels (e.g., control channels, data channels, and information related to neural network configuration for wireless communication system assistance, etc.). Information may be passed on to other components of the device  1005 . The receiver  1010  may be an example of aspects of the transceiver  1320  described with reference to  FIG. 13 . The receiver  1010  may utilize a single antenna or a set of antennas. 
     The communications manager  1015  may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks, configure one or more neural network block parameters for a neural network block of the one or more neural network blocks supported by the UE based on the capability information, transmit, to the UE, the one or more neural network block parameters, and receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. The communications manager  1015  may be an example of aspects of the communications manager  1310  described herein. 
     The communications manager  1015 , or its sub-components, may be implemented in hardware, code (e.g., software or firmware) executed by a processor, or any combination thereof. If implemented in code executed by a processor, the functions of the communications manager  1015 , or its sub-components may be executed by a general-purpose processor, a DSP, an application-specific integrated circuit (ASIC), a FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described in the present disclosure. 
     The communications manager  1015 , or its sub-components, may be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations by one or more physical components. In some examples, the communications manager  1015 , or its sub-components, may be a separate and distinct component in accordance with various aspects of the present disclosure. In some examples, the communications manager  1015 , or its sub-components, may be combined with one or more other hardware components, including but not limited to an input/output (I/O) component, a transceiver, a network server, another computing device, one or more other components described in the present disclosure, or a combination thereof in accordance with various aspects of the present disclosure. 
     The transmitter  1020  may transmit signals generated by other components of the device  1005 . In some examples, the transmitter  1020  may be collocated with a receiver  1010  in a transceiver module. For example, the transmitter  1020  may be an example of aspects of the transceiver  1320  described with reference to  FIG. 13 . The transmitter  1020  may utilize a single antenna or a set of antennas. 
       FIG. 11  shows a block diagram  1100  of a device  1105  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The device  1105  may be an example of aspects of a device  1005 , or a base station  105  as described herein. The device  1105  may include a receiver  1110 , a communications manager  1115 , and a transmitter  1135 . The device  1105  may also include a processor. Each of these components may be in communication with one another (e.g., via one or more buses). 
     The receiver  1110  may receive information such as packets, user data, or control information associated with various information channels (e.g., control channels, data channels, and information related to neural network configuration for wireless communication system assistance, etc.). Information may be passed on to other components of the device  1105 . The receiver  1110  may be an example of aspects of the transceiver  1320  described with reference to  FIG. 13 . The receiver  1110  may utilize a single antenna or a set of antennas. 
     The communications manager  1115  may be an example of aspects of the communications manager  1015  as described herein. The communications manager  1115  may include a capability information manager  1120 , a neural network block parameter manager  1125 , and an acknowledgement manager  1130 . The communications manager  1115  may be an example of aspects of the communications manager  1310  described herein. 
     The capability information manager  1120  may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. 
     The neural network block parameter manager  1125  may configure one or more neural network block parameters for a neural network block of the one or more neural network blocks supported by the UE based on the capability information and transmit, to the UE, the one or more neural network block parameters. 
     The acknowledgement manager  1130  may receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. 
     The transmitter  1135  may transmit signals generated by other components of the device  1105 . In some examples, the transmitter  1135  may be collocated with a receiver  1110  in a transceiver module. For example, the transmitter  1135  may be an example of aspects of the transceiver  1320  described with reference to  FIG. 13 . The transmitter  1135  may utilize a single antenna or a set of antennas. 
       FIG. 12  shows a block diagram  1200  of a communications manager  1205  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The communications manager  1205  may be an example of aspects of a communications manager  1015 , a communications manager  1115 , or a communications manager  1310  described herein. The communications manager  1205  may include a capability information manager  1210 , a neural network block parameter manager  1215 , an acknowledgement manager  1220 , a resource allocation manager  1225 , an adjustment parameter manager  1230 , a configuration information manager  1235 , a neural network block manager  1240 , a timing manager  1245 , and a feedback information manager  1250 . Each of these modules may communicate, directly or indirectly, with one another (e.g., via one or more buses). 
     The capability information manager  1210  may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. In some examples, the capability information manager  1210  may transmit control information to the UE that indicates a new network function block to be the default network function block. In some examples, the capability information manager  1210  may transmit, to the UE, second capability information indicating that the base station supports at least one neural network block, where receiving the capability information from the UE is based on transmitting the second capability information. In some cases, one or more neural network block types supported by the UE includes the default network function block. In some cases, the second capability information included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     The neural network block parameter manager  1215  may configure one or more neural network block parameters for a neural network block of the one or more neural network blocks supported by the UE based on the capability information. In some examples, the neural network block parameter manager  1215  may transmit, to the UE, the one or more neural network block parameters. In some examples, the neural network block parameter manager  1215  may transmit an indication of the neural network block of a set of neural network blocks that have been stored in memory of the UE but not active, thereby enabling the UE to recall the indicated neural network block from memory based on the received indication. In this manner, a base station may configure neural network blocks in a UE by transmitting just an indication or identifier of a selected neural network block, enabling the UE to implement the indicated neural network block by recalling the required information from memory. In some embodiments, neural network blocks stored in memory of the UE may be included in the list of one or more neural network blocks supported by the UE that the UE transmits to the base station. In some examples, the neural network block parameter manager  1215  may transmit a downlink message to the base station that includes the one or more neural network block parameters as part of the downlink message. 
     In some examples, the neural network block parameter manager  1215  may transmit, to the UE, one or more additional neural network block parameters. In some cases, the one or more neural network block parameters further include one or more adjustment parameters to the neural network block to be used to process one or more signals, e.g., baseband signals, by the UE. In some cases, the one or more neural network block parameters are transmitted as part of a downlink data message. In some cases, the downlink message includes a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. In some cases, the one or more neural network block parameters include one or more input values, a number of layers of the neural network block, a number of nodes for one or more layers of the neural network block, a connection map across the one or more layers of the neural network block, one or more activation functions for one or more nodes of the neural network block, one or more weight values for the one or more nodes of the neural network block, or one or more bias values for the one or more nodes of the neural network block, or a combination thereof. 
     The acknowledgement manager  1220  may receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. In some examples, the acknowledgement manager  1220  may transmit, to the UE, an acknowledgement message based on the receiving of the request message. In some examples, the acknowledgement manager  1220  may receive, from the UE, a negative acknowledgement message indicating that the one or more neural network block parameters failed to be successfully decoded. In some examples, the acknowledgement manager  1220  may receive the negative acknowledgement message based on a priority status of the one or more additional neural network block parameters. 
     The resource allocation manager  1225  may transmit a resource allocation message for a physical downlink shared channel, where the one or more neural network block parameters are transmitted over the physical downlink shared channel according to the resource allocation message. In some examples, the resource allocation manager  1225  may transmit, to the UE, a resource allocation message for a physical downlink shared channel In some cases, the resource allocation message includes a downlink control information message or radio resource control message. In some cases, the resource allocation message includes a radio resource control message, or a downlink control information message. 
     The adjustment parameter manager  1230  may receive or transmit one or more adjustment parameters. In some cases, the one or more adjustment parameters include an activation indication for one or more nodes of the neural network block, a deactivation indication for the one or more nodes of the neural network block, a weight value for the one or more nodes of the neural network block, or a bias value for the one or more nodes of the neural network block, or a combination thereof. In some cases, the one or more adjustment parameters to the neural network block include a first adjustment to a first weight value for a first node of the neural network block or a second adjustment to a second weight value for a second node of the neural network block, or both. 
     The configuration information manager  1235  may transmit configuration information for the neural network block and the one or more neural network block parameters over the physical downlink shared channel based on transmitting the resource allocation message, where the configuration information includes instructions for the UE to configure the neural network block in place of a default network function block of the UE. In some cases, the configuration information includes an indication of one or more added algorithms for the neural network block, one or more released algorithms from the default network function block, or a combination thereof. 
