Patent Publication Number: US-2021182658-A1

Title: Machine-Learning Architectures for Simultaneous Connection to Multiple Carriers

Description:
BACKGROUND 
     The evolution of wireless communication systems oftentimes stems from a demand for data throughput. As one example, the demand for data increases as more and more devices gain access to wireless communication systems. Evolving devices also execute data-intensive applications that utilize more data than traditional applications, such as data-intensive streaming-video applications, data-intensive social media applications, data-intensive audio services, etc. Thus, to accommodate increased data usage, evolving wireless communication systems utilize increasingly complex architectures to provide more data throughput relative to legacy wireless communication systems. 
     As one example, fifth generation (5G) standards and technologies transmit data using higher frequency bands, such as the above-6 Gigahertz (GHz) band (e.g., 5G millimeter wave (mmW) technologies) to increase data capacity. However, transmitting and recovering information using these higher frequency ranges poses challenges. To illustrate, higher frequency signals are more susceptible to multipath fading, scattering, atmospheric absorption, diffraction, interference, and so forth, relative to lower-frequency signals. These signal distortions oftentimes lead to errors when recovering the information at a receiver. As another example, hardware capable of transmitting, receiving, routing, and/or otherwise using these higher frequencies can be complex and expensive, which increases the processing costs in a wirelessly-networked device. 
     SUMMARY 
     This document describes techniques and apparatuses for machine-learning architectures for simultaneous connection to multiple carriers. In implementations, a network entity determines at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier. At times, the at least one DNN configuration includes a first portion for forming a first DNN at the network entity, and a second portion for forming a second DNN at the UE. The network entity forms the first DNN based on the first portion and communicates an indication of the second portion to the UE. The network entity directs the UE to form the second DNN based on the second portion, and uses the first DNN to exchange, over the wireless communication system, the information with the UE using the carrier aggregation. 
     Aspects of machine-learning architectures for simultaneous connection to multiple carriers include a user equipment (UE) associated with a wireless communication system. In implementations, the UE receives an indication of at least one deep neural network (DNN) configuration for processing information exchanged over the wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier. The UE forms, based on the indication, at least one DNN. The UE then uses the at least one DNN to process the information exchanged over the wireless communication system using the carrier aggregation. 
     The details of one or more implementations of machine-learning architectures for simultaneous connection to multiple carriers are set forth in the accompanying drawings and the following description. Other features and advantages will be apparent from the description and drawings, and from the claims. This summary introduces subject matter that is further described in the Detailed Description and Drawings. Accordingly, this summary should not be considered to describe essential features nor used to limit the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The details of one or more aspects of machine-learning architectures for simultaneous connection to multiple carriers for wireless networks are described below. The use of the same reference numbers in different instances in the description and the figures indicate similar elements: 
         FIG. 1  illustrates an example environment in which various aspects of machine-learning architectures for simultaneous connection to multiple carriers can be implemented. 
         FIG. 2  illustrates an example device diagram of devices that can implement various aspects of machine-learning architectures for simultaneous connection to multiple carriers. 
         FIG. 3  illustrates an example device diagram of a device that can implement various aspects of machine-learning architectures for simultaneous connection to multiple carriers. 
         FIG. 4  illustrates an example machine-learning module that can implement various aspects of machine-learning architectures for simultaneous connection to multiple carriers. 
         FIG. 5  illustrates example block diagrams of processing chains utilized by devices to process communications transmitted over a wireless communication system. 
         FIG. 6  illustrates an example operating environment in which multiple deep neural networks are utilized in a wireless communication system. 
         FIG. 7  illustrates an example transaction diagram between various devices for configuring a neural network using a neural network formation configuration. 
         FIG. 8  illustrates an example of generating multiple neural network formation configurations. 
         FIG. 9  illustrates an example environment in which various aspects of machine-learning architectures for simultaneous connection to multiple carriers can be implemented. 
         FIG. 10  illustrates an example environment in which various aspects of machine-learning architectures for simultaneous connection to multiple carriers can be implemented. 
         FIG. 11  illustrates an example environment in which various aspects of machine-learning architectures for simultaneous connection to multiple carriers can be implemented. 
         FIG. 12  illustrates an example environment in which various aspects of machine-learning architectures for simultaneous connection to multiple carriers can be implemented. 
         FIG. 13  illustrates an example environment in which various aspects of machine-learning architectures for simultaneous connection to multiple carriers can be implemented. 
         FIG. 14  illustrates an example transaction diagram between various devices in accordance with various implementations of machine-learning architectures for simultaneous connection to multiple carriers. 
         FIG. 15  illustrates an example transaction diagram between various devices in accordance with various implementations of machine-learning architectures for simultaneous connection to multiple carriers. 
         FIG. 16  illustrates an example method for using machine-learning architectures for simultaneous connection to multiple carriers. 
         FIG. 17  illustrates an example method for using machine-learning architectures for simultaneous connection to multiple carriers. 
     
    
    
     DETAILED DESCRIPTION 
     In conventional wireless communication systems, transmitter and receiver processing chains include complex functionality. For instance, a channel estimation block in the processing chain estimates or predicts how a signal distorts while propagating through a transmission environment. As another example, channel equalizer blocks reverse the signal distortions identified by the channel estimation block. These complex functions oftentimes become more complicated when processing higher frequency ranges, such as 5G mmW signals that are at or around the 6 GHz band. 
     DNNs provide alternative solutions to complex processing, such as the complex functionality used in a wireless communication system. By training a DNN on transmitter and/or receiver processing chain operations, the DNN can replace conventional functionality in a variety of ways, such as by replacing some or all of the conventional processing blocks used to transmit communication signals using carrier aggregation, replacing individual processing chain blocks, etc. Dynamic reconfiguration of a DNN, such as by modifying various parameter configurations (e.g., coefficients, layer connections, kernel sizes) also provides an ability to adapt to changing operating conditions. 
     This document describes aspects of machine-learning architectures for simultaneous connection to multiple carriers. In implementations, a network entity associated with a wireless communication system determines at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier. At times, the at least one DNN configuration includes a first portion for forming a first DNN at the network entity, and a second portion for forming a second DNN at the UE. The network entity forms the first DNN based on the first portion and communicates an indication of the second portion to the UE. The network entity directs the UE to form the second DNN based on the second portion, and uses the first DNN to exchange, over the wireless communication system, the information with the UE using the carrier aggregation. 
     Aspects of machine-learning architectures for simultaneous connection to multiple carriers include a user equipment (UE) associated with a wireless communication system. In implementations, the UE receives an indication of at least one deep neural network (DNN) configuration for processing information exchanged over the wireless communication system using carrier aggregation that includes at least a first component carrier and a second component carrier. The UE forms, based on the indication, at least one DNN. The UE then uses the at least one DNN to process the information exchanged over the wireless communication system using the carrier aggregation. 
     Using DNN(s) to process information exchanged over a wireless communication system using multiple component carriers, such as through carrier aggregation, allows various devices operating in the wireless communication system to correct for changes in a current operating condition, such as transmission environment changes or component carrier changes. Alternately or additionally, the DNN(s) can be formed based upon capabilities of a UE or base station participating in the multiple component carrier communications. Configuring the DNNs based on the component carriers, device capabilities, current operating conditions, and so forth, improves an overall performance (e.g., lower bit errors, improved signal quality, improved latency) of how the devices transmit and recover the information. Further, DNNs can be trained to process complex input that corresponds to a complex environment, such a complex environment that correspond to transmitting and recovering information using multiple component carriers, such as offline training performed by a manufacturer of wireless devices or online training performed at a base station or core network server. Training the DNNs on these variations (e.g., a variety of component carrier combinations, different operating environments, different device capabilities) also provides a flexible and modifiable solution to complex processing as the transmission environment and participating devices change. 
     The phrases “transmitted over,” “communications exchanged,” and “communications associated with” include generating communications to be transmitted over the wireless communication system (e.g. processing pre-transmission communications) and/or processing communications received over the wireless communication system. Thus, “processing communications transmitted over the wireless communication system,” “communications exchanged over the wireless communication system,” as well as “communications associated with the wireless communication system” include generating the transmissions (e.g., pre-transmission processing), processing received transmissions, or any combination thereof. 
     Example Environment 
       FIG. 1  illustrates an example environment  100  which includes a user equipment  110  (UE  110 ) that can communicate with base stations  120  (illustrated as base stations  121  and  122 ) through one or more wireless communication links  130  (wireless link  130 ), illustrated as wireless links  131  and  132 . For simplicity, the UE  110  is implemented as a smartphone but may be implemented as any suitable computing or electronic device, such as a mobile communication device, modem, cellular phone, gaming device, navigation device, media device, laptop computer, desktop computer, tablet computer, smart appliance, vehicle-based communication system, or an Internet-of-Things (IoT) device such as a sensor or an actuator. The base stations  120  (e.g., an Evolved Universal Terrestrial Radio Access Network Node B, E-UTRAN Node B, evolved Node B, eNodeB, eNB, Next Generation Node B, gNode B, gNB, ng-eNB, or the like) may be implemented in a macrocell, microcell, small cell, picocell, and the like, or any combination thereof. 
     The base stations  120  communicate with the UE  110  using the wireless links  131  and  132 , which may be implemented as any suitable type of wireless link. The wireless links  131  and  132  include control and data communication, such as downlink of data and control information communicated from the base stations  120  to the UE  110 , uplink of other data and control information communicated from the UE  110  to the base stations  120 , or both. The wireless links  130  may include one or more wireless links (e.g., radio links) or bearers implemented using any suitable communication protocol or standard, or combination of communication protocols or standards, such as 3rd Generation Partnership Project Long-Term Evolution (3GPP LTE), Fifth Generation New Radio (5G NR), and so forth. Multiple wireless links  130  may be aggregated in a carrier aggregation to provide a higher data rate for the UE  110 . Multiple wireless links  130  from multiple base stations  120  may be configured for Coordinated Multipoint (CoMP) communication with the UE  110 . 
     The base stations  120  are collectively a Radio Access Network  140  (e.g., RAN, Evolved Universal Terrestrial Radio Access Network, E-UTRAN, 5G NR RAN or NR RAN). The base stations  121  and  122  in the RAN  140  are connected to a core network  150 . The base stations  121  and  122  connect, at  102  and  104  respectively, to the core network  150  through an NG 2  interface for control-plane signaling and using an NG 3  interface for user-plane data communications when connecting to a 5G core network, or using an S1 interface for control-plane signaling and user-plane data communications when connecting to an Evolved Packet Core (EPC) network. The base stations  121  and  122  can communicate using an Xn Application Protocol (XnAP) through an Xn interface, or using an X2 Application Protocol (X2AP) through an X2 interface, at  106 , to exchange user-plane and control-plane data. The UE  110  may connect, via the core network  150 , to public networks, such as the Internet  160  to interact with a remote service  170 . The remote service  170  represents the computing, communication, and storage devices used to provide any of a multitude of services including interactive voice or video communication, file transfer, streaming voice or video, and other technical services implemented in any manner such as voice calls, video calls, website access, messaging services (e.g., text messaging or multi-media messaging), photo file transfer, enterprise software applications, social media applications, video-gaming, streaming video services, and podcasts. 
     Example Devices 
       FIG. 2  illustrates an example device diagram  200  of the UE  110  and one of the base stations  120 .  FIG. 3  illustrates an example device diagram  300  of a core network server  302 . The UE  110 , the base station  120 , and/or the core network server  302  may include additional functions and interfaces that are omitted from  FIG. 2  or  FIG. 3  for the sake of clarity. 
     The UE  110  includes antennas  202 , a radio frequency front end  204  (RF front end  204 ), a wireless transceiver (e.g., an LTE transceiver  206 , and/or a 5G NR transceiver  208 ) for communicating with the base station  120  in the RAN  140 . The RF front end  204  of the UE  110  can couple or connect the LTE transceiver  206 , and the 5G NR transceiver  208  to the antennas  202  to facilitate various types of wireless communication. The antennas  202  of the UE  110  may include an array of multiple antennas that are configured similar to or differently from each other. The antennas  202  and the RF front end  204  can be tuned to, and/or be tunable to, one or more frequency bands defined by the 3GPP LTE and 5G NR communication standards and implemented by the LTE transceiver  206 , and/or the 5G NR transceiver  208 . Additionally, the antennas  202 , the RF front end  204 , the LTE transceiver  206 , and/or the 5G NR transceiver  208  may be configured to support beamforming for the transmission and reception of communications with the base station  120 . By way of example and not limitation, the antennas  202  and the RF front end  204  can be implemented for operation in sub-gigahertz bands, sub-6 GHz bands, and/or above 6 GHz bands that are defined by the 3GPP LTE and 5G NR communication standards. 
     The UE  110  also includes processor(s)  210  and computer-readable storage media  212  (CRM  212 ). The processor  210  may be a single core processor or a multiple core processor composed of a variety of materials, such as silicon, polysilicon, high-K dielectric, copper, and so on. The computer-readable storage media described herein excludes propagating signals. CRM  212  may include any suitable memory or storage device such as random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), or Flash memory useable to store device data  214  of the UE  110 . The device data  214  includes user data, multimedia data, beamforming codebooks, applications, neural network tables, and/or an operating system of the UE  110 , which are executable by processor(s)  210  to enable user-plane communication, control-plane signaling, and user interaction with the UE  110 . 
     In some implementations, the computer-readable storage media  212  includes a neural network table  216  that stores various architecture and/or parameter configurations that form a neural network, such as, by way of example and not of limitation, parameters that specify a fully-connected layer neural network architecture, a convolutional layer neural network architecture, a recurrent neural network layer, a number of connected hidden neural network layers, an input layer architecture, an output layer architecture, a number of nodes utilized by the neural network, coefficients (e.g., weights and biases) utilized by the neural network, kernel parameters, a number of filters utilized by the neural network, strides/pooling configurations utilized by the neural network, an activation function of each neural network layer, interconnections between neural network layers, neural network layers to skip, and so forth. Accordingly, the neural network table  216  includes any combination of NN formation configuration elements (e.g., architecture and/or parameter configurations) that can be used to create a NN formation configuration (e.g., a combination of one or more NN formation configuration elements) that defines and/or forms a DNN. In some implementations, a single index value of the neural network table  216  maps to a single NN formation configuration element (e.g., a 1:1 correspondence). Alternately or additionally, a single index value of the neural network table  216  maps to a NN formation configuration (e.g., a combination of NN formation configuration elements). In some implementations, the neural network table includes input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration element and/or NN formation configuration as further described. 
     In some implementations, the CRM  212  may also include a user equipment neural network manager  218  (UE neural network manager  218 ). Alternately or additionally, the UE neural network manager  218  may be implemented in whole or part as hardware logic or circuitry integrated with or separate from other components of the UE  110 . The UE neural network manager  218  accesses the neural network table  216 , such as by way of an index value, and forms a DNN using the NN formation configuration elements specified by a NN formation configuration. In implementations, UE neural network manager forms multiple DNNs to process wireless communications (e.g., downlink communications and/or uplink communications exchanged with the base station  120 ). 