     The neural network block manager  1240  may receive, from the UE, a request message to use a second neural network block different than the neural network block. In some cases, the neural network block may be configured to perform channel estimation for one or more signals, e.g., baseband signals, channel state information compression for the one or more signals, or a combination thereof. 
     The timing manager  1245  may transmit an indication of a timer and an instruction for the UE to initiate the timer upon receiving the one or more neural network block parameters. In some examples, the timing manager  1245  may transmit an indication of a counter of symbols, a counter of slots, or a combination thereof and an instruction for the UE to initiate the counter of symbols, the counter of slots, or the combination thereof upon receiving the one or more neural network block parameters. 
     The feedback information manager  1250  may transmit, to the UE, a request for feedback information about a performance of the neural network block. In some examples, the feedback information manager  1250  may receive, based on the request for the feedback information about the performance of the neural network block, a report including the feedback information about the performance of the neural network block. In some cases, the request is communicated using a DCI message, a MAC-CE message, or an RRC message. In some cases, the feedback information is communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel In some cases, the feedback information includes processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
       FIG. 13  shows a diagram of a system  1300  including a device  1305  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The device  1305  may be an example of or include the components of device  1005 , device  1105 , or a base station  105  as described herein. The device  1305  may include components for bi-directional voice and data communications including components for transmitting and receiving communications, including a communications manager  1310 , a network communications manager  1315 , a transceiver  1320 , an antenna  1325 , memory  1330 , a processor  1340 , and an inter-station communications manager  1345 . These components may be in electronic communication via one or more buses (e.g., bus  1350 ). 
     The communications manager  1310  may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks, configure one or more neural network block parameters for a neural network block of the one or more neural network blocks supported by the UE based on the capability information, transmit, to the UE, the one or more neural network block parameters, and receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. 
     The network communications manager  1315  may manage communications with the core network (e.g., via one or more wired backhaul links). For example, the network communications manager  1315  may manage the transfer of data communications for client devices, such as one or more UEs  115 . 
     The transceiver  1320  may communicate bi-directionally, via one or more antennas, wired, or wireless links as described above. For example, the transceiver  1320  may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver  1320  may also include a modem to modulate the packets and provide the modulated packets to the antennas for transmission, and to demodulate packets received from the antennas. 
     In some cases, the wireless device may include a single antenna  1325 . However, in some cases the device may have more than one antenna  1325 , which may be capable of concurrently transmitting or receiving multiple wireless transmissions. 
     The memory  1330  may include RAM, ROM, or a combination thereof. The memory  1330  may store computer-readable code  1335  including instructions that, when executed by a processor (e.g., the processor  1340 ) cause the device to perform various functions described herein. In some cases, the memory  1330  may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices. 
     The processor  1340  may include an intelligent hardware device, (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor  1340  may be configured to operate a memory array using a memory controller. In some cases, a memory controller may be integrated into processor  1340 . The processor  1340  may be configured to execute computer-readable instructions stored in a memory (e.g., the memory  1330 ) to cause the device  1305  to perform various functions (e.g., functions or tasks supporting neural network configuration for wireless communication system assistance). 
     The inter-station communications manager  1345  may manage communications with other base station  105 , and may include a controller or scheduler for controlling communications with UEs  115  in cooperation with other base stations  105 . For example, the inter-station communications manager  1345  may coordinate scheduling for transmissions to UEs  115  for various interference mitigation techniques such as beamforming or joint transmission. In some examples, the inter-station communications manager  1345  may provide an X2 interface within an LTE/LTE-A wireless communication network technology to provide communication between base stations  105 . 
     The code  1335  may include instructions to implement aspects of the present disclosure, including instructions to support wireless communications. The code  1335  may be stored in a non-transitory computer-readable medium such as system memory or other type of memory. In some cases, the code  1335  may not be directly executable by the processor  1340  but may cause a computer (e.g., when compiled and executed) to perform functions described herein. 
       FIG. 14  shows a process flow diagram illustrating a method  1400  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  1400  may be implemented by a UE  115  or its components as described herein. For example, the operations of method  1400  may be performed by a communications manager as described with reference to  FIGS. 6 through 9 . In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the functions described below. Additionally, or alternatively, a UE may perform aspects of the functions described below using special-purpose hardware. 
     In block  1405 , the UE may transmit, to a base station, capability information regarding one or more neural network blocks capable of being implemented by the UE. The operations of block  1405  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1405  may be performed by a capability information manager as described with reference to  FIGS. 7 through 8 . 
     In block  1410 , the UE may receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information. The operations of block  1410  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1410  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     In block  1415 , the UE may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the one or more neural network block parameters. The operations of block  1415  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1415  may be performed by a neural network block manager as described with reference to  FIGS. 7 through 8 . 
     In block  1420 , the UE may transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. The operations of block  1420  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1420  may be performed by an acknowledgement manager as described with reference to  FIGS. 7 through 8 . 
       FIG. 15  shows a process flow diagram illustrating a method  1500  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  1500  may be implemented by a UE  115  or its components as described herein. For example, the operations of method  1500  may be performed by a communications manager as described with reference to  FIGS. 6 through 9 . In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the functions described below. Additionally, or alternatively, a UE may perform aspects of the functions described below using special-purpose hardware. 
     In block  1505 , the UE may transmit, to a base station, capability information regarding one or more neural network blocks capable of being implemented by the UE. The operations of block  1505  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1505  may be performed by a capability information manager as described with reference to  FIGS. 7 through 8 . 
     In block  1510 , the UE may receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information, wherein the one or more neural network block parameters may comprise one or more adjustment parameters to the first neural network block. The operations of block  1510  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1510  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     In block  1515 , the UE may adjust the neural network block according to the one or more adjustment parameters, where processing the one or more signals, e.g., baseband signals, using the neural network block is based on the adjusting. The operations of block  1515  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1515  may be performed by an adjustment parameter manager as described with reference to  FIGS. 7 through 8 . 
     In block  1520 , the UE may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks based on the adjusting and the one or more neural network block parameters. The operations of block  1520  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1520  may be performed by a neural network block manager as described with reference to  FIGS. 7 through 8 . 
     In block  1525 , the UE may transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. The operations of block  1525  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1525  may be performed by an acknowledgement manager as described with reference to  FIGS. 7 through 8 . 
       FIG. 16  shows a process flow diagram illustrating a method  1600  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  1600  may be implemented by a UE  115  or its components as described herein. For example, the operations of method  1600  may be performed by a communications manager as described with reference to  FIGS. 6 through 9 . In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the functions described below. Additionally, or alternatively, a UE may perform aspects of the functions described below using special-purpose hardware. 
     In block  1605 , the UE may transmit, to a base station, capability information regarding one or more neural network blocks capable of being implemented by the UE. The operations of block  1605  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1605  may be performed by a capability information manager as described with reference to  FIGS. 7 through 8 . 
     In block  1610 , the UE may receive, from the base station, a resource allocation message for a physical downlink shared channel. The operations of block  1610  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1610  may be performed by a resource allocation manager as described with reference to  FIG. 9 s   .  8 . 
     In block  1615 , the UE may receive, from the base station, one or more neural network block parameters based on the transmitting of the capability information. The operations of block  1615  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1615  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     In block  1620 , the UE may receive configuration information for the neural network block and the one or more neural network block parameters over the physical downlink shared channel based on receiving the resource allocation message. The operations of block  1620  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1620  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     In block  1625 , the UE may configure the neural network block in place of a default network function block of the UE based on receiving the configuration information and the one or more neural network block parameters, where processing the one or more signals, e.g., baseband signals, using the neural network block is based on the configuring. The operations of block  1625  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1625  may be performed by a configuration information manager as described with reference to  FIG. 8 . 
     In block  1630 , the UE may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the configuration information. The operations of block  1630  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1630  may be performed by a neural network block manager as described with reference to  FIGS. 7 through 8 . 