     The device diagram for the base station  120 , shown in  FIG. 2 , includes a single network node (e.g., a gNode B). The functionality of the base station  120  may be distributed across multiple network nodes or devices and may be distributed in any fashion suitable to perform the functions described herein. The base station  120  include antennas  252 , a radio frequency front end  254  (RF front end  254 ), one or more wireless transceivers (e.g. one or more LTE transceivers  256 , and/or one or more 5G NR transceivers  258 ) for communicating with the UE  110 . The RF front end  254  of the base station  120  can couple or connect the LTE transceivers  256  and the 5G NR transceivers  258  to the antennas  252  to facilitate various types of wireless communication. The antennas  252  of the base station  120  may include an array of multiple antennas that are configured similar to, or different from, each other. The antennas  252  and the RF front end  254  can be tuned to, and/or be tunable to, one or more frequency band defined by the 3GPP LTE and 5G NR communication standards, and implemented by the LTE transceivers  256 , and/or the 5G NR transceivers  258 . Additionally, the antennas  252 , the RF front end  254 , the LTE transceivers  256 , and/or the 5G NR transceivers  258  may be configured to support beamforming, such as Massive-Multiple-In, Multiple Out (Massive-MIMO), for the transmission and reception of communications with the UE  110 . 
     The base station  120  also include processor(s)  260  and computer-readable storage media  262  (CRM  262 ). The processor  260  may be a single core processor or a multiple core processor composed of a variety of materials, such as silicon, polysilicon, high-K dielectric, copper, and so on. CRM  262  may include any suitable memory or storage device such as random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), or Flash memory useable to store device data  264  of the base station  120 . The device data  264  includes network scheduling data, radio resource management data, beamforming codebooks, applications, and/or an operating system of the base station  120 , which are executable by processor(s)  260  to enable communication with the UE  110 . 
     CRM  262  also includes a base station manager  266 . Alternately or additionally, the base station manager  266  may be implemented in whole or part as hardware logic or circuitry integrated with or separate from other components of the base station  120 . In at least some aspects, the base station manager  266  configures the LTE transceivers  256  and the 5G NR transceivers  258  for communication with the UE  110 , as well as communication with a core network, such as the core network  150 . 
     CRM  262  also includes a base station neural network manager  268  (BS neural network manager  268 ). Alternately or additionally, the BS neural network manager  268  may be implemented in whole or part as hardware logic or circuitry integrated with or separate from other components of the base station  120 . In at least some aspects, the BS neural network manager  268  selects the NN formation configurations utilized by the base station  120  and/or UE  110  to configure deep neural networks for processing wireless communications, such as by selecting a combination of NN formation configuration elements. In some implementations, the BS neural network manager receives feedback from the UE  110 , and selects the neural network formation configuration based on the feedback. Alternately or additionally, the BS neural network manager  268  receives neural network formation configuration directions from core network  150  elements through a core network interface  276  or an inter-base station interface  274  and forwards the neural network formation configuration directions to UE  110 . At times, the BS neural network manager  268  selects one or more NN formation configurations for processing communications exchanged using carrier aggregation, other multiple component carrier communications, and/or other split architecture implementations. 
     CRM  262  includes training module  270  and neural network table  272 . In implementations, the base station  120  manage and deploy NN formation configurations to UE  110 . Alternately or additionally, the base station  120  maintain the neural network table  272 . The training module  270  teaches and/or trains DNNs using known input data. For instance, the training module  270  trains DNN(s) for different purposes, such as processing communications transmitted over a wireless communication system (e.g., encoding downlink communications, modulating downlink communications, demodulating downlink communications, decoding downlink communications, encoding uplink communications, modulating uplink communications, demodulating uplink communications, decoding uplink communications). This includes training the DNN(s) offline (e.g., while the DNN is not actively engaged in processing the communications) and/or online (e.g., while the DNN is actively engaged in processing the communications). 
     In implementations, the training module  270  extracts learned parameter configurations from the DNN to identify the NN formation configuration elements and/or NN formation configuration, and then adds and/or updates the NN formation configuration elements and/or NN formation configuration in the neural network table  272 . The extracted parameter configurations include any combination of information that defines the behavior of a neural network, such as node connections, coefficients, active layers, weights, biases, pooling, etc. 
     The neural network table  272  stores multiple different NN formation configuration elements and/or NN formation configurations generated using the training module  270 . In some implementations, the neural network table includes input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration element and/or NN formation configuration. For instance, the input characteristics includes, by way of example and not of limitation, power information, signal-to-interference-plus-noise ratio (SINR) information, channel quality indicator (CQI) information, channel state information (CSI), Doppler feedback, frequency bands, BLock Error Rate (BLER), Quality of Service (QoS), Hybrid Automatic Repeat reQuest (HARD) information (e.g., first transmission error rate, second transmission error rate, maximum retransmissions), latency, Radio Link Control (RLC), Automatic Repeat reQuest (ARQ) metrics, received signal strength (RSS), uplink SINR, timing measurements, error metrics, UE capabilities, base station capabilities (BS capabilities), power mode, Internet Protocol (IP) layer throughput, end2end latency, end2end packet loss ratio, etc. Accordingly, the input characteristics include, at times, Layer  1 , Layer  2 , and/or Layer  3  metrics. In some implementations, a single index value of the neural network table  272  maps to a single NN formation configuration element (e.g., a 1:1 correspondence). Alternately or additionally, a single index value of the neural network table  272  maps to a NN formation configuration (e.g., a combination of NN formation configuration elements). 
     In implementations, the base station  120  synchronizes the neural network table  272  with the neural network table  216  such that the NN formation configuration elements and/or input characteristics stored in one neural network table is replicated in the second neural network table. Alternately or additionally, the base station  120  synchronizes the neural network table  272  with the neural network table  216  such that the NN formation configuration elements and/or input characteristics stored in one neural network table represent complementary functionality in the second neural network table (e.g., NN formation configuration elements for transmitter path processing in the first neural network table, NN formation configuration elements for receiver path processing in the second neural network table). 
     The base station  120  also include an inter-base station interface  274 , such as an Xn and/or X2 interface, which the base station manager  266  configures to exchange user-plane, control-plane, and other information between other base station  120 , to manage the communication of the base station  120  with the UE  110 . The base station  120  include a core network interface  276  that the base station manager  266  configures to exchange user-plane, control-plane, and other information with core network functions and/or entities. 
     In  FIG. 3 , the core network server  302  may provide all or part of a function, entity, service, and/or gateway in the core network  150 . Each function, entity, service, and/or gateway in the core network  150  may be provided as a service in the core network  150 , distributed across multiple servers, or embodied on a dedicated server. For example, the core network server  302  may provide the all or a portion of the services or functions of a User Plane Function (UPF), an Access and Mobility Management Function (AMF), a Serving Gateway (S-GW), a Packet Data Network Gateway (P-GW), a Mobility Management Entity (MME), an Evolved Packet Data Gateway (ePDG), and so forth. The core network server  302  is illustrated as being embodied on a single server that includes processor(s)  304  and computer-readable storage media  306  (CRM  306 ). The processor  304  may be a single core processor or a multiple core processor composed of a variety of materials, such as silicon, polysilicon, high-K dielectric, copper, and so on. CRM  306  may include any suitable memory or storage device such as random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), hard disk drives, or Flash memory useful to store device data  308  of the core network server  302 . The device data  308  includes data to support a core network function or entity, and/or an operating system of the core network server  302 , which are executable by processor(s)  304 . 
     CRM  306  also includes one or more core network applications  310 , which, in one implementation, is embodied on CRM  306  (as shown). The one or more core network applications  310  may implement the functionality such as UPF, AMF, S-GW, P-GW, MME, ePDG, and so forth. Alternately or additionally, the one or more core network applications  310  may be implemented in whole or part as hardware logic or circuitry integrated with or separate from other components of the core network server  302 . 
     CRM  306  also includes a core network neural network manager  312  that manages NN formation configurations used to process communications exchanged between UE  110  and the base stations  120 . In some implementations, the core network neural network manager  312  analyzes various parameters, such as current signal channel conditions (e.g., as reported by base stations  120 , as reported by other wireless access points, as reported by UEs  110  (via base stations or other wireless access points)), capabilities at base stations  120  (e.g., antenna configurations, cell configurations, Multiple-In, Multiple-Out (MIMO), capabilities, radio capabilities, processing capabilities), capabilities of UE  110  (e.g., antenna configurations, MIMO capabilities, radio capabilities, processing capabilities), and so forth. For example, the base stations  120  obtain the various parameters during the communications with the UE and forward the parameters to the core network neural network manager  312 . The core network neural network manager selects, based on these parameters, a NN formation configuration that improves the accuracy of a DNN processing the communications. Improving the accuracy signifies an improved accuracy in the output, such as lower bit errors, generated by the neural network relative to a neural network configured with another NN formation configuration. The core network neural network manager  312  then communicates the selected NN formation configuration to the base stations  120  and/or the UE  110 . In implementations, the core network neural network manager  312  receives UE and/or BS feedback from the base station  120  and selects an updated NN formation configuration based on the feedback. Alternately or additionally, the core network neural network manager  312  selects one or more NN formation configurations for processing communications exchanged using carrier aggregation, other multiple component carrier communications, and/or other split architecture implementations. 
     CRM  306  includes training module  314  and neural network table  316 . In implementations, the core network server  302  manages and deploys NN formation configurations to multiple devices in a wireless communication system, such as UEs  110  and base stations  120 . Alternately or additionally, the core network server maintains the neural network table  316  outside of the CRM  306 . The training module  314  teaches and/or trains DNNs using known input data. For instance, the training module  314  trains DNN(s) to process different types of pilot communications transmitted over a wireless communication system. This includes training the DNN(s) offline and/or online. In implementations, the training module  314  extracts a learned NN formation configuration and/or learned NN formation configuration elements from the DNN and stores the learned NN formation configuration elements in the neural network table  316 . Thus, a NN formation configuration includes any combination of architecture configurations (e.g., node connections, layer connections) and/or parameter configurations (e.g., weights, biases, pooling) that define or influence the behavior of a DNN. In some implementations, a single index value of the neural network table  316  maps to a single NN formation configuration element (e.g., a 1:1 correspondence). Alternately or additionally, a single index value of the neural network table  316  maps to a NN formation configuration (e.g., a combination of NN formation configuration elements). 
     In some implementations, the training module  314  of the core network neural network manager  312  generates complementary NN formation configurations and/or NN formation configuration elements to those stored in the neural network table  216  at the UE  110  and/or the neural network table  272  at the base station  121 . As one example, the training module  314  generates neural network table  316  with NN formation configurations and/or NN formation configuration elements that have a high variation in the architecture and/or parameter configurations relative to medium and/or low variations used to generate the neural network table  272  and/or the neural network table  216 . For instance, the NN formation configurations and/or NN formation configuration elements generated by the training module  314  correspond to fully-connected layers, a full kernel size, frequent sampling and/or pooling, high weighting accuracy, and so forth. Accordingly, the neural network table  316  includes, at times, high accuracy neural networks at the trade-off of increased processing complexity and/or time. 
     The NN formation configurations and/or NN formation configuration elements generated by the training module  270  have, at times, more fixed architecture and/or parameter configurations (e.g., fixed connection layers, fixed kernel size, etc.), and less variation, relative to those generated by the training module  314 . The training module  270 , for example, generates streamlined NN formation configurations (e.g., faster computation times, less data processing), relative to those generated by the training module  314 , to optimize or improve a performance of end2end network communications at the base station  121  and/or the UE  110 . Alternately or additionally, the NN formation configurations and/or NN formation configuration elements stored at the neural network table  216  at the UE  110  include more fixed architecture and/or parameter configurations, relative to those stored in the neural network table  316  and/or the neural network table  272 , that reduce requirements (e.g., computation speed, less data processing points, less computations, less power consumption, etc.) at the UE  110  relative to the base station  121  and/or the core network server  302 . In implementations, the variations in fixed (or flexible) architecture and/or parameter configurations at each neural network are based on the processing resources (e.g., processing capabilities, memory constraints, quantization constraints (e.g., 8-bit vs. 16-bit), fixed-point vs. floating point computations, floating point operations per second (FLOPS), power availability) of the devices targeted to form the corresponding DNNs. Thus, UEs or access points with less processing resources relative to a core network server or base station receive NN formation configurations optimized for the available processing resources. 
     The neural network table  316  stores multiple different NN formation configuration elements generated using the training module  314 . In some implementations, the neural network table includes input characteristics for each NN formation configuration element and/or NN formation configuration, where the input characteristics describe properties about the training data used to generate the NN formation configuration. For instance, the input characteristics can include power information, SINR information, CQI, CSI, Doppler feedback, RSS, error metrics, minimum end-to-end (E2E) latency, desired E2E latency, E2E QoS, E2E throughput, E2E packet loss ratio, cost of service, etc. 
     CRM  306  also includes an end-to-end machine-learning controller  318  (E2E ML controller  318 ). The E2E ML controller  318  determines an end-to-end machine-learning configuration (E2E ML configuration) for processing information exchanged through an E2E communication, such as a QoS flow. In implementations, the E2E ML controller analyzes any combination of ML capabilities (e.g., supported ML architectures, supported number of layers, available processing power, memory limitations, available power budget, fixed-point processing vs. floating point processing, maximum kernel size capability, computation capability) of devices participating in the E2E communication. Alternately or additionally, the E2E ML controller analyzes any combination of QoS requirements, QoS parameters, and/or QoS characteristics to determine an E2E ML configuration that satisfies the associated requirements, parameters, and/or characteristics. In some implementations, the E2E ML controller obtains metrics that characterize a current operating environment and analyzes the current operating environment to determine the E2E ML configuration. This includes determining an E2E ML configuration that includes an architecture configuration in combination with parameter configuration(s) that define a DNN or determining an E2E ML configuration that simply includes parameter configurations used to update the DNN. 
     In determining the E2E ML configuration, the E2E ML controller sometimes determines a partitioned E2E ML configuration that distributes the processing functionality associated with the E2E ML configuration across multiple devices. For clarity,  FIG. 3  illustrates the end-to-end machine-learning controller  318  as separate from the core network neural network manager  312 , but in alternate or additional implementations, the core network neural network manager  312  includes functionality performed by the end-to-end machine-learning controller  318  or vice versa. Further, while  FIG. 3  illustrates the core network server  302  implementing the E2E ML controller  318 , alternate or additional devices can implement the E2E ML controller, such as the base station  120  and/or other network elements. 
     The core network server  302  also includes a network-slice manager  320 . Generally speaking, the network-slice manager  320  partitions network resources (e.g., physical hardware, physical spectrum, logical channels, network functions, services provided, quality of service, latency) to determine and generate network slices that provide different quality-of-service flows through the wireless communication network (e.g., provide different quality-of-service flows between at least one UE  110 , at least one base station  120 , and the core network  150 ). At times, the network-slice manager  320  works in conjunction with the E2E ML controller  318  to determine the partitions to the network resources and provide communication exchanges that meet or exceed a quality-of-service level. For example, the quality-of-service level can be specified through one or more quality-of-service parameters, such as latency, throughput (e.g., bandwidth or data rate), reliability, or an error rate (e.g., a bit error rate). Other example quality-of-service parameters include availability, packet loss, or jitter. In addition to the quality-of-service level, the network slice can also provide a particular level of security through cryptography. In some implementations, the network-slice manager  320  associates each network slice with one or more end-to-end machine-learning architectures to provide the quality-of-service level. For clarity,  FIG. 3  illustrates the network-slice manager  320  as separate from the core network neural network manager  312  and the E2E ML controller  318 , but in alternate or additional implementations, the core network neural network manager  312  and/or the E2E ML controller  318  include the functionality performed by the network-slice manager  320  or vice versa. Further, while  FIG. 3  illustrates the core network server  302  implementing the network-slice manager  320 , alternate or additional devices can implement the network-slice manager  320 , such as the base station  120  and/or other network elements. 
     The core network server  302  also includes a core network interface  322  for communication of user-plane, control-plane, and other information with the other functions or entities in the core network  150 , base stations  120 , or UE  110 . In implementations, the core network server  302  communicates NN formation configurations to the base station  120  using the core network interface  322 . The core network server  302  alternately or additionally receives feedback from the base stations  120  and/or the UE  110 , by way of the base stations  120 , using the core network interface  322 . 
     Having described an example environment and example devices that can be utilized for neural network formation configuration feedback in wireless communications, consider now a discussion of configurable machine-learning modules that is in accordance with one or more implementations. 
     Configurable Machine-Learning Modules 
       FIG. 4  illustrates an example machine-learning module  400 . The machine-learning module  400  implements a set of adaptive algorithms that learn and identify patterns within data. The machine-learning module  400  can be implemented using any combination of software, hardware, and/or firmware. 
     In  FIG. 4 , the machine-learning module  400  includes a deep neural network  402  (DNN  402 ) with groups of connected nodes (e.g., neurons and/or perceptrons) that are organized into three or more layers. The nodes between layers are configurable in a variety of ways, such as a partially-connected configuration where a first subset of nodes in a first layer are connected with a second subset of nodes in a second layer, a fully-connected configuration where each node in a first layer are connected to each node in a second layer, etc. A neuron processes input data to produce a continuous output value, such as any real number between 0 and 1. In some cases, the output value indicates how close the input data is to a desired category. A perceptron performs linear classifications on the input data, such as a binary classification. The nodes, whether neurons or perceptrons, can use a variety of algorithms to generate output information based upon adaptive learning. Using the DNN, the machine-learning module  400  performs a variety of different types of analysis, including single linear regression, multiple linear regression, logistic regression, step-wise regression, binary classification, multiclass classification, multi-variate adaptive regression splines, locally estimated scatterplot smoothing, and so forth. 
     In some implementations, the machine-learning module  400  adaptively learns based on supervised learning. In supervised learning, the machine-learning module  400  receives various types of input data as training data. The machine-learning module  400  processes the training data to learn how to map the input to a desired output. As one example, the machine-learning module  400  receives digital samples of a signal as input data and learns how to map the signal samples to binary data that reflects information embedded within the signal. As another example, the machine-learning module  400  receives binary data as input data and learns how to map the binary data to digital samples of a signal with the binary data embedded within the signal. During a training procedure, the machine-learning module  400  uses labeled or known data as an input to the DNN. The DNN analyzes the input using the nodes and generates a corresponding output. The machine-learning module  400  compares the corresponding output to truth data and adapts the algorithms implemented by the nodes to improve the accuracy of the output data. Afterwards, the DNN applies the adapted algorithms to unlabeled input data to generate corresponding output data. 
     The machine-learning module  400  uses statistical analyses and/or adaptive learning to map an input to an output. For instance, the machine-learning module  400  uses characteristics learned from training data to correlate an unknown input to an output that is statistically likely within a threshold range or value. This allows the machine-learning module  400  to receive complex input and identify a corresponding output. Some implementations train the machine-learning module  400  on characteristics of communications transmitted over a wireless communication system (e.g., time/frequency interleaving, time/frequency deinterleaving, convolutional encoding, convolutional decoding, power levels, channel equalization, inter-symbol interference, quadrature amplitude modulation/demodulation, frequency-division multiplexing/de-multiplexing, transmission channel characteristics). This allows the trained machine-learning module  400  to receive samples of a signal as an input, such as samples of a downlink signal received at a user equipment, and recover information from the downlink signal, such as the binary data embedded in the downlink signal. 
     In  FIG. 4 , the DNN includes an input layer  404 , an output layer  406 , and one or more hidden layer(s)  408  that are positioned between the input layer  404  and the output layer  406 . Each layer has an arbitrary number of nodes, where the number of nodes between layers can be the same or different. In other words, input layer  404  can have a same number and/or different number of nodes as output layer  406 , output layer  406  can have a same number and/or different number of nodes than hidden layer(s)  408 , and so forth. 
     Node  410  corresponds to one of several nodes included in input layer  404 , where the nodes perform independent computations from one another. As further described, a node receives input data, and processes the input data using algorithm(s) to produce output data. At times, the algorithm(s) include weights and/or coefficients that change based on adaptive learning. Thus, the weights and/or coefficients reflect information learned by the neural network. Each node can, in some cases, determine whether to pass the processed input data to the next node(s). To illustrate, after processing input data, node  410  can determine whether to pass the processed input data to node  412  and/or node  414  of hidden layer(s)  408 . Alternately or additionally, node  410  passes the processed input data to nodes based upon a layer connection architecture. This process can repeat throughout multiple layers until the DNN generates an output using the nodes of output layer  406 . 
     A neural network can also employ a variety of architectures that determine what nodes within the neural network are connected, how data is advanced and/or retained in the neural network, what weights and coefficients are used to process the input data, how the data is processed, and so forth. These various factors collectively describe a NN formation configuration. To illustrate, a recurrent neural network, such as a long short-term memory (LSTM) neural network, forms cycles between node connections in order to retain information from a previous portion of an input data sequence. The recurrent neural network then uses the retained information for a subsequent portion of the input data sequence. As another example, a feed-forward neural network passes information to forward connections without forming cycles to retain information. While described in the context of node connections, the NN formation configuration can include a variety of parameter configurations that influence how the neural network processes input data. 
     A NN formation configuration of a neural network can be characterized by various architecture and/or parameter configurations. To illustrate, consider an example in which the DNN implements a convolutional neural network. Generally, a convolutional neural network corresponds to a type of DNN in which the layers process data using convolutional operations to filter the input data. Accordingly, the convolutional NN formation configuration can be characterized with, by way of example and not of limitation, pooling parameter(s), kernel parameter(s), weights, and/or layer parameter(s). 
     A pooling parameter corresponds to a parameter that specifies pooling layers within the convolutional neural network that reduce the dimensions of the input data. To illustrate, a pooling layer can combine the output of nodes at a first layer into a node input at a second layer. Alternately or additionally, the pooling parameter specifies how and where in the layers of data processing the neural network pools data. A pooling parameter that indicates “max pooling,” for instance, configures the neural network to pool by selecting a maximum value from the grouping of data generated by the nodes of a first layer, and use the maximum value as the input into the single node of a second layer. A pooling parameter that indicates “average pooling” configures the neural network to generate an average value from the grouping of data generated by the nodes of the first layer and use the average value as the input to the single node of the second layer. 
     A kernel parameter indicates a filter size (e.g., a width and height) to use in processing input data. Alternately or additionally, the kernel parameter specifies a type of kernel method used in filtering and processing the input data. A support vector machine, for instance, corresponds to a kernel method that uses regression analysis to identify and/or classify data. Other types of kernel methods include Gaussian processes, canonical correlation analysis, spectral clustering methods, and so forth. Accordingly, the kernel parameter can indicate a filter size and/or a type of kernel method to apply in the neural network. 
     Weight parameters specify weights and biases used by the algorithms within the nodes to classify input data. In implementations, the weights and biases are learned parameter configurations, such as parameter configurations generated from training data. 
     A layer parameter specifies layer connections and/or layer types, such as a fully-connected layer type that indicates to connect every node in a first layer (e.g., output layer  406 ) to every node in a second layer (e.g., hidden layer(s)  408 ), a partially-connected layer type that indicates which nodes in the first layer to disconnect from the second layer, an activation layer type that indicates which filters and/or layers to activate within the neural network, and so forth. Alternately or additionally, the layer parameter specifies types of node layers, such as a normalization layer type, a convolutional layer type, a pooling layer type, etc. 
     While described in the context of pooling parameters, kernel parameters, weight parameters, and layer parameters, other parameter configurations can be used to form a DNN without departing from the scope of the claimed subject matter. Accordingly, a NN formation configuration can include any other type of parameter that can be applied to a DNN that influences how the DNN processes input data to generate output data. 
     Some implementations configure machine-learning module  400  based on a current operating environment. To illustrate, consider a machine-learning module trained to generate binary data from digital samples of a signal. A transmission environment oftentimes modifies the characteristics of a signal traveling through the environment. Transmission environments oftentimes change, which impacts how the environment modifies the signal. A first transmission environment, for instance, modifies a signal in a first manner, while a second transmission environment modifies the signal in a different manner than the first. These differences impact an accuracy of the output results generated by a machine-learning module. For instance, a neural network configured to process communications transmitted over the first transmission environment may generate errors when processing communications transmitted over the second transmission environment (e.g., bit errors that exceed a threshold value). 
     Various implementations generate and store NN formation configurations and/or NN formation configuration elements (e.g., various architecture and/or parameter configurations) for different transmission environments. Base stations  120  and/or core network server  302 , for example, train the machine-learning module  400  using any combination of BS neural network manager  268 , training module  270 , core network neural network manager  312 , and/or training module  314 . The training can occur offline when no active communication exchanges are occurring, or online during active communication exchanges. For example, the base stations  120  and/or core network server  302  can mathematically generate training data, access files that store the training data, obtain real-world communications data, etc. The base stations  120  and/or core network server  302  then extract and store the various learned NN formation configurations in a neural network table. Some implementations store input characteristics with each NN formation configuration, where the input characteristics describe various properties of the transmission environment corresponding to the respective NN formation configuration. In implementations, a neural network manager selects a NN formation configuration and/or NN formation configuration element(s) by matching a current transmission environment and/or current operating environment to the input characteristics. 
     Having described configurable machine-learning modules, consider now a discussion of deep neural networks in wireless communication systems that is in accordance with one or more implementations. 
     Deep Neural Networks in Wireless Communication Systems 
     Wireless communication systems include a variety of complex components and/or functions, such as the various devices and modules described with reference to the example environment  100  of  FIG. 1 , the example device diagram  200  of  FIG. 2 , and the example device diagram  300  of  FIG. 3 . In some implementations, the devices participating in the wireless communication system chain together a series of functions to enable the exchange of information over wireless connections. 
     To demonstrate,  FIG. 5  illustrates example block diagram  500  and example block diagram  502 , each of which depicts an example processing chain utilized by devices in a wireless communication system. For simplicity, the block diagrams illustrate high-level functionality, but the block diagrams may include additional functions that are omitted from  FIG. 5  for the sake of clarity. 
     In the upper portion of  FIG. 5 , block diagram  500  includes a transmitter block  504  and a receiver block  506 . Transmitter block  504  includes a transmitter processing chain that progresses from top to bottom. The transmitter processing chain begins with input data that progresses to an encoding stage, followed by a modulating stage, and then a radio frequency (RF) analog transmit (Tx) stage. The encoding stage can include any type and number of encoding stages employed by a device to transmit data over the wireless communication system. 
     To illustrate, an encoding stage receives binary data as input, and processes the binary data using various encoding algorithms to append information to the binary data, such as frame information. Alternately or additionally, the encoding stage transforms the binary data, such as by applying forward error correction that adds redundancies to help information recovery at a receiver. As another example, the encoding stage converts the binary data into symbols. 
     An example modulating stage receives an output generated by the encoding stage as input and embeds the input onto a signal. For instance, the modulating stage generates digital samples of signal(s) embedded with the input from the encoding stage. Thus, in transmitter block  504 , the encoding stage and the modulating stage represent a high-level transmitter processing chain that often includes lower-level complex functions, such as convolutional encoding, serial-to-parallel conversion, cyclic prefix insertion, channel coding, time/frequency interleaving, and so forth. The RF analog Tx stage receives the output from the modulating stage, generates an analog RF signal based on the modulating stage output, and transmits the analog RF signal to receiver block  506 . 
     Receiver block  506  performs complementary processing relative to transmitter block  504  using a receiver processing chain. The receiver processing chain illustrated in receiver block  506  progresses from top to bottom and includes an RF analog receive (Rx) stage, followed by a demodulating stage, and a decoding stage. 
     The RF analog RX stage receives signals transmitted by the transmitter block  504 , and generates a signal used by the demodulating stage. As one example, the RF analog Rx stage includes a down-conversion component and/or an analog-to-digital converter (ADC) to generate samples of the received signal. The demodulating stage processes input from the RF analog RX stage to extract data embedded on the signal (e.g., data embedded by the modulating stage of the transmitter block  504 ). The demodulating stage, for instance, recovers symbols and/or binary data. 
     The decoding stage receives input from the demodulating stage, such as recovered symbols and/or binary data, and processes the input to recover the transmitted information. To illustrate, the decoding stage corrects for data errors based on forward error correction applied at the transmitter block, extracts payload data from frames and/or slots, and so forth. Thus, the decoding stage generates the recovered information. 
     As noted, the transmitter and receiver processing chains illustrated by transmitter block  504  and receiver block  506  have been simplified for clarity and can include multiple complex modules. At times, these modules are specific to particular functions and/or conditions. Consider, for example, a receiver processing chain that processes Orthogonal Frequency Division Modulation (OFDM) transmissions. To recover information from OFDM transmissions, the receiver block  506  oftentimes includes multiple processing blocks, each dedicated to a particular function, such as an equalization block that corrects for distortion in a received signal, a channel estimation block that estimates transmission channel properties to identify the effects on a transmission due to scattering, power decay, and so forth. At high frequencies, such as 5G mmW signals in the 6 GHz band, these blocks can be computationally and/or monetarily expensive (e.g., require substantial processing power, require expensive hardware). Further, implementing blocks that generate outputs with an accuracy within a desired threshold oftentimes requires more specific and less flexible components. To illustrate, an equalization block that functions for 5G mmW signals in the 6 GHz band may not perform with the same accuracy at other frequency bands, thus necessitating different equalization blocks for different bands and adding complexity to the corresponding devices. 
     Some implementations include DNNs in the transmission and/or receiver processing chains. In block diagram  502 , transmitter block  508  includes one or more deep neural network(s)  510  (DNNs  510 ) in the transmitter processing chain, while receiver block  512  includes one or more deep neural network(s)  514  (DNNs  514 ) in the receiver processing chain. 