     In block  1635 , the UE may transmit, to the base station, an acknowledgement message indicating that the one or more neural network block parameters have been successfully received. The operations of block  1635  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1635  may be performed by an acknowledgement manager as described with reference to  FIGS. 7 through 8 . 
       FIG. 17  shows a process flow diagram illustrating a method  1700  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  1700  may be implemented by a base station  105  or its components as described herein. For example, the operations of method  1700  may be performed by a communications manager as described with reference to  FIGS. 10 through 13 . In some examples, a base station may execute a set of instructions to control the functional elements of the base station to perform the functions described below. Additionally, or alternatively, a base station may perform aspects of the functions described below using special-purpose hardware. 
     In block  1705 , the base station may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. The operations of block  1705  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1705  may be performed by a capability information manager as described with reference to  FIGS. 11 through 12 . 
     In block  1710 , the base station may configure one or more neural network block parameters for a neural network block of the one or more neural network blocks capable of being implemented by the UE based on the capability information. The operations of block  1710  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1710  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In block  1715 , the base station may transmit, to the UE, the one or more neural network block parameters. The operations of block  1715  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1715  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In block  1720 , the base station may receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. The operations of block  1720  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1720  may be performed by an acknowledgement manager as described with reference to  FIGS. 11 through 12 . 
       FIG. 18  shows a process flow diagram illustrating a method  1800  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  1800  may be implemented by a base station  105  or its components as described herein. For example, the operations of method  1800  may be performed by a communications manager as described with reference to  FIGS. 10 through 13 . In some examples, a base station may execute a set of instructions to control the functional elements of the base station to perform the functions described below. Additionally, or alternatively, a base station may perform aspects of the functions described below using special-purpose hardware. 
     In block  1805 , the base station may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. The operations of block  1805  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1805  may be performed by a capability information manager as described with reference to  FIGS. 11 through 12 . 
     In block  1810 , the base station may configure one or more neural network block parameters for a neural network block of the one or more neural network blocks capable of being implemented by the UE based on the capability information. The one or more neural network block parameters may include one or more adjustment parameters to the neural network block to be used to process one or more signals, e.g., baseband signals, by the UE. The operations of block  1810  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1810  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In block  1815 , the base station may transmit, to the UE, the one or more neural network block parameters. The operations of block  1815  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1815  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In block  1820 , the base station may receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. The operations of block  1820  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1820  may be performed by an acknowledgement manager as described with reference to  FIGS. 11 through 12 . 
       FIG. 19  shows a process flow diagram illustrating a method  1900  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  1900  may be implemented by a base station  105  or its components as described herein. For example, the operations of method  1900  may be performed by a communications manager as described with reference to  FIGS. 10 through 13 . In some examples, a base station may execute a set of instructions to control the functional elements of the base station to perform the functions described below. Additionally, or alternatively, a base station may perform aspects of the functions described below using special-purpose hardware. 
     In block  1905 , the base station may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. The operations of block  1905  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1905  may be performed by a capability information manager as described with reference to  FIGS. 11 through 12 . 
     In block  1910 , the base station may configure one or more neural network block parameters for a neural network block of the one or more neural network blocks capable of being implemented by the UE based on the capability information. The operations of block  1910  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1910  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In block  1915 , the base station may transmit, to the UE, a resource allocation message for a physical downlink shared channel. The operations of block  1915  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1915  may be performed by a resource allocation manager as described with reference to  FIG. 12 . 
     In block  1920 , the base station may transmit at least one of configuration information for the neural network block and the one or more neural network block parameters over the physical downlink shared channel based on transmitting the resource allocation message, where the configuration information includes instructions for the UE to configure the neural network block in place of a default network function block of the UE. The operations of block  1920  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1920  may be performed by a configuration information manager as described with reference to  FIG. 12 . 
     In block  1925 , the base station may receive, from the UE, an acknowledgment message indicating that the one or more neural network block parameters have been successfully received by the UE. The operations of block  1925  may be performed according to the methods described herein. In some examples, aspects of the operations of block  1925  may be performed by an acknowledgement manager as described with reference to  FIGS. 11 through 12 . 
       FIG. 20  shows a process flow diagram illustrating a method  2000  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of the method  2000  may be implemented by a UE  115  or its components as described herein. For example, the operations of the method  2000  may be performed by a communications manager as described with reference to  FIGS. 6 through 9 . In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the functions described below. Additionally or alternatively, a UE may perform aspects of the functions described below using special-purpose hardware. 
     In block  2005 , the UE may transmit, to a base station, capability information regarding one or more neural network blocks capable of being implemented by the UE. The operations of block  2005  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2005  may be performed by a capability information manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may identify the default network function block, wherein the one or more neural network blocks supported by the UE includes the default network function block. 
     In block  2010 , the UE may receive, from the base station, control signaling with one or more neural network block parameters based on the capability information transmitted by the UE in the operations of block  2005 . The operations of block  2010  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2010  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the one or more neural network block parameters may further include one or more adjustment parameters to the first neural network block used to process the one or more signals, e.g., baseband signals, by the UE. The method may further include adjusting the first neural network block according to the one or more adjustment parameters, wherein processing the one or more signals using the first neural network block is based at least in part on the adjusting. The one or more adjustment parameters may include an activation indication for one or more nodes of the first neural network block, a deactivation indication of the one or more nodes of the first neural network block, a weight value for the one or more nodes of the first neural network block, an adjustment to a weight value for a submodule of the first neural network block, or a bias value for the one or more nodes of the first neural network block, or any such combination. 
     In some examples, adjusting the first neural network block according to the one or more adjustment parameters may further include performing a first operation on the one or more signals, e.g., baseband signals, using a first node of the first neural network block based at least in part on a first weight value indicated by the one or more adjustment parameters, and performing a second operation on the one or more signals, e.g., baseband signals, using a second node of the first neural network block based at least in part on a second weight value indicated by the one or more adjustment parameters, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on performing the first operation and the second operation. Adjusting the first neural network block may further include identifying the first neural network block having one or more configuration options, wherein the one or more neural network blocks supported by the UE includes the first neural network block. 
     In some examples, the first neural network block may be configured to perform channel estimation for the one or more signals, e.g., baseband signals, channel state information compression for the one or more signals, or a combination thereof. 
     According to some examples, the one or more neural network block parameters may include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     In some examples, the capability information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel, or a combination thereof. 
     According to some examples, the control signaling may include a resource allocation message including the one or more neural network block parameters for configuring network components. The resource allocation message may include a downlink control information message, a media access control element, or radio resource control message. The network components may include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or any such combination. In some examples, the one or more cell groups can include a master cell group, a secondary cell group, a supplementary cell group, or any such combination. 
     In some examples, receiving control signaling from the base station may include receiving the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are received over the physical downlink control channel, and configuring the first neural network block in place of a default network function block of the UE based at least in part on receiving the one or more neural network block parameters, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on the configuring. In some examples, receiving control signaling from the base station may include receiving configuration information over a physical downlink control channel, wherein the configuration information includes an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. Configuration information received from the base station may indicate a new network function block to be the default network function block, wherein identifying the default network function block is based at least in part on receiving the control signaling. 
     In some examples, the UE may monitor for the configuration information based at least in part on receiving the resource allocation message, wherein receiving the configuration information is based at least in part on the monitoring. 
     In some examples, receiving the control signaling may further include identifying a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks, receiving an indication of the first neural network block of the set of neural network blocks, and identifying the first neural network block of the set of neural network blocks based at least in part on receiving the indication of the first neural network block, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on identifying the first neural network block. 
     According to some examples, the UE may receive a downlink message that includes the one or more neural network block parameters, wherein receiving the one or more neural network block parameters is based at least in part on receiving the downlink message. The downlink message may include a downlink control information message, a media access control element, a radio resource control message, or a combination thereof. 
     In some examples, upon receiving the one or more neural network block parameters, the UE may initiate a counter of symbols, a counter of slots, or a combination thereof, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on the counter of symbols, the counter of slots, or the combination thereof satisfying a threshold. Upon receiving the one or more neural network block parameters, the UE may initiate a timer, wherein processing the one or more signals using the first neural network block is based at least in part on an expiration of the timer. 
     According to some examples, the UE may receive, from the base station, additional control signaling including one or more additional neural network block parameters. The UE may transmit, to the base station, a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. The UE may determine a priority status of the one or more additional neural network block parameters, wherein transmitting the negative acknowledgement message is based at least in part on the priority status. 
     In some examples, the UE may receive, from the base station, second capability information indicating that the base station supports at least one neural network block, wherein transmitting the capability information to the base station is based at least in part on receiving the second capability information. The second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     In block  2015 , the UE may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the one or more neural network block parameters. The operations of block  2015  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2015  may be performed by a neural network block manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may transmit a request message to use a second neural network block different than the first neural network block to process the one or more signals, e.g., baseband signals, using the second neural network block based at least in part on transmitting the request message. The UE may receive, from the base station, an acknowledgment message based at least in part on the transmitting of the request message, wherein processing the one or more signals using the second neural network block is based at least in part on receiving the acknowledgement message. 
     According to some examples, the UE may initiate a timer, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on an expiration of the timer. The UE may further determine that the timer has expired, and may process, based at least in part on determining that the timer has expired, the one or more signals using a default neural network block different than the first neural network block, wherein the first neural network block is an active neural network block. 
     According to some examples, the UE may receive, from the base station, a request for feedback information about a performance of the first neural network block; and may transmit, based at least in part on processing the one or more signals, e.g., baseband signals, using the first neural network block and the request, a report comprising the feedback information about the performance of the first neural network block to the base station. The request may be communicated using a DCI message, a MAC-CE message, or an RRC message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel In some examples, the feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In block  2020 , the UE may transmit, to the base station, an acknowledgement message indicating that the control signaling has been successfully received. The operations of block  2020  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2020  may be performed by an acknowledgement manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may transmit a configuration of a configured first neural network block to the base station over a physical uplink control channel or a physical uplink shared channel, where the configured first neural network block is the result of adjusting parameters of the first neural network block. 
       FIG. 21  shows a process flow diagram illustrating a method  2100  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  2100  may be implemented by a UE  115  or its components as described herein. For example, the operations of method  2100  may be performed by a communications manager as described with reference to  FIGS. 6 through 9 . In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the functions described below. Additionally, or alternatively, a UE may perform aspects of the functions described below using special-purpose hardware. 
     In block  2105 , the UE may transmit, to a base station, capability information regarding one or more neural network blocks capable of being implemented by the UE. The operations of block  2105  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2105  may be performed by a capability information manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may identify the default network function block, wherein the one or more neural network blocks supported by the UE includes the default network function block. 
     In block  2110 , the UE may receive, from the base station, control signaling with one or more neural network block parameters based on the capability information transmitted by the UE in operations of block  2105 . The operations of block  2110  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2110  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     According to some examples, the control signaling may include a resource allocation message including the one or more neural network block parameters for configuring network components. The resource allocation message may include a downlink control information message, a media access control element, or radio resource control message. The network components may include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or any such combination. In some examples, the one or more cell groups can include a master cell group, a secondary cell group, a supplementary cell group, or any such combination. 
     In some examples, receiving control signaling from the base station may include receiving the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are received over the physical downlink control channel, and configuring the first neural network block (e.g., active neural network block to process baseband signals) in place of a default network function block of the UE based at least in part on receiving the one or more neural network block parameters, wherein processing one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on the configuring. In some examples, receiving control signaling from the base station may include receiving configuration information over a physical downlink control channel, wherein the configuration information includes an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. Configuration information received from the base station may indicate a new network function block to be the default network function block, wherein identifying the default network function block is based at least in part on receiving the control signaling. 
     In some examples, the UE may monitor for the configuration information based at least in part on receiving the resource allocation message, wherein receiving the configuration information is based at least in part on the monitoring. 
     In some examples, receiving the control signaling may further include identifying a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks, receiving an indication of the first neural network block of the set of neural network blocks, and identifying the first neural network block of the set of neural network blocks based at least in part on receiving the indication of the first neural network block, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on identifying the first neural network block. 
     According to some examples, the UE may receive a downlink message that includes the one or more neural network block parameters, wherein receiving the one or more neural network block parameters is based at least in part on receiving the downlink message. The downlink message may include a downlink control information message, a media access control element, a radio resource control message, or a combination thereof. 
     In some examples, upon receiving the one or more neural network block parameters, the UE may initiate a counter of symbols, a counter of slots, or a combination thereof, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on the counter of symbols, the counter of slots, or the combination thereof satisfying a threshold. Upon receiving the one or more neural network block parameters, the UE may initiate a timer, wherein processing the one or more signals using the first neural network block is based at least in part on an expiration of the timer. 
     According to some examples, the UE may receive, from the base station, additional control signaling including one or more additional neural network block parameters. The UE may transmit, to the base station, a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. The UE may determine a priority status of the one or more additional neural network block parameters, wherein transmitting the negative acknowledgement message is based at least in part on the priority status. 
     In some examples, the UE may receive, from the base station, second capability information indicating that the base station supports at least one neural network block, wherein transmitting the capability information to the base station is based at least in part on receiving the second capability information. The second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     In block  2115 , the UE may adjust the neural network block according to the one or more adjustment parameters, and process the one or more signals, e.g., baseband signals, using the adjusted neural network block. The one or more adjustment parameters may include an activation indication for one or more nodes of the first neural network block, a deactivation indication of the one or more nodes of the first neural network block, a weight value for the one or more nodes of the first neural network block, an adjustment to a weight value for a submodule of the first neural network block, or a bias value for the one or more nodes of the first neural network block, or any such combination. The operations of block  2115  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2115  may be performed by an adjustment parameter manager as described with reference to  FIG. 8 . 
     In some examples, adjusting the first neural network block according to the one or more adjustment parameters may further include performing a first operation on the one or more signals, e.g., baseband signals, using a first node of the first neural network block based at least in part on a first weight value indicated by the one or more adjustment parameters, and performing a second operation on the one or more signals, e.g., baseband signals, using a second node of the first neural network block based at least in part on a second weight value indicated by the one or more adjustment parameters, wherein processing the one or more signals using the first neural network block is based at least in part on performing the first operation and the second operation. Adjusting the first neural network block may further include identifying the first neural network block having one or more configuration options, wherein the one or more neural network blocks supported by the UE includes the first neural network block. 
     In some examples, the first neural network block may be configured to perform channel estimation for the one or more baseband signals, channel state information compression for the one or more baseband signals, or a combination thereof. 
     According to some examples, the one or more neural network block parameters may include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     In some examples, the capability information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel, or a combination thereof. 
     In block  2120 , the UE may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks based on the adjustments and the one or more neural network block parameters. The operations of block  2120  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2120  may be performed by a neural network block manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may transmit a request message to use a second neural network block (e.g., additional active neural network block) different than the first neural network block to process the one or more signals, e.g., baseband signals, using the second neural network block based at least in part on transmitting the request message. The UE may receive, from the base station, an acknowledgment message based at least in part on the transmitting of the request message, wherein processing the one or more signals using the second neural network block is based at least in part on receiving the acknowledgement message. 
     According to some examples, the UE may initiate a timer, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on an expiration of the timer. The UE may further determine that the timer has expired, and may process, based at least in part on determining that the timer has expired, the one or more signals, e.g., baseband signals, using a default neural network block different than the first neural network block 
     According to some examples, the UE may receive, from the base station, a request for feedback information about a performance of the first neural network block; and may transmit, based at least in part on processing the one or more signals, e.g., baseband signals, using the first neural network block and the request, a report comprising the feedback information about the performance of the first neural network block to the base station. The request may be communicated using a DCI message, a MAC-CE message, or an RRC message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel In some examples, the feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In block  2125 , the UE may transmit, to the base station, an acknowledgement message indicating that the control signaling has been successfully received. The operations of block  2125  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2125  may be performed by an acknowledgement manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may transmit a configuration of a configured first neural network block to the base station over a physical uplink control channel or a physical uplink shared channel, where the configured first neural network block is the result of adjusting parameters of the first neural network block. 