     For simplicity, the DNNs  510  in the transmitter block  508  correspond to the encoding stage and the modulating stage of transmitter block  504 . The DNNs  510 , however, can perform any high-level and/or low-level operation found within the transmitter processing chain. For instance, a first DNN performs low-level transmitter-side forward error correction, a second DNN performs low-level transmitter-side convolutional encoding, and so forth. Alternately or additionally, the DNNs  510  perform high-level processing, such as end-to-end processing that corresponds to the encoding stage and the modulating stage of transmitter block  508 . 
     In a similar manner, the DNNs  514  in receiver block  512  perform receiver processing chain functionality (e.g., demodulating stage, decoding stage). The DNNs  514  can perform any high-level and/or low-level operation found within the receiver processing chain, such as low-level receiver-side bit error correction, low-level receiver-side symbol recovery, high-level end-to-end demodulating and decoding, etc. Accordingly, DNNs  514  in wireless communication systems can be configured to replace high-level operations and/or low-level operations in transmitter and receiver processing chains. At times, the DNNs  514  performing the high-level operations and/or low-level operations can be configured and/or reconfigured based on a current operating environment as further described. This DNN reconfigurability, along with DNN coefficient updates, provides more flexibility and adaptability to the processing chains relative to the more specific and less flexible components. 
     Some implementations process communication exchanges over the wireless communication system using multiple DNNs, where each DNN has a respective purpose (e.g., uplink processing, downlink processing, uplink encoding processing, downlink decoding processing, etc.). To demonstrate,  FIG. 6  illustrates an example operating environment  600  that includes UE  110  and base station  120 . In implementations, the UE  110  and base station  120  exchange communications with one another over a wireless communication system by processing the communications using multiple DNNs. 
     In  FIG. 6 , the base station neural network manager  268  of the base station  120  includes a downlink processing module  602  for processing downlink communications, such as for generating downlink communications transmitted to the UE  110 . To illustrate, the base station neural network manager  268  forms deep neural network(s)  604  (DNNs  604 ) in the downlink processing module  602  using NN formation configurations as further described. In some examples, the DNNs  604  correspond to the DNNs  510  of  FIG. 5 . In other words, the DNNs  604  perform some or all of the transmitter processing functionality used to generate downlink communications. 
     Similarly, the UE neural network manager  218  of the UE  110  includes a downlink processing module  606 , where the downlink processing module  606  includes deep neural network(s)  608  (DNNs  608 ) for processing (received) downlink communications. In various implementations, the UE neural network manager  218  forms the DNNs  608  using NN formation configurations. In  FIG. 6 , the DNNs  608  correspond to the DNNs  514  of  FIG. 5 , where the deep neural network(s)  606  of UE  110  perform some or all receiver processing functionality for (received) downlink communications. Accordingly, the DNNs  604  and the DNNs  608  perform complementary processing to one another (e.g., encoding/decoding, modulating/demodulating). 
     The DNNs  604  and/or DNNs  608  can include multiple deep neural networks, where each DNN is dedicated to a respective channel, a respective purpose, and so forth. The base station  120 , as one example, processes downlink control channel information using a first DNN of the DNNs  604 , processes downlink data channel information using a second DNN of the DNNs  604 , and so forth. As another example, the UE  110  processes downlink control channel information using a first DNN of the DNNs  608 , processes downlink data channel information using a second DNN of the DNNs  608 , etc. 
     The base station  120  and/or the UE  110  also process uplink communications using DNNs. In environment  600 , the UE neural network manager  218  includes an uplink processing module  610 , where the uplink processing module  610  includes deep neural network(s)  612  (DNNs  612 ) for generating and/or processing uplink communications (e.g., encoding, modulating). In other words, uplink processing module  610  processes pre-transmission communications as part of processing the uplink communications. The UE neural network manager  218 , for example, forms the DNNs  612  using NN formation configurations. At times, the DNNs  612  correspond to the DNNs  510  of  FIG. 5 . Thus, the DNNs  612  perform some or all of the transmitter processing functionality used to generate uplink communications transmitted from the UE  110  to the base station  120 . 
     Similarly, uplink processing module  614  of the base station  120  includes deep neural network(s)  616  (DNNs  616 ) for processing (received) uplink communications, where base station neural network manager  268  forms DNNs  616  using NN formation configurations as further described. In examples, the DNNs  616  of the base station  120  correspond to the DNNs  514  of  FIG. 5 , and perform some or all receiver processing functionality for (received) uplink communications, such as uplink communications received from UE  110 . At times, the DNNs  612  and the DNNs  616  perform complementary functionality of one another. Alternately or additionally, the uplink processing module  610  and/or the uplink processing module  614  include multiple DNNs, where each DNN has a dedicated purpose (e.g., processes a respective channel, performs respective uplink functionality, and so forth).  FIG. 6  illustrates the DNNs  604 ,  608 ,  612 , and  616  as residing within the respective neural network managers to signify that the neural network managers form the DNNs, however, the DNNs can be formed external to the neural network managers (e.g., UE neural network manager  218  and base station neural network manager  268 ) within different components, processing chains, modules, etc. 
     Having described deep neural networks in wireless communication systems, consider now a discussion of signaling and control transactions over a wireless communication system that can be used to configure deep neural networks for downlink and uplink communications that is in accordance with one or more implementations. 
     Signaling and Control Transactions to Configure Deep Neural Networks 
       FIG. 7  illustrates an example signaling and control transaction diagram  700  between a base station and a user equipment in accordance with one or more aspects of neural network formation configurations in wireless communication. Alternate or additional implementations include transactions that include a core network server. For example, the core network server  302  performs, in some implementations, various signaling and control actions performed by the base station  120  as illustrated by  FIG. 7 . The signaling and control transactions may be performed by the base station  120  and the UE  110  of  FIG. 1  using elements of  FIGS. 1-6 . For clarity,  FIG. 7  omits the core network server  302 , but alternate implementations include the core network server as further described. 
     As illustrated, at  705 , the UE  110  optionally indicates UE capabilities (e.g., capabilities supported by the UE) to a network entity, such as the base station  120 . In some implementations, the UE capabilities include ML-related capabilities, such as a maximum kernel size capability, a memory limitation, a computation capability, supported ML architectures, supported number of layers, available processing power, memory limitation, available power budget, and fixed-point processing versus floating point processing. At times, the base station forwards the UE capabilities to a core network server (e.g., the core network server  302 ). 
     At  710  the base station  120  determines a neural network formation configuration. In determining the neural network formation configuration, the base station analyzes any combination of information, such as a channel type being processed by the deep neural network (e.g., downlink, uplink, data, control, etc.), transmission medium properties (e.g., power measurements, signal-to-interference-plus-noise ratio (SINR) measurements, channel quality indicator (CQI) measurements), encoding schemes, UE capabilities, BS capabilities, and so forth. In some implementations, the base station  120  determines the neural network formation configuration based upon the UE capabilities indicated at  705 . Alternately or additionally, the base station  120  obtains the UE capabilities from a networked storage device, such as a server. In some implementations, the core network server  302  determines the neural network formation configuration in manner(s) similar to that described with respect to the base station, and communicates the determined neural network formation configuration to the base station. 
     The base station  120 , for instance, receives message(s) from the UE  110  (not shown) that indicates one or more capabilities of the UE, such as, by way of example and not of limitation, connectivity information, dual connectivity information, carrier aggregation capabilities, downlink physical parameter values, uplink physical parameter values, supported downlink/uplink categories, inter-frequency handover, and ML-capabilities (e.g., a maximum kernel size capability, a memory limitation, a computation capability, supported ML architectures, supported number of layers, available processing power, memory limitation, available power budget, fixed-point processing vs. floating point processing). The base station  120  (and/or the core network server  302 ) identifies, from the message(s), the UE capabilities that impact how the UE processes communications, and/or how the base station processes communications from the UE and selects a neural network formation configuration with improved output accuracy relative to other neural network formation configurations. 
     In some implementations, the base station  120  (and/or the core network server  302 ) selects the neural network formation configuration from multiple neural network formation configurations. Alternately or additionally, the base station  120  (and/or the core network server  302 ) selects the neural network formation configuration by selecting a subset of neural network architecture formation elements in a neural network table. At times, the base station  120  (and/or the core network server  302 ) analyzes multiple neural network formation configurations and/or multiple neural network formation configuration elements included in a neural network table, and determines the neural network formation configuration by selects and/or creates a neural network formation configuration that aligns with current channel conditions, such as by matching the channel type, transmission medium properties, etc., to input characteristics as further described. Alternately or additionally, the base station  120  (and/or the core network server  302 ) selects the neural network formation configuration based on network parameters, such as scheduling parameters (e.g., scheduling Multiple User, Multiple Input, Multiple Output (MU-MIMO) for downlink communications, scheduling MU-MIMO for uplink communications). 
     At  715 , the base station  120  communicates the neural network formation configuration to the UE  110 . Alternately or additionally, the core network server  302  communicates the neural network formation configuration to the base station  120 , and the base station  120  forwards the neural network formation configuration to the UE  110 . In some implementations, the base station transmits a message that specifies the neural network formation configuration, such as by transmitting a message that includes an index value that maps to an entry in a neural network table, such as neural network table  216  of  FIG. 2 . Alternately or additionally, the base station transmits a message that includes neural network parameter configurations (e.g., weight values, coefficient values, number of filters). In some cases, the base station  120  specifies a purpose and/or processing assignment in the message, where the processing assignment indicates what channels, and/or where in a processing chain, the configured neural network applies to, such as a downlink control channel processing, an uplink data channel processing, downlink decoding processing, uplink encoding processing, etc. Accordingly, the base station can communicate a processing assignment with a neural network formation configuration. 
     In some implementations, the base station  120  communicates multiple neural network formation configurations to the UE  110 . For example, the base station transmits a first message that directs the UE to use a first neural network formation configuration for uplink encoding, and a second message that directs the UE to use a second neural network formation configuration for downlink decoding. In some scenarios, the base station  120  communicates multiple neural network formation configurations, and the respective processing assignments, in a single message. As yet another example, the base station communicates the multiple neural network formation configurations using different radio access technologies (RATs). The base station can, for instance, transmit a first neural network formation configuration for downlink communication processing to the UE  110  using a first RAT and/or carrier, and transmit a second neural network formation configuration for uplink communication processing to the UE  110  using a second RAT and/or carrier. 
     At  720 , the UE  110  forms a first neural network based on the neural network formation configuration. For instance, the UE  110  accesses a neural network table using the index value(s) communicated by the base station to obtain the neural network formation configuration and/or the neural network formation configuration elements. Alternately or additionally, the UE  110  extracts neural network architecture and/or parameter configurations from the message. The UE  110  then forms the neural network using the neural network formation configuration, the extracted architecture and/or parameter configurations, etc. In some implementations, the UE processes all communications using the first neural network, while in other implementations, the UE processes select communications using the first neural network based on a processing assignment (e.g., a downlink control channel processing assignment, an uplink data channel processing assignment, downlink decoding processing assignment, uplink encoding processing assignment). 
     At  725 , the base station  120  communicates information based on the neural network formation configuration. For instance, with reference to  FIG. 6 , the base station  120  processes downlink communications using a second neural network configured with complementary functionality to the first neural network. In other words, the second neural network uses a second neural network formation configuration complementary to the neural network formation configuration. In turn, at  730 , the UE  110  recovers the information using the first neural network. 
     Having described signaling and control transactions that can be used to configure neural networks for processing communications, consider now a discussion of generating and communicating neural network formation configurations that is in accordance with one or more implementations. 
     Generating and Communicating Neural Network Formation Configurations 
     In supervised learning, machine-learning modules process labeled training data to generate an output. The machine-learning modules receive feedback on an accuracy of the generated output and modify processing parameters to improve the accuracy of the output. FIG.  8  illustrates an example  800  that describes aspects of generating multiple NN formation configurations. At times, various aspects of the example  800  are implemented by any combination of training module  270 , base station neural network manager  268 , core network neural network manager  312 , and/or training module  314  of  FIGS. 2 and 3 . 
     The upper portion of  FIG. 8  includes machine-learning module  400  of  FIG. 4 . In implementations, a neural network manager determines to generate different NN formation configurations. To illustrate, consider a scenario in which the base station neural network manager  268  determines to generate a NN formation configuration by selecting a combination of NN formation configuration elements from a neural network table, where the NN formation configuration corresponds to a UE decoding and/or demodulating downlink communications. In other words, the NN formation configuration (by way of the combination of NN formation configuration elements) forms a DNN that processes downlink communications received by a UE. Oftentimes, however, transmission channel conditions vary which, in turn, affects the characteristics of the downlink communications. For instance, a first transmission channel distorts the downlink communications by introducing frequency offsets, a second transmission channel distorts the downlink communications by introducing Doppler effects, a third transmission channel distorts the downlink communications by introducing multipath channel effects, and so forth. To accurately process the downlink communications (e.g., reduce bit errors), various implementations select multiple NN formation configurations, where each NN formation configuration (and associated combination of NN formation configuration elements) corresponds to a respective input condition, such as a first transmission channel, a second transmission channel, etc. 
     Training data  802  represents an example input to the machine-learning module  400 . In  FIG. 8 , the training data  802  represents data corresponding to a downlink communication. Training data  802 , for instance, can include digital samples of a downlink communications signal, recovered symbols, recovered frame data, etc. In some implementations, the training module generates the training data mathematically or accesses a file that stores the training data. Other times, the training module obtains real-world communications data. Thus, the training module can train the machine-learning module  400  using mathematically generated data, static data, and/or real-world data. Some implementations generate input characteristics  804  that describe various qualities of the training data, such as transmission channel metrics, UE capabilities, UE velocity, and so forth. 
     Machine-learning module  400  analyzes the training data, and generates an output  806 , represented here as binary data. Some implementations iteratively train the machine-learning module  400  using the same set of training data and/or additional training data that has the same input characteristics  804  to improve the accuracy of the machine-learning module  400 . During training, the machine-learning module  400  modifies some or all of the architecture and/or parameter configurations of a neural network included in the machine-learning module  400 , such as node connections, coefficients, kernel sizes, etc. At some point in the training, the training module determines to extract the architecture and/or parameter configurations  808  of the neural network (e.g., pooling parameter(s), kernel parameter(s), layer parameter(s), weights), such as when the training module determines that the accuracy meets or exceeds a desired threshold, the training process meets or exceeds an iteration number, and so forth. The training module then extracts the architecture and/or parameter configurations from the machine-learning module  400  to use as a NN formation configuration and/or NN formation configuration element(s). The architecture and/or parameter configurations can include any combination of fixed architecture and/or parameter configurations, and/or variable architectures and/or parameter configurations. 
     The lower portion of  FIG. 8  includes neural network table  810  that represents a collection of NN formation configuration elements, such as neural network table  216 , neural network table  272 , and/or neural network table  316  of  FIG. 2  and  FIG. 3 . The neural network table  810  stores various combinations of architecture configurations, parameter configurations  808 , and input characteristics  804 , but alternate implementations exclude the input characteristics  804  from the table. Various implementations update and/or maintain the NN formation configuration elements and/or the input characteristics  804  as the machine-learning module  400  learns additional information. For example, at index  812 , the neural network manager and/or the training module updates neural network table  810  to include architecture and/or parameter configurations  808  generated by the machine-learning module  400  while analyzing the training data  802 . 