       FIG. 22  shows a process flow diagram illustrating a method  2200  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  2200  may be implemented by a UE  115  or its components as described herein. For example, the operations of method  2200  may be performed by a communications manager as described with reference to  FIGS. 6 through 9 . In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the functions described below. Additionally, or alternatively, a UE may perform aspects of the functions described below using special-purpose hardware. 
     In block  2205 , the UE may transmit, to a base station, capability information regarding one or more neural network blocks capable of being implemented by the UE. The operations of block  2205  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2205  may be performed by a capability information manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may identify the default network function block, wherein the one or more neural network blocks supported by the UE includes the default network function block. 
     In some examples, the capability information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel, or a combination thereof. 
     In block  2210 , the UE may receive, from the base station, a resource allocation message over a physical downlink control channel. The operations of block  2210  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2210  may be performed by a resource allocation manager as described with reference to  FIG. 8 . The resource allocation message may include a downlink control information message, a media access control element, or radio resource control message. 
     In block  2215 , the UE may receive, from the base station, one or more neural network block parameters included in the resource allocation message based on the capability information transmitted in the operations of block  2205 . The operations of block  2215  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2215  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     According to some examples, the UE may receive a downlink message that includes the one or more neural network block parameters, wherein receiving the one or more neural network block parameters is based at least in part on receiving the downlink message. The downlink message may include a downlink control information message, a media access control element, a radio resource control message, or a combination thereof. 
     In some examples, upon receiving the one or more neural network block parameters, the UE may initiate a counter of symbols, a counter of slots, or a combination thereof, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on the counter of symbols, the counter of slots, or the combination thereof satisfying a threshold. Upon receiving the one or more neural network block parameters, the UE may initiate a timer, wherein processing the one or more signals using the first neural network block is based at least in part on an expiration of the timer. 
     According to some examples, the UE may receive, from the base station, additional control signaling including one or more additional neural network block parameters. The UE may transmit, to the base station, a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. The UE may determine a priority status of the one or more additional neural network block parameters, wherein transmitting the negative acknowledgement message is based at least in part on the priority status. 
     In some examples, the UE may receive, from the base station, second capability information indicating that the base station supports at least one neural network block, wherein transmitting the capability information to the base station is based at least in part on receiving the second capability information. The second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     According to some examples, the one or more neural network block parameters may include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     In block  2220 , the UE may receive configuration information for the neural network block and the one or more neural network block parameters over the physical downlink control channel based on receiving the resource allocation message. The operations of block  2220  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2220  may be performed by a neural network block parameter manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the configuration information may include an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. Configuration information received from the base station may indicate a new network function block to be the default network function block, wherein identifying the default network function block is based at least in part on receiving the control signaling. In some examples, the UE may monitor for the configuration information based at least in part on receiving the resource allocation message, wherein receiving the configuration information is based at least in part on the monitoring. 
     In some examples, receiving the configuration information may further include identifying a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks, receiving an indication of the first neural network block (e.g., active neural network block to process baseband signals) of the set of neural network blocks, and identifying the first neural network block of the set of neural network blocks based at least in part on receiving the indication of the first neural network block, wherein processing the one or more signals using the first neural network block is based at least in part on identifying the first neural network block. 
     In block  2225 , the UE may configure the neural network block in place of a default network function block of the UE based upon receiving the configuration information and the one or more neural network block parameters, wherein processing the one or more signals is performed using the configured neural network block. The operations of block  2225  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2225  may be performed by a configuration information manager as described with reference to  FIG. 8 . 
     In block  2230 , the UE may process one or more signals, e.g., baseband signals, generated by the UE using a neural network block of the one or more neural network blocks and the configuration information. In some examples, the first neural network block may be configured to perform channel estimation for the one or more baseband signals, channel state information compression for the one or more baseband signals, or a combination thereof. The operations of block  2230  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2230  may be performed by a neural network block manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may transmit a request message to use a second neural network block different than the first neural network block to process the one or more signals, e.g., baseband signals, using the second neural network block based at least in part on transmitting the request message. The UE may receive, from the base station, an acknowledgment message based at least in part on the transmitting of the request message, wherein processing the one or more signals using the second neural network block is based at least in part on receiving the acknowledgement message. 
     According to some examples, the UE may initiate a timer, wherein processing the one or more signals, e.g., baseband signals, using the first neural network block is based at least in part on an expiration of the timer. The UE may further determine that the timer has expired, and may process, based at least in part on determining that the timer has expired, the one or more signals, e.g., baseband signals, using a default neural network block different than the first neural network block. 
     According to some examples, the UE may receive, from the base station, a request for feedback information about a performance of the first neural network block; and may transmit, based at least in part on processing the one or more signals, e.g., baseband signals, using the first neural network block and the request, a report comprising the feedback information about the performance of the first neural network block to the base station. The request may be communicated using a DCI message, MAC-CE message, or an RRC message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel In some examples, the feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In block  2235 , the UE may transmit, to the base station, an acknowledgement message indicating that the control signaling has been successfully received. The operations of block  2235  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2235  may be performed by an acknowledgement manager as described with reference to  FIGS. 7 through 8 . 
     In some examples, the UE may transmit a configuration of a configured first neural network block to the base station over a physical uplink control channel or a physical uplink shared channel, where the configured first neural network block is the result of adjusting parameters of the first neural network block. 
       FIG. 23  shows a process flow diagram illustrating a method  2300  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  2300  may be implemented by a base station  105  or its components as described herein. For example, the operations of method  2300  may be performed by a communications manager as described with reference to  FIGS. 10 through 13 . In some examples, a base station may execute a set of instructions to control the functional elements of the base station to perform the functions described below. Additionally, or alternatively, a base station may perform aspects of the functions described below using special-purpose hardware. 
     In block  2305 , the base station may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. The operations of block  2305  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2305  may be performed by a capability information manager as described with reference to  FIGS. 11 through 12 . 
     According to some examples, the capability information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel, or a combination thereof. 
     In some examples, the base station may transmit, to the UE, second capability information indicating that the base station supports at least one neural network block, wherein receiving the capability information from the UE is based at least in part on transmitting the second capability information. The second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     In block  2310 , the base station may configure, based on the received UE capability, control signaling with one or more neural network block parameters for a neural network block of the one or more neural network blocks. The operations of block  2310  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2310  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In some examples, the one or more neural network block parameters further include one or more adjustment parameters to the first neural network block used to process one or more signals, e.g., baseband signals, by the UE. The control signaling may include a resource allocation message including the one or more neural network block parameters for configuring network components. The resource allocation message may include a downlink control information message, a media access control element, or radio resource control message. Network components may include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or a combination thereof. In some examples, the one or more cell groups may include a master cell group, a secondary cell group, a supplementary cell group, or a combination thereof. 
     According to some examples, the one or more adjustment parameters may include an activation indication for one or more nodes of a first submodule of the first neural network block, a deactivation indication for the one or more nodes of the first submodule of the first neural network block, a weight value for the one or more nodes of the first submodule of the first neural network block, or a bias value for the one or more nodes of the first submodule of the first neural network block, or a combination thereof. The one or more adjustment parameters to the first neural network block may include a first adjustment to a first weight value for a first node of the first neural network block or a second adjustment to a second weight value for a second node of the first neural network block, or both. 
     According to some examples, the one or more neural network block parameters may include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     In some examples, transmitting the control signaling may further include transmitting configuration information for the first neural network block and the one or more neural network block parameters over a physical downlink control channel, wherein the configuration information includes instructions for the UE to configure the first neural network block in place of a default network function block of the UE. The configuration information may include an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. In some examples, the one or more neural network block types supported by the UE may include the default network function block. According to some examples, transmitting the control signaling may further include transmitting control information to the UE that indicates a new network function block to be the default network function block, wherein the control information is included in the control signaling. In some examples, transmitting the control signaling may further include transmitting an indication of the first neural network block of a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks. 
     In some examples, the base station may receive a request message from the UE to use a second neural network block different than the first neural network block, and may transmit an acknowledgement message to the UE based at least in part on the receiving of the request message. 
     According to some examples, the base station may transmit a downlink message that includes the one or more neural network block parameters, wherein the one or more neural network block parameters transmitted as part of the downlink message 
     In some examples, base station may transmit an indication of a timer and an instruction for the UE to initiate the timer upon receiving the one or more neural network block parameters. In some examples, the base station may transmit an indication of a counter of symbols, a counter of slots, or a combination thereof and an instruction for the UE to initiate the counter of symbols, the counter of slots, or the combination thereof upon receiving the one or more neural network block parameters. 
     In block  2315 , the base station may transmit, to the UE, the control signaling. The operations of block  2315  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2315  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In some examples, the base station may transmit, to the UE, one or more additional neural network block parameters, and may receive a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. Receiving the negative acknowledgement message may be based at least in part on a priority status of the one or more additional neural network block parameters. 
     According to some examples, the base station may transmit, to the UE, a request for feedback information about a performance of the first neural network block, and may receive, based at least in part on the request for the feedback information about the performance of the first neural network block, a report comprising the feedback information about the performance of the first neural network block. The request may be communicated using a DCI message, MAC-CE message, or an RRC message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel. The feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In some examples, transmitting the control signaling may further include transmitting, to the UE, the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are transmitted over the physical downlink control channel according to the control signaling. 
     In block  2320 , the base station may receive, from the UE, an acknowledgment message indicating that the control signaling has been successfully received by the UE. The operations of block  2320  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2320  may be performed by an acknowledgement manager as described with reference to  FIGS. 11 through 12 . 
     In some examples, the first neural network block may be configured to perform channel estimation for one or more signals, e.g., baseband signals, channel state information compression for the one or more signals, e.g., baseband signals, or a combination thereof. 
     According to some examples, the base station may receive a configuration of a configured first neural network block from the UE over a physical uplink control channel or a physical uplink shared channel, wherein the configured first neural network block has been configured by the UE based at least in part on the one or more neural network block parameters. 
       FIG. 24  shows a process flow diagram illustrating a method  2400  that supports neural network configuration for wireless communication system assistance in accordance with aspects of the present disclosure. The operations of method  2400  may be implemented by a base station  105  or its components as described herein. For example, the operations of method  2400  may be performed by a communications manager as described with reference to  FIGS. 10 through 13 . In some examples, a base station may execute a set of instructions to control the functional elements of the base station to perform the functions described below. Additionally, or alternatively, a base station may perform aspects of the functions described below using special-purpose hardware. 
     In block  2405 , the base station may receive, from a UE, capability information indicating that the UE supports one or more neural network blocks. The operations of block  2405  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2405  may be performed by a capability information manager as described with reference to  FIGS. 5, and 10 through 13 . 
     According to some examples, the capability information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel, or a combination thereof. 
     In some examples, the base station may transmit, to the UE, second capability information indicating that the base station supports at least one neural network block, wherein transmitting the capability information from the UE is based at least in part on receiving the second capability information. The second capability information may be included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     In block  2410 , the base station may configure, based on the received UE capability, control signaling with one or more neural network block parameters for a neural network block of the one or more neural network blocks. The operations of block  2410  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2410  may be performed by a neural network block parameter manager as described with reference to  FIGS. 11 through 12 . 
     In some examples, the one or more neural network block parameters further include one or more adjustment parameters to the first neural network block used to process one or more signals, e.g., baseband signals, by the UE. The control signaling may include a resource allocation message including the one or more neural network block parameters for configuring network components. The resource allocation message may include a downlink control information message, a media access control element, or radio resource control message. Network components may include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or a combination thereof. In some examples, the one or more cell groups may include a master cell group, a secondary cell group, a supplementary cell group, or a combination thereof. 
     According to some examples, the one or more adjustment parameters may include an activation indication for one or more nodes of the first submodule of the first neural network block, a deactivation indication for the one or more nodes of the first neural network block, a weight value for the one or more nodes of the first submodule of the first neural network block, or a bias value for the one or more nodes of the first submodule of the first neural network block, or a combination thereof. The one or more adjustment parameters to the first neural network block may include a first adjustment to a first weight value for a first node of the first neural network block or a second adjustment to a second weight value for a second node of the first neural network block, or both. 
     According to some examples, the one or more neural network block parameters may include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     According to some examples, transmitting the control signaling may further include transmitting control information to the UE that indicates a new network function block to be the default network function block, wherein the control information is included in the control signaling. In some examples, transmitting the control signaling may further include transmitting an indication of the first neural network block of a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks. 
     In block  2415 , the base station may transmit, to the UE, a resource allocation message included in the control signaling for a physical downlink control channel. The operations of block  2415  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2415  may be performed by a resource allocation manager as described with reference to  FIG. 12 . 
     In block  2420 , the base station may transmit configuration information for the neural network block and the one or more neural network block parameters over the physical downlink control channel based on transmitting the resource allocation message, wherein the configuration information includes instructions for the UE to configure the neural network block in place of a default network function block of the UE. 
     In some examples, the configuration information may include instructions for the UE to configure the first neural network block in place of a default network function block of the UE. The configuration information may include an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. In some examples, the one or more neural network block types supported by the UE may include the default network function block. 
     In some examples, the base station may receive a request message from the UE to use a second neural network block different than the first neural network block, and may transmit an acknowledgement message to the UE based at least in part on the receiving of the request message. 
     According to some examples, the base station may transmit a downlink message that includes the one or more neural network block parameters, wherein the one or more neural network block parameters transmitted as part of the downlink message. 
     In some examples, the base station may transmit an indication of a timer and an instruction for the UE to initiate the timer upon receiving the one or more neural network block parameters. In some examples, the base station may transmit an indication of a counter of symbols, a counter of slots, or a combination thereof and an instruction for the UE to initiate the counter of symbols, the counter of slots, or the combination thereof upon receiving the one or more neural network block parameters. 
     In some examples, the base station may transmit, to the UE, one or more additional neural network block parameters, and may receive a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. Receiving the negative acknowledgement message may be based at least in part on a priority status of the one or more additional neural network block parameters. 
     According to some examples, the base station may transmit, to the UE, a request for feedback information about a performance of the first neural network block, and may receive, based at least in part on the request for the feedback information about the performance of the first neural network block, a report comprising the feedback information about the performance of the first neural network block. The request may be communicated using a DCI message, MAC-CE message, or an RRC message, and the feedback information may be communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel. The feedback information may include processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     In some examples, transmitting the control signaling may further include transmitting, to the UE, the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are transmitted over the physical downlink control channel according to the control signaling. 
     In block  2425 , the base station may receive, from the UE, an acknowledgment message indicating that the control signaling has been successfully received by the UE. The operations of block  2425  may be performed according to the methods described herein. In some examples, aspects of the operations of block  2425  may be performed by an acknowledgement manager as described with reference to  FIGS. 11 through 12 . 
     In some examples, the first neural network block may be configured to perform channel estimation for one or more signals, e.g., baseband signals, channel state information compression for the one or more signals, e.g., baseband signals, or a combination thereof. 
     According to some examples, the base station may receive a configuration of a configured first neural network block from the UE over a physical uplink control channel or a physical uplink shared channel, wherein the configured first neural network block has been configured by the UE based at least in part on the one or more neural network block parameters. 