     The neural network manager and/or the training module alternately or additionally adds the input characteristics  804  to the neural network table  810  and links the input characteristics  804  to the architecture and/or parameter configurations  808 . This allows the input characteristics  804  to be obtained at a same time as the architecture and/or parameter configurations  808 , such as through using an index value that references into the neural network table  810  (e.g., references NN formation configurations, references NN formation configuration elements). In some implementations, the neural network manager selects a NN formation configuration by matching the input characteristics to a current operating environment, such as by matching the input characteristics to current channel conditions, UE capabilities, UE characteristics (e.g., velocity, location, etc.) and so forth. 
     Having described generating and communicating neural network formation configurations, consider now a discussion of E2E ML for wireless networks that is in accordance with one or more implementations. 
     E2E ML for Wireless Networks 
     Aspects of an end-to-end communication (E2E communication) involve two endpoints exchanging information over a communication path, such as through a wireless network. At times, the E2E communication performs a single-directional exchange of information, where a first endpoint sends information and a second endpoint receives the information. Other times, the E2E communication performs bi-directional exchanges of information, where both endpoints send and receive the information. The endpoints of an E2E communication can include any entity capable of consuming and/or generating the information, such as a computing device, an application, a service, and so forth. To illustrate, consider an example in which an application executing at a UE exchanges information with a remote service over a wireless network. For this example, the E2E communication corresponds to the communication path between the application and the remote service, where the application and the remote service act as endpoints. 
     While the E2E communication involves endpoints that exchange information, the E2E communication alternately or additionally includes intermediate, entities (e.g., devices, applications, services) that participate in the exchange of information. To illustrate, consider again the example of an E2E communication established through a wireless network where an application at a UE functions as a first endpoint and a remote service functions as a second endpoint. In establishing the E2E communication between the endpoints, the wireless network utilizes any combination of UE(s), base station(s), core network server(s), remote network(s), remote service(s), and so forth, such as that described with reference to the environment  100  of  FIG. 1 . Thus, intermediary entities, such as the base station  120  and the core network  150 , participate in establishing the E2E communication and/or participating in the E2E communication to enable an exchange of information between the endpoints. 
     Different factors impact the operational efficiency of the E2E communication and how the network elements process information exchanged through the E2E communication. For instance, with reference to an E2E communication established using a wireless network, a current operating environment (e.g., current channel conditions, UE location, UE movement, UE capabilities) impacts how accurately (e.g., bit error rate, packet loss) a receiving endpoint recovers the information. As one example, an E2E communication implemented using 5G mmW technologies becomes susceptible to more signal distortions relative to lower frequency sub-6 GHz signals as further described. 
     As another example, various implementations partition wireless network resources differently based on an end-to-end analysis of an E2E communication, where the wireless network resources include any combination of, by way of example and not of limitation, physical hardware, physical spectrum, logical channels, network functions, services provided, quality of service, latency, and so forth. Wireless network-resource partitioning allows the wireless network to dynamically allocate the wireless network resources based on an expected usage to improve an efficiency of how the wireless network resources are used (e.g., reduce the occurrence of unused and/or wasted resources). To illustrate, consider a variety of devices connecting to a wireless network, where the devices have different performance requirements relative to one another (e.g., a first device has secure data transfer requirements, a second device has high priority/low latency data transfer requirements, a third device has high data rate requirements). For at least some devices, a fixed and/or static distribution of wireless network resources (e.g., a fixed configuration for the wireless network resources used to implement an E2E communication) can lead to unused resources and/or fail to meet the performance requirements of some services. Thus, partitioning the wireless network resources can improve an overall efficiency of how the wireless network resources are utilized. However, the partitioning causes variations in how one pair of E2E endpoints exchanges information relative to a second pair of E2E endpoints. 
     A Quality-of-Service flow (QoS flow) corresponds to information exchanged in a wireless network. At times, an E2E communication includes and/or corresponds to a QoS flow. Some wireless networks configure a QoS flow with operating rules, priority levels, classifications, and so forth, that influence how information is exchanged through the QoS flow. For example, a QoS profile indicates to a wireless network the QoS parameters and/or QoS characteristics of a particular QoS flow, such as a Guaranteed Flow Bit Rate (GFBR) parameter used to indicate an uplink and/or downlink guaranteed bit rate for the QoS flow, a Maximum Flow Bit Rate (MFBR) parameters used to indicate a maximum uplink and/or downlink bit rate for the QoS flow, an Allocation and Retention Priority (ARP) parameter that indicates a priority level, a pre-emption capability, and/or pre-emption vulnerability of the QoS flow, a Reflective QoS attribute (RQA) that indicates a type of traffic carried on the QoS flow is subject to Reflective QoS (e.g., implicit updates), a Notification Control parameter that indicates whether notifications are requested when a guaranteed flow bit rate cannot be guaranteed, or resumes, for the QoS flow, an aggregate bit rate parameter that indicates an expected aggregate bit rate for the collective non-guaranteed-bit-rate (Non-GBR) flows associated with a particular UE, default parameters for 5Q1 and ARP priority levels, a Maximum Packet Loss Rate (MPLR) for uplink and/or downlink that indicates a maximum rate for lost packets of the QoS flow, a Resource Type characteristic that indicates types of resources that can be used by the QoS flow (e.g., GBR resource type, Delay-critical GBR resource type, non-GBR resource type), a scheduling priority level characteristic that distinguishes between multiple QoS flows of a same UE, a Packet Delay Budget characteristic that provides an upper bound to how long a packet may be delayed, a Packet Error Rate characteristic that indicates an upper bound for a rate of PDUs unsuccessfully received, an Averaging Window characteristics that indicates a window of data over which to calculate the GFBR and/or MFBR, a Maximum Data Burst Volume characteristic that indicates a largest amount of data that is required to be served over a pre-defined time period, and so forth. In some implementations, the parameters and/or characteristics that specify the configuration of a QoS flow can be pre-configured (e.g., default) and/or dynamically communicated, such as through the QoS profile. These variations impact how the wireless network partitions the various wireless network resources to support the QoS flow configuration. 
     For example, a UE can include three applications, where each application has a different performance requirement (e.g., resource type, priority level, packet delay budget, packet error rate, maximum data burst volume, averaging window, security level). These different performance requirements cause the wireless network to partition the wireless network resources assigned to the respective QoS flows assigned to each application differently from one another. 
     To demonstrate, consider a scenario in which the UE includes a gaming application, an augmented reality application, and a social media application. In some instances, the gaming application interacts with a remote service (through the data network) to connect with another gaming application to exchange audio in real-time, video in real-time, commands, views, and so forth, such that the gaming application has performance requirements with high data volume and low latency. The augmented reality application also interacts with a remote service through the data network to transmit location information and subsequently receive image data that overlays on top of a camera image generated at the UE. Relative to the gaming application, the augmented reality application utilizes less data, but has some time-sensitivity to maintain synchronization between a current location and a corresponding image overlay. Finally, the social media application interacts with a remote service through the data network to receive feed information, where the feed information has less data volume and time-criticality relative to data consumed by the augmented reality application and/or the gaming application. 
     Based upon these performance requirements, the wireless network establishes QoS flows between the applications and a data network, where the wireless network constructs each QoS flow based on QoS requirements, QoS parameters and/or QoS characteristics (e.g., resource type, priority level, packet delay budget, packet error rate, maximum data burst volume, averaging window, security level) that indicate a high data volume performance requirement and a time-sensitivity performance requirement. In implementations, the QoS requirements, the QoS parameters and/or the QoS characteristics included in a QoS profile correspond to the performance requirements of the QoS flow. As one example, the wireless network processes a QoS profile associated with a first QoS flow that configures any combination of a GFBR parameter, a Maximum Data Burst Volume characteristic, an ARP parameter, and so forth. The wireless network then constructs the QoS flow by partitioning the wireless network resources based on the QoS parameters and/or characteristics. 
     While the configurability of the QoS flows provide flexibility to the wireless network to dynamically modify how the wireless network resources are allocated, the configurability adds complexity in how the wireless network processes information that is exchanged between the endpoints. Some implementations train DNNs to perform some or all of the complex processing associated with exchanging information using E2E communications with various configurations. By training a DNN on the differing processing chain operations and/or wireless network resource partitioning, the DNN can replace the conventional complex functionality as further described. The usage of DNNs in an E2E communication also allows a network entity to adapt the DNN to changing operating conditions, such as by modifying various parameter configurations (e.g., coefficients, layer connections, kernel sizes). 
     One or more implementations determine an E2E ML configuration for processing information exchanged through an E2E communication. In some cases, an end-to-end machine-learning controller (E2E ML controller) obtains capabilities of device(s) associated with end-to-end communications in a wireless network, such as machine-learning (ML) capabilities of device(s) participating in the E2E communication, and determines an E2E ML configuration based on the ML capabilities (e.g., supported ML architectures, supported number of layers, available processing power, memory limitation, available power budget, fixed-point processing vs. floating point processing, maximum kernel size capability, computation capability) of the device(s). Alternately or additionally, the E2E ML controller identifies a current operating environment and determines the E2E ML configuration based on the current operating environment. Some implementations of the E2E ML controller communicate with a network-slice manager to determine an E2E ML configuration that corresponds to a network slice (e.g., a partitioning of wireless network resources). In determining the E2E ML configuration, some implementations of the E2E ML controller partition the E2E ML configuration based on the device(s) participating in the E2E communication and communicate a respective partition of the E2E ML configuration to each respective device. 
     To demonstrate, consider  FIG. 9  that illustrates an example environment  900  in which example E2E ML configurations for E2E communications include DNNs operating at multiple devices. In implementations, the example environment  900  illustrates aspects of machine-learning architectures for broadcast and multicast communications. The environment  900  includes the UE  110 , the base station  120 , and the remote service  170  of  FIG. 1 , and the core network server  302  of  FIG. 3 . In implementations, the UE  110  and the remote service  170  exchange information with one another using E2E communication  902  and E2E communication  904 . For clarity, the E2E communications  902  and  904  are illustrated as being separate and single-directional E2E communications such that the exchange of information over each communication E2E corresponds to one direction (e.g., E2E communication  902  includes uplink transmissions, E2E communication  904  includes downlink transmissions), but in alternate or additional implementations, the E2E communication  902  and the E2E communication  904  correspond to a single, bi-directional E2E communication that includes both, signified in the environment  900  with a dashed line  906 . In implementations, the E2E communication  902  and the E2E communication  904  (in combination) correspond to a single QoS flow. 
     The environment  900  also includes the E2E ML controller  318  that is implemented by the core network server  302 , where the E2E ML controller  318  determines an E2E ML configuration for the E2E communication  902  and/or the E2E communication  904 . In some implementations, the E2E ML controller determines a first E2E ML configuration for the E2E communication  902  and a second E2E ML configuration for the E2E communication  904 , such as when each E2E communication corresponds to single-directional information exchanges. In other implementations, the E2E ML controller determines an E2E ML configuration for a bi-directional E2E communication that includes both E2E communications  902  and  904 . For example, in response to the UE  110  requesting a connection to the remote server  170 , such as through the invocation of an application, the E2E ML controller determines an E2E ML configuration for a corresponding connection based on any combination of ML capabilities of the UE  110  (e.g., supported ML architectures, supported number of layers, processing power available for ML processing, memory constraints applied to ML processing, power budget available for ML processing, fixed-point processing vs. floating point processing), performance requirements associated with the requested connection (e.g., resource type, priority level, packet delay budget, packet error rate, maximum data burst volume, averaging window, security level), available wireless network resources, ML capabilities of intermediary devices (e.g., the base station  120 , the core network server  302 ), a current operating environment (e.g., channel conditions, UE location), and so forth. As one example, the E2E ML controller  318 , by way of the core network server  302 , receives base station metrics and/or UE metrics that describe the current operating environment. As another example, the E2E ML controller, by way of the core network server  302 , receives base station ML capabilities and/or UE capabilities. Alternately or additionally, the E2E ML controller communicates with the network-slice manager  320  to identify a network slice that partitions the wireless network resources in a manner that supports QoS requirement(s). 
     In one or more implementations, the E2E ML controller  318  analyzes a neural network table based upon any combination of the device capabilities, the wireless network resource partitioning, the operating parameters, the current operating environment, the ML capabilities, and so forth, to determine the E2E ML configuration. While described as being implemented by the core network server  302 , in alternate or additional implementations, the E2E ML controller  318  may be implemented by another network entity, such as the base station  120 . 
     To illustrate, and with reference to  FIG. 8 , the training module  270  and/or the training module  314  train the machine-learning module  400  with variations of the training data  802  that reflect different combinations of the capabilities, wireless network resource partitioning, the operating parameters, the current operating environment, and so forth. The training module extracts and stores the architecture and/or parameter configurations (e.g., architecture and/or parameter configurations  808 ) in a neural network table such that, at a later point in time, the E2E ML controller  318  accesses the neural network table to obtain and/or identify neural network formation configurations that correspond to a determined E2E ML configuration. The E2E ML controller  318  then communicates the neural network formation configurations to various devices and directs the respective device to form a respective DNN as further described. 
     In determining the E2E ML configuration, the E2E ML controller  318  sometimes partitions the E2E ML configuration based on devices participating in the corresponding E2E communication. For example, the E2E ML controller  318  determines a first partition of the E2E ML configuration that corresponds to processing information at the UE  110 , a second partition of the E2E ML configuration that corresponds to processing information at the base station  120 , and a third partition of the E2E ML configuration that corresponds to processing information at the core network server  302 , where determining the partitions can be based on any combination of the capabilities, wireless network resource partitioning, the operating parameters, the current operating environment, and so forth. 
     As one example, consider an E2E communication that corresponds to voice transmissions over a wireless network, such as the E2E communication  902 , the E2E communication  904 , and/or a combination of both E2E communications. In determining an E2E ML configuration for the E2E communication, the E2E ML controller  318  alternately or additionally identifies that performance requirement(s) of the E2E communication indicates large volumes of data transfer with low latency requirements. When given performance requirement(s), the E2E ML controller identifies an E2E ML configuration that, when formed by the respective DNN(s), performs end-to-end functionality that exchanges voice communications and satisfies the performance requirement(s). To illustrate, the E2E ML controller determines an E2E ML configuration that performs end-to-end functionality for transmitting voice from a UE to a core network server, such as signal processing, voice encoding, channel encoding, and/or channel modulation at the UE side, channel decoding, demodulation, and/or signal processing at the base station side, decoding voice at the core network server side, and so forth, and selects a configuration designed to satisfy the performance requirements. 
     Some implementations partition an E2E ML configuration based the ML capabilities of devices participating in the E2E communication and/or the performance requirements. A UE, for instance, may have less processing resources (e.g., processing capabilities, memory constraints, quantization constraints, fixed-point vs. floating point computations, FLOPS, power availability relative to a base station and/or a core network server, which can be indicated through the ML capabilities. In response to identifying the different processing resources through an analysis of the ML capabilities, the E2E ML controller partitions the E2E ML configuration such that a first partition (e.g., at the UE  110 ) forms a DNN that performs less processing than a DNN formed by a second or third partition (e.g., at the base station, at the core network server). Alternately or additionally, the E2E ML controller partitions the E2E ML configuration to produce neural networks designed to not exceed device capabilities. For example, when provided with UE capabilities, the E2E ML controller directs the UE to form a DNN with less layers and a smaller kernel size relative to a DNN formed by the base station and/or the core network server based on processing constraints of the UE. Alternately or additionally, the E2E ML controller partitions the E2E ML configuration to form, at the UE) a neural network with an architecture (e.g., a convolutional neural network, a long short-term memory (LSTM) network, partially connected, fully connected) that processes information without exceeding memory constraints of the UE. In some instances, the E2E ML controller calculates whether an amount of computation performed at each device collectively meets a performance requirement corresponding to a latency budget and determines an E2E ML configuration designed to meet the performance requirement. 
     In the environment  900 , the E2E ML controller  318  determines a first E2E ML configuration for processing information exchanged through the E2E communication  902  and determines to partition the first E2E ML configuration across multiple devices such as by partitioning the first E2E ML configuration across the UE  110 , the base station  120 , and the core network server  302  based on device capabilities. In other words, some implementations determine an E2E ML configuration that corresponds to a distributed DNN in which multiple devices implement and/or form portions of the DNN. To communicate the partitioning, the E2E ML controller  318  identifies a first neural network formation configuration (NN formation configuration) that corresponds to a first partition of the E2E ML configuration and communicates, by using the core network server  302 , the first NN formation configuration to the UE  110 . The E2E ML controller  318  and/or the core network server  302  then directs the UE to form a user equipment-side deep neural network  908  (UE-side DNN  908 ) for processing information exchanged through the E2E communication  902 . Similarly, the E2E ML controller  318  identifies a second NN formation configuration that corresponds to a second partition of the E2E ML configuration and communicates the second NN formation configuration to the base station  120 . The E2E ML controller  318  and/or the core network server  302  then directs the base station  120  to form, using the second NN formation configuration, a base station-side deep neural network  910  (B S-side DNN  910 ) for processing information exchanged through the E2E communication  902 . The E2E ML controller  318  also identifies and communicates a third NN formation configuration to the core network server  302  to use in forming a core network server-side deep neural network  912  (CNS-side DNN  912 ) for processing information exchanged through the E2E communication  902 . 
     In implementations, the E2E ML controller  318  partitions the E2E ML configuration to distribute processing computations performed over the E2E communication such that the UE-side DNN  908  performs less processing relative to the BS-side DNN  910  (e.g., a UE-side DNN  908  that uses less layers, less data processing points, and so forth, relative to the BS-side DNN  910 ). Alternately or additionally, the E2E ML controller  318  partitions the E2E ML configuration such that the B S-side DNN  910  performs less processing relative to CNS-side DNN  912 . In combination, the processing performed by the UE-side DNN  908 , the BS-side DNN  910 , and the CNS-side DNN  912  exchange information across the E2E communication  902 . 
     In a similar manner, the E2E ML controller  318  determines a second E2E ML configuration for processing information exchanged through the E2E communication  904 , where the E2E ML controller partitions and/or distributes the second E2E ML configuration across multiple devices. In the environment  900 , this partitioning corresponds to a core network server-side deep neural network  914  (CNS-side DNN  914 ), a base station-side deep neural network  916  (BS-side DNN  916 ), and a user equipment-side deep neural network  918  (UE-side DNN  918 ). In combination, the processing performed by the CNS-side DNN  914 , the B S-side DNN  916 , and the UE-side DNN  918  corresponds to exchanging information using the E2E communication  904 . While the E2E ML controller determines the first and second E2E ML configurations separately in the environment  900  for single-directional E2E communications (e.g., the E2E communications  902  and  904 ), in alternate or additional implementations, the E2E ML controller  318  determines a single E2E ML configuration that corresponds to exchanging bi-directional information using an E2E communication. Accordingly, with respect to the E2E communication  902  and/or the E2E communication  904 , the E2E ML controller  318  determines a partitioned E2E ML configuration and communicates respective portions of the partitioned E2E ML configuration to the devices participating in the E2E communication  902  and/or the E2E communication  904 . 
     In implementations, the E2E ML controller  318  periodically reassess metrics, performance requirements, wireless link performance, processing capabilities of devices or other aspects affecting, or providing an indication of, a current operating environment and/or a current performance (e.g., bit errors, BLER) to determine whether to update the E2E ML configuration. For example, the E2E ML controller  318  determines modifications (e.g., parameter changes) to an existing DNN to better accommodate the performance requirements of devices, applications, and/or transmissions in a wireless network. A UE changing location may impact on the wireless link performance, or a user opening an application at the UE may reduce the processing capability the user equipment can provide for machine learning. By reassessing dynamically changing conditions (e.g., changes in the operating environment, changes in the devices), the E2E ML controller can modify or update the E2E ML configuration to improve an overall efficiency of how the wireless network resources are utilized. 
     Having described E2E ML for wireless networks, consider now a discussion of machine-learning architectures for machine-learning architectures for simultaneous connection to multiple carriers that are in accordance with one or more implementations. 
     Machine-Learning Architectures for Simultaneous Connection to Multiple Component Carriers 
     In various implementations, a wireless communication system uses multiple component carriers to propagate information between devices. As one example, a base station transmits data to a receiving UE over multiple component carriers (simultaneously) as a way to increase the throughput of data between the base station and UE. As another example, a UE establishes a first connection to a first base station with a first component carrier, and a second connection to a second base station with a second component carrier to increase data throughput. While exchanging information over multiple component carriers increases data throughput between devices, the use of multiple component carriers also increases the complexity of the communications. A first UE receiving information over multiple component carriers, for instance, manages the information with more complex synchronization operations relative to a second UE receiving information over a single component carrier. Synchronization at the base station also increases in complexity, such as when the base station manages the multiple component carriers or synchronizes the communications with a second base station. 
       FIG. 10  illustrates an example environment  1000  that can utilize machine-learning architectures for simultaneous connection to multiple carriers in accordance with one or more implementations, such as carrier aggregation, dual connectivity, or other split architecture communications. The environment  1000  includes the base station  120  and UE  110  of  FIG. 1 , where the base station  120  and the UE  110  exchange information using carrier aggregation by way of downlink communications  1002  and uplink communications  1004 . Downlink communications  1002  transmit information to the UE  110  using three component carriers: component carrier  1006 , component carrier  1008 , and component carrier  1010 . Similarly, uplink communications  1004  transmit information to the base station  120  using two component carriers: component carrier  1012  and component  1014 . For clarity, each component carrier illustrated in the environment  1000  corresponds to a single-directional communication link (e.g., downlink only, uplink only). However, in alternate or additional implementations, a component carrier corresponds to a bi-directional communication link, such as a communication link that includes a downlink component and an uplink component. Further, while the environment  1000  illustrates the base station and the UE each using carrier aggregation, other implementations can include one-way carrier aggregation that corresponds using carrier aggregation in a single direction for the information transfer (e.g., only downlink carrier aggregation, only uplink carrier aggregation). 
     Carrier aggregation (CA) adds more bandwidth to a communication link between devices by transferring information using multiple component carriers. The base station  120 , for instance, increases a number of aggregated component carriers to increase an amount of information exchanged. At a given instance in time, a receiving device (e.g., UE  110 ) obtains the information from the multiple component carriers in a more-timely fashion relative to obtaining the information using a single component carrier. In CA, a protocol stack at the base station  120  manages aspects of the component carrier  1006 , the component carrier  1008 , and the component carrier  1010 , such as which component carrier carries what information. The protocol stack at the base station  120 , as one example, manages splitting user traffic amongst the component carrier  1006 , the component carrier  1008 , and the component carrier  1010 . Further, in CA, the (aggregated) component carriers use a common cell-radio network temporary identifier (C-RNTI) between one another. 
     Image  1016 , image  1018 , and image  1020  illustrate three example configurations that can be utilized for CA. Image  1016  depicts a contiguous, intra-band configuration of the component carrier  1006 , the component carrier  1008 , and the component carrier  1010  in the frequency domain, where each component carriers used in the CA resides in a same band. The contiguous placement of each component carrier simplifies the transmissions but can be difficult to obtain based on other active communications residing in the contiguous spectrum. Image  1018  depicts a non-contiguous, intra-band configuration, where at least one component carrier used in the carrier-aggregation-communications resides in the same band as the other component carriers, but at a non-contiguous location. This allows a device selecting the component carriers, such as the base station  120 , to use the wireless network resources more efficiently by selecting unused component carriers for the carrier aggregations. Image  1020  depicts a non-contiguous, inter-band configuration, where at least one component carrier used in the carrier-aggregation-communications resides in a different frequency band. For example, in various implementations, band  1  of image  1020  corresponds to licensed bands allocated to the base station  120  and band  2  of image  1020  corresponds to unlicensed bands used by the base station  120 , such as frequency bands accessed through Licensed-Assisted Access (LAA). As another example, band  1  of image  1020  can correspond to 5G mmW communications, and band  2  of image  1020  can correspond to narrow-band cellular IoT communications (e.g., different communication types with different bandwidths). For clarity,  FIG. 10  illustrates a split with two bands, but alternate implementations can utilize more bands and/or component carriers. 
     In implementations and with reference to  FIG. 8 , a training module (e.g., training module  270 , training module  314 ) trains a machine-learning module to process carrier-aggregation-communications using variations of these configurations, such as different bands, different combinations of component carriers, licensed access versus unlicensed access, varying bandwidths, varying protocols, etc. This includes training the machine-learning module on different component carriers. For instance, consider the neural network table  812  of  FIG. 8 . The index value  814  maps to the architecture and/or parameter configurations  808  that form a DNN based on processing in an operating environment, signal characteristics, and so forth, that correspond to the input characteristics  804 . Some implementations generate, for various component carriers, versions of the architecture and/or parameter configurations  808  for the input characteristics  804 . In other words, the training module generates a first grouping of architecture and/or parameter configurations based on the input characteristics  804  for a first component carrier, a second grouping of architecture and/or parameter configurations based on the input characteristics  804  for a second component carrier, and so forth. 
     Various implementations use a component-carrier-index-value to reference the architecture and/or parameter configurations. To illustrate, and with reference to the neural network table  812 , the addition of component-carrier-index-values would add depth or layers (e.g., another dimension) to the neural network table. A first component-carrier-index-value (corresponding to a first component carrier) maps, for example, to a first instance or layer of the neural network table. Similarly, a second component-carrier-index-value (corresponding to a second component carrier) maps to a second instance or layer of the neural network table, and so forth. 
       FIG. 11  illustrates an example environment  1100  that utilizes machine-learning architectures for simultaneous connection to multiple carriers in accordance with one or more implementations, such as carrier aggregation, dual connectivity, or other split architecture communications. The environment  1100  includes the base station  120  and UE  110  of  FIG. 1 , and a second base station  1102 . In some implementations, the base station  120  acts as a master managing the communications, and the base station  1102  acts as a secondary to the master that receives instructions from the master. 
     In the environment  1100 , the UE  110  maintains dual connectivity (DC) with the base station  120  and the base station  1102 . To illustrate, the UE  110  uses at least a first component carrier to establish communication link  1104  with the base station  120 . In this example, the communication link  1104  illustrates a bi-directional communication link that includes downlink communications and uplink communications between the UE  110  and the base station  120 , but in other implementations, the UE  110 /base station  120  establish the communication link  1104  as a single-directional link. At times, the communication link  1104  includes multiple component carriers, such as those described with reference to downlink communications  1002  and/or uplink communications  1004  of  FIG. 10 . 
     The UE  110  also uses at least a second component carrier to establish communication link  1106  with the base station  1102 . The environment  1100  illustrates the communication link  1106  in the form of (single-directional) downlink communications. Optionally, the communication link  1106  can alternately or additionally include uplink communication capabilities, illustrated here as communication link  1108 . Thus, the communication link  1106  and the communication link  1108  can, collectively, correspond to a component carrier with bi-directional communication capabilities. 
     To main synchronicity between the different connections, the base station  120  communicates with the base station  1102  using link  1110 . For instance, in some implementations, link  1110  corresponds to an X2 interface or an Xn interface. This allows the master (e.g., the base station  120 ) to synchronize the information being exchanged through the different communication links. 
     In some implementations, the base station  120  communicates instructions, commands, and/or information to the UE  110  over the communication link  1104 , where the instructions, commands, and/or information pertain to the communication link  1106  and/or the communication link  1108 . Contrary to CA, the communication link  1104  and the communication link  1106  (with or without the optional communication link  1108 ) utilized for DC have different C-RNTI values from one another. Thus, the communication link  1104  has a first C-RNTI, and the communication link  1106  has a second C-RNTI. 
     Similar to CA, DC can include multiple variations. In some implementations, the communication link  1104  and the communication link  1106  (and/or the communication link  1108 ) correspond to connections made using a same Radio Access Technology (RAT), thus making the DC illustrated in the environment  1100  intra-RAT DC. Other times, the communication link  1104  and the communication link  1106  (and/or the communication link  1108 ) correspond to connections made using different RATs, thus making the DC illustrated in the environment  1100  inter-RAT DC. As another variation, DC sometimes includes carrier aggregation. To illustrate, the communication link  1104 , the communication link  1106 , and/or the optional communication link  1108  can include multiple component carriers (e.g., downlink communications  1002 , uplink communications  1004 ). In implementations and with reference to  FIG. 8 , a training module (e.g., training module  270 , training module  314 ) trains a machine-learning module to process DC using variations of these configurations (e.g., different component carriers, Inter-RAT DC, Intra-RAT DC, the inclusion of CA, the exclusion of CA). 
     In aspects of machine-learning architectures for simultaneous connection to multiple carriers, a network entity determines a deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over the wireless communication system using carrier aggregation. To illustrate, consider now  FIG. 12  that illustrates an example environment  1200  that can utilize machine-learning architectures for simultaneous connection to multiple carriers, in accordance with one or more implementations, such as carrier aggregation, dual connectivity, or other split architecture communications. In this example, the communications employ downlink carrier aggregation. The environment  1200  includes the base station  120  and the UE  110  of  FIG. 1 . At times, the environment  1200  optionally includes the core network server  302  of  FIG. 3 . 
     The base station  120  includes one or more DNN(s)  1202  that process information exchanged over a wireless communication system using carrier aggregation. Alternately or additionally, the DNNs  1202  process other types of multiple component carrier communications and/or split architectures, such as Coordinated Multi Point (CoMP) communications, DC communications, Central Unit-Distributed Unit (CU-DU) architectures, or Multi-RAT Dual Connectivity (MR-DC). Similarly, UE  110  includes one or more DNN(s)  1204  that perform complementary operations relative to DNNs  1202 . Optionally, the core network server  302  includes one or more DNN(s)  1206 , such as in scenarios that employ E2E ML configurations as described with reference to  FIG. 14 . 