     Implementation examples are described in the following paragraphs. While some of the following implementation examples are described in terms of example methods, further example implementations may include: the example methods discussed in the following paragraphs implemented by a UE comprising a processor configured with processor-executable instructions to perform operations of the methods of the following implementation examples; the example methods discussed in the following paragraphs implemented by a UE comprising means for performing functions of the methods of the following implementation examples; the example methods discussed in the following paragraphs implemented in a computer program product comprising processor-executable instructions configured to cause a processor perform operations of the methods of the following implementation examples; and the example methods discussed in the following paragraphs may be implemented as a non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of a UE to perform the operations of the methods of the following implementation examples. 
     Example 1. A method for wireless communications performed by a processor of a user equipment (UE), including: transmitting, to a base station, capability information indicating one or more neural network blocks supported by the UE; receiving, from the base station, control signaling with one or more neural network block parameters based at least in part on the transmitting of the capability information; processing one or more signals generated by the UE using a first neural network block of the one or more neural network blocks and the one or more neural network block parameters. 
     Example 2. The method of example 1, wherein the one or more neural network block parameters further include one or more adjustment parameters to the first neural network block used to process the one or more signals, e.g., baseband signals, by the UE, the method further including: adjusting the first neural network block according to the one or more adjustment parameters, wherein processing the one or more signals, using the first neural network block is based at least in part on the adjusting. 
     Example 3. The method of either example 1 or 2, wherein the control signaling includes a resource allocation message including the one or more neural network block parameters for configuring network components. 
     Example 4. The method of example 3, wherein the network components include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or a combination thereof. 
     Example 5. The method of example 4, wherein the one or more cell groups include a master cell group, a secondary cell group, a supplementary cell group, or a combination thereof. 
     Example 6. The method of example 5, the resource allocation message includes a downlink control information message, a media access control element, or radio resource control message. 
     Example 7. The method of example 3, wherein the one or more adjustment parameters include an activation indication for one or more nodes of the first neural network block, a deactivation indication of the one or more nodes of the first neural network block, a weight value for the one or more nodes of the first neural network block, an adjustment to a weight value for a submodule, or a bias value for the one or more nodes of the first neural network block, or a combination thereof. 
     Example 8. The method of example 3, further including: performing a first operation on the one or more signals, e.g., baseband signals, using a first submodule of the first neural network block based at least in part on a first weight value indicated by the one or more adjustment parameters; and performing a second operation on the one or more signals, e.g., baseband signals, using a second submodule of the first neural network block based at least in part on a second weight value indicated by the one or more adjustment parameters, wherein processing the one or more signals using the first neural network block is based at least in part on performing the first operation and the second operation. 
     Example 9. The method of example 3, further including: identifying the first neural network block having one or more configuration options, wherein the one or more neural network blocks supported by the UE includes the first neural network block. 
     Example 10. The method of examples 1-9, wherein receiving control signaling may further include: receiving, from the base station, the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are received over the physical downlink control channel; and configuring the first neural network block in place of a default network function block of the UE based at least in part on receiving the one or more neural network block parameters, wherein processing the one or more signals using the first neural network block is based at least in part on the configuring. 
     Example 11. The method of example 10, further including: transmitting the configuration of a configured first neural network block to the base station over a physical uplink control channel or a physical uplink shared channel. 
     Example 12. The method of examples 1-11, wherein receiving the control signaling further includes: receiving configuration information over a physical downlink control channel, wherein the configuration information includes an indication of one or more added algorithms for the first neural network block, one or more released algorithms from a default network function block, or a combination thereof. 
     Example 13. The method of example 12, further including: identifying the default network function block, wherein the one or more neural network blocks supported by the UE includes the default network function block. 
     Example 14. The method of example 13, further including: receiving configuration information from the base station that indicates a new network function block to be the default network function block, wherein identifying the default network function block is based at least in part on receiving the control signaling. 
     Example 15. The method of example 12, further including: monitoring for the configuration information based at least in part on receiving the resource allocation message, wherein receiving the configuration information is based at least in part on the monitoring. 
     Example 16. The method of examples 1-15, further including: transmitting, by the UE, a request message to use a second neural network block different than the first neural network block; and processing one or more signals, e.g., baseband signals, using the second neural network block based at least in part on transmitting the request message. 
     Example 17. The method of example 16, further including: receiving, from the base station, an acknowledgment message based at least in part on the transmitting of the request message, wherein processing the one or more signals using the second neural network block is based at least in part on receiving the acknowledgement message. 
     Example 18. The method of examples 1-17, wherein receiving the control signaling with the one or more neural network block parameters further includes: identifying a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks; receiving an indication of the first neural network block of the set of neural network blocks; and identifying the first neural network block of the set of neural network blocks based at least in part on receiving the indication of the first neural network block, wherein processing the one or more signals using the first neural network block is based at least in part on identifying the first neural network block. 
     Example 19. The method of examples 1-18, further including: receiving a downlink message that includes the one or more neural network block parameters, wherein receiving the one or more neural network block parameters is based at least in part on receiving the downlink message. 
     Example 20. The method of example 19, wherein the downlink message includes a downlink control information message, a media access control element, a radio resource control message, or a combination thereof. 
     Example 21. The method of examples 1-20, further including: initiating, upon receiving the one or more neural network block parameters, a timer, wherein processing the one or more signals using the first neural network block is based at least in part on an expiration of the timer. 
     Example 22. The method of examples 1-21, further including: initiating, upon receiving the one or more neural network block parameters, a counter of symbols, a counter of slots, or a combination thereof, wherein processing the one or more signals using the first neural network block is based at least in part on the counter of symbols, the counter of slots, or the combination thereof satisfying a threshold. 
     Example 23. The method of examples 1-22, further including: initiating, upon processing the one or more signals using the first neural network block, a timer; determining that the timer has expired; and processing, based at least in part on determining that the timer has expired, the one or more signals using a default neural network block different than the first neural network block. 
     Example 24. The method of examples 1-23, further including: receiving, from the base station, additional control signaling including one or more additional neural network block parameters; and transmitting, to the base station, a negative acknowledgement message indicating that the one or more additional neural network block parameters failed to be successfully decoded. 
     Example 25. The method of example 24, further including: determining a priority status of the one or more additional neural network block parameters, wherein transmitting the negative acknowledgement message is based at least in part on the priority status. 
     Example 26. The method of examples 1-25, further including: receiving, from the base station, a request for feedback information about a performance of the first neural network block; and transmitting, based at least in part on processing the one or more signals using the first neural network block and the request, a report including the feedback information about the performance of the first neural network block to the base station. 
     Example 27. The method of example 26, wherein: the request is communicated using a DCI message, a MAC-CE message, or an RRC message; and the feedback information is communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel. 
     Example 28. The method of example 26, wherein the feedback information includes processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     Example 29. The method of examples 1-28, wherein the first neural network block may be configured to perform channel estimation for the one or more baseband signals, channel state information compression for the one or more baseband signals, or a combination thereof. 
     Example 30. The method of examples 1-29, further including: receiving, from the base station, second capability information indicating that the base station supports at least one neural network block, wherein transmitting the capability information to the base station is based at least in part on receiving the second capability information. 
     Example 31. The method of example 30, wherein the second capability information is included in a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     Example 32. The method of examples 1-31, wherein the one or more neural network block parameters include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     Example 33. The method of any of examples 1-32, wherein the capability information is communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel, or a combination thereof. 
     Example 34. The method of any of examples 1-33, further including transmitting, to the base station, an acknowledgement message indicating that the control signaling has been successfully received. 
     Example 35. A method for wireless communications performed by a processor at a base station, including: receiving, from a user equipment (UE), UE capability information indicating that the UE supports one or more neural network blocks; configuring, based on the received UE capability information, control signaling with one or more neural network block parameters for a first neural network block of one or more neural network blocks supported by the UE; transmitting the control signaling to the UE. 