     DNNs  1202  perform operations associated with communicating information using carrier aggregation, other multiple component carrier communications, and/or split architecture communications. To illustrate, in various implementations, the DNNs  1202  include, or perform equivalent operations corresponding to, a first sub-DNN  1208  that generates a split output, a second sub-DNN  1210  that receives and processes a first output of the split output, and a third sub-DNN  1212  that receives and processes a second output of the split output. Alternately or additionally, the DNNs  1202  perform transmitter processing chain operations, such as those described with reference to  FIG. 6 . At times, the DNNs  1202  include the sub-DNN  1208 , sub-DNN  1210 , and sub-DNN  1212  as distinct and separate DNNs formed for performing specific processing operations (e.g., generating a split output, generating a first component carrier, generating a second component carrier). Other times, the DNNs  1202  are implement as a single DNN that performs the operations of the sub-DNN  1208 , sub-DNN  1210 , and sub-DNN  1212 , or as a DNN formed from a portion of a distributed E2E ML configuration as further described. 
     In some implementations, the component carriers have different characteristics that affect the operations performed by each sub-DNN. To illustrate, consider an example in which the first component carrier resides in a licensed band and the second component carrier resides in an unlicensed band. Since the bands use different protocol and/or communication mechanisms, the sub-DNNs (or portions of a single DNN) that process the respective component carriers perform different operations from one another. The sub-DNN processing the second component carrier in the unlicensed band, for example, performs processing operations that perform listen-before-talk (LBT) and/or a clear channel assessment (CCA), while the sub-DNN processing the first component carrier in the licensed band performs processing operations that correspond to the communication mechanisms of the licensed band. As another example, the component carriers can reside in different frequency spectrums that correspond to different signaling mechanisms. A sub-DNN processing a first component carrier with mmW properties may include beamforming/tracking operations, while a sub-DNN processing a second component carrier with longer wavelengths relative to mmW may exclude the beamforming/tracking operations. Thus, the sub-DNNs included in the DNNs  1202  (and/or the DNNs  1204 ) can be configured to perform different operations from one another based on various characteristics of the component carriers being processed. In implementations, a training module (e.g., training module  270 , training module  314 ) jointly trains the machine-learning module  400  to process multiple component carriers with different processing operations (e.g., beamforming, excluding beamforming, LBT, excluding LBT). Sometimes, this training can result in different architectures for the respective sub-DNNs. 
     In some implementations, the sub-DNNs of DNNs  1202  are interchangeable and/or modularized from one another. To illustrate, consider an example in which the base station  120  exchanges information with the UE  110  using a single component carrier. In such a scenario, the DNNs  1202  include, or perform functionality equivalent to, sub-DNN  1208  and sub-DNN  1210  to generate and transmit information using a first component carrier  1214 . At a later arbitrary point in time, the base station determines to exchange information using carrier aggregation or other forms of multiple component carrier communications. The base station determines, by way of any combination of the base station neural network manager  268 , the core network neural network manager  312 , an E2E ML controller, and/or a network-slice manager, a DNN configuration corresponding to the sub-DNN  1212  and/or updates to sub-DNN  1208 . The determined DNN configuration adds processing to the DNNs  1202  that generates and transmits information using a second component carrier  1216 . The base station  120  then forms and adds the sub-DNN  1212  to the DNNs  1202 . Alternately or additionally, the base station  120  updates the DNN  1208  to generate a split output as further described. As another example, the base station  120  removes a sub-DNN from the DNNs  1202  (e.g., sub-DNN  1210 , sub-DNN  1212 ) in response to terminating the second component carrier of the carrier-aggregation-communications, or changing component carriers. 
     Similar to the DNNs  1202 , the DNNs  1204  perform operations associated with communicating information using carrier aggregation, other multiple component carrier communications, and/or split architecture communications. In implementations, the DNNs  1204  perform complementary processing to the DNNs  1202  as described with respect to  FIG. 6 . At times, the DNNs  1204  include, or perform equivalent operations corresponding to, a first sub-DNN  1218  that receives and processes a first component carrier (e.g., the component carrier  1214 ), a second sub-DNN  1220  that receives and processes a second component carrier (e.g., the component carrier  1216 ), and a third sub-DNN  1222  that receives multiple inputs, and aggregates the information from the multiple inputs. Alternately or additionally, the DNNs  1204  perform receiver processing chain operations. At times, the UE  110  forms the sub-DNN  1218 , sub-DNN  1220 , and sub-DNN  1222  as distinct and separate (interchangeable/modularized) DNNs for performing specific processing operations (e.g., receiving and processing a first component carrier, receiving and processing a second component carrier, aggregating multiple inputs). Other times, the UE  110  forms the DNNs  1204  as a single DNN that performs processing equivalent to that described for the sub-DNN  1218 , the sub-DNN  1220 , and the sub-DNN  1222 , or a DNN formed from a portion of a distributed E2E ML configuration as further described. 
     Optionally, the DNNs  1206  of the core network server  302  include one or more sub-DNN(s), generally labeled in this example as sub-DNN  1224 , for processing portions of the carrier-aggregation-communications, other multiple component carrier communications, and/or split architecture communications. At times, the core network server  302  forms the sub-DNN  1224  as a portion of an E2E ML configuration, such as that described with reference to  FIG. 10 . 
     In various implementations, the core network server  302  and/or the base station  120  determine a configuration for the DNNs  1202 ,  1204 , and  1206 , such as through any combination of the core network neural network manager  312 , the E2E ML controller  318 , the network-slice manager  320 , or the base station neural network manager  268 . For instance, similar to that described with reference to  FIG. 8 , the core network server  302  or the base station  120  analyzes a neural network table using any combination of component carrier information, bandwidth information, current operating conditions, UE feedback, BS feedback, QoS requirements, metrics, and so forth, to determine the configuration for the DNNs  1202 ,  1204 , and/or  1206 . The UE  110  receives the configuration information, such as by receiving an indication of a neural network formation configuration as described with reference to  FIG. 7 , and forms the DNNs  1204 . This can include the core network server  302  and/or the base station  120  determining updates to the DNNs  1204  (and/or the DNNs  1202  and DNNs  1206 ) based on feedback, where the updates can include large (e.g., architectural) changes or small (e.g., parameter) changes to the DNNs and/or sub-DNNs. 
       FIG. 13  illustrates an example environment  1300  that utilizes machine-learning architectures for simultaneous connection to multiple carriers in accordance with one or more implementations, such as carrier aggregation, dual connectivity, or other split architecture communications. In various implementations, the example environment  1300  works in conjunction with one or more aspects described with respect to the example environment  1200  of  FIG. 12 . In the environment  1300 , the communications exchanged between devices correspond to uplink carrier aggregation communications. Similar to that described with respect to  FIG. 12 , the environment  1300  includes the base station  120  and the UE  110  of  FIG. 1 . At times, the environment  1300  optionally includes the core network server  302  of  FIG. 3 . 
     To perform operations associated with communicating information using carrier aggregation, the base station  120  includes one or more DNN(s)  1302  and the UE  110  includes one or more DNN(s)  1304 . Similar to processing information exchanged using downlink carrier aggregation, the core network server  302  optionally includes one or more DNN(s)  1306 . 
     In various implementations, the DNNs  1304  at the UE  110  perform similar operations to that described with respect to the DNNs  1202  at the base station  120  of  FIG. 12 . The DNNs  1304  can correspond to a single DNN, a portion of a distributed E2E DNN, or multiple sub-DNNs. For instance, the DNN(s)  1304  include, or perform equivalent operations corresponding to, a first sub-DNN  1308  that generates a split output for uplink carrier aggregation, a second sub-DNN  1310  that receives and processes a first output of the split output to generate a first component carrier  1312 , and a third sub-DNN  1314  that receives and processes a second output of the split output to generate a second component carrier  1316 . Alternately or additionally, the DNNs  1304  perform transmitter processing chain operations. At times, the sub-DNNs  1308 ,  1310 , and  1314  are interchangeable/modularized as further described. 
     In implementations, the DNNs  1302  at the base station  120  perform complementary processing to the DNNs  1304  as described with respect to  FIG. 6 . At times, the DNN(s)  1302  include, or perform equivalent operations corresponding to, a first sub-DNN  1318  that receives and processes a first component carrier (e.g., the component carrier  1312 ), a second sub-DNN  1320  that receives and processes a second component carrier (e.g., the component carrier  1316 ), and a third sub-DNN  1322  that receives multiple inputs, and aggregates the information from the multiple inputs. Alternately or additionally, the DNNs  1302  perform receiver processing chain operations. The base station  120  sometimes forms the sub-DNNs  1318 ,  1320 , and  1322  as distinct and separate (interchangeable) DNNs for performing specific processing operations (e.g., receiving and processing a first component carrier, receiving and processing a second component carrier, aggregating multiple inputs). Other times, the sub-DNNs  1318 ,  1320 , and  1322  are implemented as a single, distinct DNN (e.g., DNN  1302 ) that performs processing equivalent to that described for the sub-DNNs, or a DNN formed from a portion of a distributed E2E ML configuration as further described. 
     Optionally, the core network server  302  includes one or more sub-DNN(s), generally labeled in this example as sub-DNN  1324 , for processing portions of the carrier-aggregation-communications, other multiple component carrier communications, and/or split architecture communications. At times, the core network server  302  forms the sub-DNN  1324  as a portion of an E2E ML configuration, such as that described with reference to  FIG. 10 . 
     Having described ML architectures for simultaneous connection to multiple carriers, consider now a discussion of signaling and control transactions over a wireless communication system that can be used in various aspects of ML architectures for simultaneous connection to multiple carriers. 
     Signaling and Control Transactions for ML Architectures for Simultaneous Connection to Multiple Carriers 
       FIGS. 14 and 15  illustrate example signaling and control transaction diagrams between a base station, a user equipment, and/or a core network server in accordance with one or more aspects of using machine-learning architectures for simultaneous connection to multiple carriers, such as carrier aggregation, dual connectivity, or other split architecture communications. The signaling and control transactions may be performed by the base station  120  and the UE  110  of  FIG. 1 , and/or the core network server  302  of  FIG. 3 , using elements of  FIGS. 1-13 . 
     A first example of signaling and control transactions of using machine-learning architectures for simultaneous connection to multiple carriers is illustrated by the signaling and control transaction diagram  1400  of  FIG. 14 . The diagram  1400  includes transactions that include the UE  110 , the base station  120 , and (optionally) core network server  302 . Thus, various implementations include the core network server  302 , while other implementations exclude the core network server  302 . To indicate these implementations, various transactions span the core network server  302  and the base station  120  to designate that the corresponding transaction can be performed at the core network server  302  (only), the base station  120  (only), or a combination of the core network server  302  and the base station  120  through interactions with one another. This is further denoted through the use of “core network server  302 /base station  120 ”. 
     At  1405 , the core network server  302 /base station  120  optionally receives metrics and/or UE capabilities from the UE  110 . For instance, the base station  120  receives UE capabilities from the UE  110  in response to sending a request for UE capabilities. Alternately or additionally, the core network server  302  receives the UE capabilities or the UE metrics from the UE  110  through the base station  120 . In some implementations, the UE capabilities include ML-related capabilities, such as a maximum kernel size capability, a memory limitation, a computation capability, supported ML architectures, supported number of layers, available processing power, memory limitation, available power budget, and fixed-point processing versus floating point processing. Alternately or additionally, the UE capabilities include carrier aggregation capabilities, dual connectivity capabilities, and so forth. As another example, the base station  120  receives UE metrics from the UE  110 , such as power measurements (e.g., RSSI), error metrics, timing metrics, QoS, latency, a Reference Signal Receive Power (RSRP), SINR information, CQI, CSI, Doppler feedback, etc. 
     In some implementations, the core network server  302  receives BS metrics from the base station  120  that are based on communications that the base station exchanges with the UE  110  as described with reference to  FIG. 8 . The core network server  302  can also receive BS capabilities from the base station  120 , such as processing power, power state, capacity (e.g., supportable number of connections), working range, and so forth. 
     At  1410 , the core network server  302 /base station  120  (by way of the BS neural network manager  268 , the core network neural network manager  312 , the E2E ML controller  318 , the network-slice manager  320 , an E2E ML controller implemented by the base station, and/or a network-slice manager implemented by the base station) determines one or more DNN configurations for processing a simultaneous connection to multiple component carrier, such as one or more DNN configurations for carrier-aggregation-communications. The DNN configuration(s) can include a (partitioned) E2E ML configuration, multiple sub-DNN configurations, and/or multiple DNN configurations for multiple devices. For instance, with reference to  FIG. 12 , the core network server  302 /base station  120  can determine a first sub-DNN configuration used to form the sub-DNN  1208 , a second sub-DNN configuration used to form the sub-DNN  1210 , and a third sub-DNN configuration used to form the sub-DNN  1212 . In some implementations, the core network server  302 /base station  120  determines distinct DNN configurations, such as a first DNN configuration that forms the DNNs  1302  at the base station  120 , a second DNN configuration that forms the DNNs  1304 , and so forth, with reference to  FIG. 13 . The DNN configurations can form sub-DNNs (or portions of a DNN) that perform different processing operations from one another, such as a first sub-DNN that processes a first component carrier in licensed band and a second sub-DNN that processes a second component in an unlicensed band. 
     In some implementations, the core network server  302 /base station  120  determines the DNN configuration(s) based on characteristics about the multiple component carriers, such as characteristics about carrier aggregation and whether the carrier aggregation corresponds to contiguous Intra-RAT carrier aggregation, non-contiguous Intra-RAT carrier aggregation, or non-contiguous Inter-RAT carrier aggregation. Alternately or additionally, the core network server  302 /base station  120  determines the DNN configuration(s) based on the UE capabilities received at  1405 , such as carrier aggregation capabilities or dual connectivity capabilities. 
     At  1415 , the core network server  302 /base station  120  communicates at least one of the DNN configuration(s) to the UE  110  and/or the base station  120 . This can include communicating a sub-DNN configuration, a partition of an E2E ML configuration, or a single DNN configuration that performs all UE-related carrier aggregation processing. As described at  715  with reference to  FIG. 7 , the base station (and/or core network server) sometimes communicates the DNN configuration(s) by transmitting a message that indicates a neural network formation configuration and/or an indication to form a DNN based on the neural network formation configuration. In implementations, the core network server  302 /base station  120  transmits one or more index values that map to entries in a neural network table. This can include the core network server  302 /base station  120  communicating a component-carrier-index-value that maps to a neural network formation configuration for a specific component carrier as further described. Alternately or additionally, the core network server  302 /base station  120  transmits a message that includes neural network parameter configurations (e.g., weight values, coefficient values, number of filters). 
     In some implementations, the base station  120  (and/or the core network server  302  by way of the base station  120 ) communicates the configuration of the DNN to the UE  110  using a first component carrier, where the DNN configuration corresponds to forming a DNN for processing a second component carrier of the carrier aggregation. For instance, consider again the example in which the base station  120  exchanges information with the UE  110  using a single component carrier, where the base station uses the sub-DNN  1208  and the sub-DNN  1210  to generate and transmit information using the first component carrier  1214 . Similarly, the UE  110  uses the sub-DNN  1218  and the sub-DNN  1222  to process the information exchanged using the first component carrier. In various implementations, the base station  120  communicates a sub-DNN configuration for a DNN that processes a second component carrier over the first component carrier  1214 . For instance, the base station  120  transmits the sub-DNN configuration (for the DNN that processes the second component carrier) using Layer  1  or Layer  2  control channels associated with the first component carrier. 