     Example 36. The method of example 35, wherein the one or more neural network block parameters further includes one or more adjustment parameters to the first neural network block used to process one or more signals, e.g., baseband signals, by the UE. 
     Example 37. The method of example 36, wherein the control signaling includes a resource allocation message including the one or more neural network block parameters for configuring network components. 
     Example 38. The method of example 37, wherein the network components include one or more cell groups, one or more component carriers associated with each of the one or more cell groups, one or more bandwidth parts associated with each of the one or more component carriers, or a combination thereof. 
     Example 39. The method of example 38, wherein the one or more cell groups includes a master cell group, a secondary cell group, a supplementary cell group, or a combination thereof. 
     Example 40. The method of any of examples 35-39, wherein transmitting the control signaling further includes: transmitting, to the UE, the control signaling over a physical downlink control channel, wherein the one or more neural network block parameters are transmitted over the physical downlink control channel according to the control signaling. 
     Example 41. The method of example 37, wherein the resource allocation message includes a downlink control information message, a media access control element, or radio resource control message. 
     Example 42. The method of any of examples 35-41, wherein the one or more adjustment parameters include an activation indication for one or more nodes of the first neural network block, a deactivation indication for the one or more nodes of the first neural network block, a weight value for the one or more nodes of the first neural network block, or a bias value for the one or more nodes of the first neural network block, or a combination thereof. 
     Example 43. The method of any of examples 35-42, wherein the one or more adjustment parameters to the first neural network block include a first adjustment to a first weight value for a first submodule of the first neural network block or a second adjustment to a second weight value for a second submodule of the first neural network block, or both. 
     Example 44. The method of any of examples 35-43, wherein transmitting the control signaling further includes: transmitting configuration information for the first neural network block and the one or more neural network block parameters over a physical downlink control channel, wherein the configuration information includes instructions for the UE to configure the first neural network block in place of a default network function block of the UE. 
     Example 45. The method of any of examples 35-44, wherein the configuration information includes an indication of one or more added algorithms for the first neural network block, one or more released algorithms from the default network function block, or a combination thereof. 
     Example 46. The method of any of examples 35-45, wherein one or more neural network block types supported by the UE includes the default network function block. 
     Example 47. The method of example 46, wherein transmitting the control signaling further includes: transmitting control information to the UE that indicates a new network function block to be the default network function block, wherein the control information is included in the control signaling. 
     Example 48. The method of any of examples 35-47, further including: receiving a request message from the UE to use a second neural network block different than the first neural network block; and transmitting an acknowledgement message to the UE based at least in part on the receiving of the request message. 
     Example 49. The method of any of examples 35-48, wherein transmitting the control signaling further includes: transmitting an indication of the first neural network block of a set of neural network blocks stored by the UE, wherein the one or more neural network blocks supported by the UE includes the set of neural network blocks. 
     Example 50. The method of any of examples 35-49, further including: transmitting a downlink message that includes the one or more neural network block parameters, wherein the one or more neural network block parameters transmitted as part of the downlink message. 
     Example 51. The method of any of examples 35-50, further including: transmitting an indication of a timer and an instruction for the UE to initiate the timer upon receiving the one or more neural network block parameters. 
     Example 52. The method of any of examples 35-51, further including: transmitting an indication of a counter of symbols, a counter of slots, or a combination thereof and an instruction for the UE to initiate the counter of symbols, the counter of slots, or the combination thereof upon receiving the one or more neural network block parameters. 
     Example 53. The method of any of examples 35-52, further including: transmitting, to the UE, one or more additional neural network block parameters; and receiving, from the UE, a negative acknowledgement message indicating that the one or more neural network block parameters failed to be successfully decoded. 
     Example 54. The method of example 53, wherein receiving the negative acknowledgement message is based at least in part on a priority status of the one or more additional neural network block parameters. 
     Example 55. The method of any of examples 35-54, further including: transmitting, to the UE, a request for feedback information about a performance of the first neural network block; and receiving, based at least in part on the request for the feedback information about the performance of the first neural network block, a report including the feedback information about the performance of the first neural network block. 
     Example 56. The method of example 55, wherein the request is communicated using a DCI message, a MAC-CE message, or an RRC message; and the feedback information is communicated using an uplink control message on an uplink control channel or an uplink data message on a physical uplink shared channel. 
     Example 57. The method of example 55, wherein the feedback information includes processed data, unprocessed data, complete measurements, partial measurements, or a combination thereof. 
     Example 58. The method of any of examples 35-57, wherein the first neural network block may be configured to perform channel estimation for one or more baseband signals, channel state information compression for the one or more baseband signals, or a combination thereof. 
     Example 59. The method of any of examples 35-58, further including: transmitting, to the UE, second capability information indicating that the base station supports at least one neural network block, wherein receiving the capability information from the UE is based at least in part on receiving the second capability information. 
     Example 60. The method of example 59, wherein the second capability information includes a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     Example 61. The method of any of examples 35-60, wherein the capability information includes a system information block, a master information block, a downlink control information message, a media access control element, or a radio resource control message, or a combination thereof. 
     Example 62. The method of any of examples 35-61, wherein the one or more neural network block parameters include one or more input values, a number of layers of the first neural network block, a number of nodes for one or more layers of the first neural network block, a connection map across the one or more layers of the first neural network block, one or more activation functions for one or more nodes of the first neural network block, one or more weight values for the one or more nodes of the first neural network block, or one or more bias values for the one or more nodes of the first neural network block, or a combination thereof. 
     Example 63. The method of any of examples 35-62, further including: receiving a configuration of a configured first neural network block from the UE over a physical uplink control channel or a physical uplink shared channel, wherein the configured first neural network block has been configured by the UE based at least in part on the one or more neural network block parameters. 
     Example 64. The method of any of examples 35-63, further comprising receiving, from the UE, an acknowledgment message indicating that the control signaling has been successfully received by the UE. 
     Example 65. An apparatus for wireless communications at a user equipment (UE), including: a processor; memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform operations of any of claims  1 - 34 . 
     Example 66. An apparatus for wireless communications at a base station, including: a processor; memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform operations of any of claims  35 - 64 . 
     Example 67. An apparatus for wireless communications at a user equipment (UE), including means for performing functions of any of examples 1-34. 
     Example 68. An apparatus for wireless communications at a base station, including means for performing functions of any of examples 35-64. 
     Example 69. A non-transitory computer-readable medium storing code for wireless communications at a user equipment (UE), the code including instructions executable by a processor to perform operations of any of claims  1 - 34 . 
     Example 70. A non-transitory computer-readable medium storing code for wireless communications at a base station, the code including instructions executable by a processor to perform operations of any of claims  35 - 64 . 
     Example 71. A computer program product comprising code for wireless communications at a user equipment (UE), the code including instructions executable by a processor to perform operations of any of claims  1 - 34 . 
     Example 72. A computer program product comprising code for wireless communications at a base station, the code including instructions executable by a processor to perform operations of any of claims  35 - 64 . 
     It should be noted that the methods described herein describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Further, aspects from two or more of the methods may be combined. 
     Although aspects of an LTE, LTE-A, LTE-A Pro, or NR system may be described for purposes of example, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein are applicable beyond LTE, LTE-A, LTE-A Pro, or NR networks. For example, the described techniques may be applicable to various other wireless communications systems such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, as well as other systems and radio technologies not explicitly mentioned herein. 
     Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     The various illustrative blocks and components described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a digital signal processor (DSP), an ASIC, a central processor unit (CPU), an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration). 
     The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. 
     Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include random-access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that may be used to carry or store desired program code means in the form of instructions or data structures and that may be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of computer-readable medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media. 
     As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of” or “one or more of”) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on.” 
     In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If just the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label, or other subsequent reference label. 
     The description set forth herein, in connection with the appended drawings, describes example configurations and does not represent all the examples that may be implemented or that are within the scope of the claims. The term “example” used herein means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other examples.” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some instances, known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples. 
     The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be used to process other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein, but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.