     At  1420 , the core network server  302  optionally forms at least one core-network-server-side deep neural network (CNS-side DNN) based on at least one of the DNN configurations determined at  1410 , where the CNS-side DNN can include multiple sub-DNNs, be a distinct DNN, or be part of a distributed DNN. In implementations, the CNS-side DNN(s) perform at least some processing for exchanging information over a wireless communication system using a simultaneous connection to multiple component carriers (e.g., carrier aggregation), which can include pre-transmission processing. 
     At  1425 , the base station  120  forms at least one base-station-side deep neural network (BS-side DNN) based on the DNN configuration(s) determined at  1410 . The BS-side DNN can include multiple sub-DNNs, be a distinct DNN, or be part of a distributed DNN. In implementations, the B S-side DNN(s) formed by the base station perform at least some processing for exchanging information over a wireless communication system using a simultaneous connection to multiple component carriers, including pre-transmission processing. 
     Similarly, at  1430 , the UE  110  forms at least one user-equipment-side deep-neural-network (UE-side DNN) based on the one or more DNN configurations determined at  1410 , where the UE-side DNN can include multiple sub-DNNs, be a distinct DNN, or be part of a distributed DNN. In implementations, the UE  110  accesses a neural network table using information received at  1520  to obtain one or more architectures and/or parameters as described with reference to  FIG. 8 . In implementations, the DNN formed by the UE  110  performs at least some processing for exchanging information over a wireless communication system using a simultaneous connection to multiple component carriers. 
     Afterwards, at  1435 , the core network server  302 /base station  120  and the UE  110  process the simultaneous connection to multiple component carriers using the DNNs, such as that described with reference to  FIGS. 10-13 . In implementations, the core network server  302 /base station  120  and/or the UE  110  iteratively perform the signaling and control transactions described in the signaling and control transaction diagram  1400 , signified by dashed line  1440 . These iterations allow the base station  120  and/or the UE  110  to dynamically modify the DNNs processing the information exchanged using the simultaneous connection to multiple component carriers (e.g., carrier aggregation, dual connectivity) based upon changing operating conditions, and improve the performance of the exchanges, as further described. 
     Changing operating conditions impact the performance of how well each DNN processes information. To illustrate, and with reference to  FIG. 8 , the training module  270  and/or the training module  314  generate neural network formation configurations based on operating conditions as described by the input characteristics. This includes operating conditions corresponding to exchanging information over the wireless communication system using multiple component carriers. As current operating conditions deviate, the performance of the DNN begins to deteriorate. 
     Various implementations modify one or more DNNs based on feedback from a UE and/or a base station. The modifications can include architectural changes and/or parameter changes to the DNN(s). To demonstrate, consider now a second example of signaling and control transactions of using machine-learning architectures for simultaneous connection to multiple carriers, illustrated in  FIG. 15  by the signaling and control transaction diagram  1500 . In some implementations, the signaling and control transaction diagram  1500  represents a continuation of the signaling and control transaction diagram  1400  of  FIG. 14 . Accordingly, the diagram  1500  includes transactions that include the UE  110 , the base station  120 , and (optionally) core network server  302 , where various transactions can be performed at the core network server  302  (only), the base station  120  (only), or a combination of the core network server  302  and the base station  120  through interactions with one another. This is further denoted through the use of “core network server  302 /base station  120 ”. 
     At  1505 , the core network server  302 /base station  120  and the UE  110  process, using one or more DNNs, information exchanged over a wireless communication system using a simultaneous connection to multiple component carriers. In some implementations, the processing performed at  1505  corresponds to the processing performed at  1435  of  FIG. 14 . 
     At  1510 , the core network server  302 /base station  120  receives feedback from the UE  110 . For example, the UE  110  communicates one or more metrics, such as BLER, SINR, CQI feedback, or a packet loss rate to the base station  120  (and/or the core network server  302  through the base station  120 ). Alternately or additionally, the base station  120  generates one or more metrics, such as a Round-Trip Time (RTT) latency metric, uplink received power, uplink SINR, uplink packet errors, uplink throughput, timing measurements, power information, SINR information, CQI, CSI, or Doppler feedback, and sends the metrics as feedback to the core network server  302 . 
     At  1515 , the core network server  302 /base station  120  analyzes the feedback. For example, the core network server  302 /base station  120  (by way of the BS neural network manager  268 , the core network neural network manager  312 , the E2E ML controller  318 , the network-slice manager  320 , an E2E ML controller implemented by the base station, and/or a network-slice manager implemented by the base station) analyzes the feedback to determine whether modifications to the DNNs would improve an overall performance of exchanging the information using multiple component carriers. 
     At  1520 , the core network server  302 /base station  120  (by way of the BS neural network manager  268 , the core network neural network manager  312 , the E2E ML controller  318 , the network-slice manager  320 , an E2E ML controller implemented by the base station, and/or a network-slice manager implemented by the base station) determines a modification to at least one of the DNN(s) based on the feedback. In some implementations, the core network server  302 /base station  120  determines a large modification that changes an architecture configuration of the DNNs. Alternately or additionally, the core network server  302 /base station  120  determines a small modification that corresponds to changing parameter configurations without changing the architecture configuration, such as changing coefficient values, weights, or kernel sizes. The modification can correspond to DNNs formed from a partitioned E2E ML configuration, modifications to one or more sub-DNNs, or modifications to distinct DNNs. 
     At  1525 , core network server  302 /base station  120  communicates the modification to the UE  110 . For instance, similar to that described at  1415  of  FIG. 14 , the base station transmits a message to the UE  110 , where the message indicates a neural network formation configuration corresponding to the modification. Alternately or additionally, the core network server  302  communicates the modification to the base station  120  and/or the UE  110  (by way of the base station  120 ). The indication can include one or more index values that map to entries in a neural network table. At times, the message includes a component-carrier-index-value that maps to neural network table information for a specific component carrier as further described. Other times, the message. includes neural network parameter values (e.g., weight values, coefficient values, number of filters. 
     At  1530 , the core network server  302  (optionally) updates one or more DNNs based on the modification. Similarly, at  1535  and  1540 , respectively, the base station  120  (optionally) updates one or more DNNs based on the modification and the UE  110  updates one or more DNNs based on the modification. The core network server  302 , the base station  120 , and/or the UE  110  update any combination of sub-DNNs, distributed DNNs based on a partitioned E2E ML configuration, and/or distinct DNNs. In implementations, the core network server  302 , the base station  120 , and the UE(s)  110  iteratively perform the signaling and control transactions described in the signaling and control transaction diagram  1500 , signified with dashed line  1545 . These iterations allow the core network server  302 , the base station  120  and/or the UE(s)  110  to dynamically modify the DNNs processing the information exchanged over the wireless communication system using multiple component carrier communications based upon changing operating conditions, and improve an overall performance, as further described. 
     Example Methods 
     Example methods  1600  and  1700  are described with reference to  FIG. 16  and  FIG. 17  in accordance with one or more aspects of machine-learning architectures for simultaneous connection to multiple carriers, other types of communications that use multiple component carriers, and/or other split architecture communications. The order in which the method blocks are described are not intended to be construed as a limitation, and any number of the described method blocks can be skipped or combined in any order to implement a method or an alternate method. Generally, any of the components, modules, methods, and operations described herein can be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or any combination thereof. Some operations of the example methods may be described in the general context of executable instructions stored on computer-readable storage memory that is local and/or remote to a computer processing system, and implementations can include software applications, programs, functions, and the like. Alternatively, or additionally, any of the functionality described herein can be performed, at least in part, by one or more hardware logic components, such as, and without limitation, Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SoCs), Complex Programmable Logic Devices (CPLDs), and the like. 
       FIG. 16  illustrates an example method  1600  for using a machine-learning architecture for simultaneous connection to multiple carriers, other types of communications that use multiple component carriers, and/or other split architecture communications. In some implementations, operations of method  1600  are performed by a network entity, such as any one of the base stations  120  or the core network server  302 . 
     At  1605 , the network entity determines at least one DNN configuration for processing information exchanged with a UE over the wireless communication system using a simultaneous connection to multiple component carriers, such as that described at  1410  of  FIG. 14 . In implementations, the multiple component carriers include at least a first component carrier and a second component carrier, but additional component carriers can be included. In one or more implementations, the network entity (e.g., core network server  302 ) determines an E2E ML configuration for exchanging communications using a simultaneous connection to multiple component carriers (e.g., at  1410 ) with the UE  110 . As another example, the network entity (e.g., base station  120 ) determines the configuration of one or more DNNs (e.g., at  710 ), such as a first configuration for a BS-side DNN and a second configuration for a UE-side DNN that performs complementary operations to one another. At times, the network entity determines multiple sub-DNN configurations, such as a first sub-DNN configuration for forming a first sub-DNN (sub-DNN  1208 , sub-DNN  1308 ) that generates a split output, a second sub-DNN configuration for forming a second sub-DNN that processes a first component carrier (sub-DNN  1210 , sub-DNN  1310 ), a third sub-DNN configuration for forming a third sub-DNN that processes a second component carrier (sub-DNN  1212 , sub-DNN  1314 ), a fourth sub-DNN configuration for forming a fourth sub-DNN that aggregates multiple inputs (sub-DNN  1222 , sub-DNN  1322 ), and so forth. 
     In determining the DNN configuration(s), some implementations of the network entity determine DNN configuration(s) for processing downlink carrier aggregation. For instance, the network entity determines DNN configuration(s) for processing downlink carrier aggregation that exchanges information using a first component carrier in a licensed band and a second component carrier in an unlicensed band. The DNN configuration(s) can include a first DNN configuration that forms a first DNN that generates a split output that includes a first output associated with the first component carrier and a second output associated with the second component carrier. Alternately or additionally, the DNN configuration(s) include a second DNN configuration that forms a second DNN that aggregates a first input associated with the first component carrier and a second input associated with the second component carrier. In various implementations, the network entity determines DNN configuration(s) for processing uplink carrier aggregation. At times, determining the DNN configuration(s) can be based on a count or number of component carriers included in the communications. 
     At  1610 , the network entity forms at least a first DNN based on a first portion of the at least one DNN configuration, such as that described at  1420  and at  1425  of  FIG. 14 . In implementations, the network entity (core network server  302 ) forms a CNS-side DNN (e.g., DNN(s)  1206 , DNN(s)  1306 ), where the CNS-side DNN can be a distributed DNN based on a partition of an E2E ML configuration, include sub-DNNs, or be a distinct DNN. As another example, the network entity (e.g., base station  120 ) forms one or more BS-side DNNs (e.g., DNN(s)  1202 , DNN(s)  1302 ). Similar to the CNS-side DNN, the BS-side DNN(s) can be a distributed DNN based on a partition of an E2E ML configuration, include sub-DNNs, or be a distinct DNN. In implementations, the network entity accesses a neural network table to obtain one or more architecture and/or parameter configurations. In response to obtaining the configurations, the network entity forms the network entity DNN using the architecture and/or parameter configurations. 
     At  1615 , the network entity communicates an indication of a second portion of the DNN configuration(s) to the UE and directs the UE to form a second DNN. For example, similar to that described at  715  of  FIG. 7 , the network entity (e.g., core network server, base station  120 ) communicates one or more index values to the UE (e.g., UE  110 ) that map to entries of a neural network table to provide the UE with a neural network formation configuration. Alternately or additionally, the network entity communicates a component-carrier-index-value to the UE. The indication can include a command that directs the UE  110  to form the second DNN using the neural network formation configuration indicated by the index value(s). In some implementations, the second portion of the DNN configuration(s) includes a (modularized) sub-DNN configuration that forms a sub-DNN to process the information exchanged using the second component carrier. At times, the network entity communicates the indication of the second portion using the first component carrier. 
     At  1620 , the network entity uses the at least first DNN to exchange, over the wireless communication system, the information with the UE using the simultaneous connection to the multiple component carriers. For example, the network entity (e.g., the core network server  302 , the base station  120 ) uses the first DNN to perform transmitter processing chain operations associated with carrier aggregation, including pre-transmission processing, such as that described with reference to  FIG. 6 . 
     At  1625 , the network entity optionally receives feedback from the UE. As one example, the network entity (e.g., the base station  120 ) receives one or more UE metrics (e.g., power information, SINR information, CQI, CSI, Doppler feedback, QoS, latency) from the UE (e.g., UE  110 ) that are based on downlink communications exchanged with the base station  120 . As another example, the network entity (e.g., the core network server  302 ) receives the one or more UE metrics from the UE (e.g., UE  110 ) by way of the base station  120 . Alternately or additionally, the network entity (e.g., core network server  302 ) receives BS metrics from the base station  120 , such as uplink received power, uplink SINR, uplink packet errors, uplink throughput, timing measurements, power information, SINR information, CQI, CSI, Doppler feedback, QoS, latency, etc. 
     At  1630 , the network entity optionally determines a modification to the DNN configuration(s) by analyzing the feedback. In implementations, the network entity (e.g., core network server  302 , base station  120 ) analyzes a neural network table based on the feedback to determine the modification. This includes determining a large modification that corresponds to one or more architectural changes to the DNN configuration(s), or a smaller modification that corresponds to one or more parameter changes to a fixed DNN architecture of the DNN configuration(s). 
     At  1635 , the network entity optionally communicates the modification to the UE and directs the UE to update the second DNN based on the modification. For instance, the network entity (e.g., core network server  302 , base station  120 ) communicates one or more index values to the UE (e.g., UE  110 ) that map to entries of a neural network table, communicates a component-carrier-index-value, and/or communicates parameter values as further described. 
       FIG. 17  illustrates an example method  1700  for using a machine-learning architecture for simultaneous connection to multiple carriers. In some implementations, operations of method  1700  are performed by a user equipment, such as UE  110  of  FIG. 1   
     At  1705 , the UE receives an indication of at least one DNN configuration for processing information exchanged over a wireless communication system using a simultaneous connection to multiple carriers, such as carrier aggregation or dual connectivity. For instance, the UE (e.g., UE  110 ) receives, from a network entity (e.g., core network server  302 , base station  120 ), a message that includes an index value that maps into a neural network table. Alternately or additionally, the indication includes a component-carrier-index-value that maps to neural network table information for a particular component carrier. The DNN configuration(s) can include any combination of a portion of an E2E ML configuration, one or more sub-DNN configurations, or a distinct DNN configuration. In implementations, the multiple component carriers include at least a first component carrier and a second component carrier, but additional component carriers can be included. 
     At  1710 , the UE forms at least one DNN based on the indication. As one example, the UE (e.g., UE  110 ) forms DNN(s) to process downlink carrier aggregation. As another example, the UE (e.g., UE  110 ) forms DNN(s) to process uplink carrier aggregations. To illustrate, with respect to forming DNNs for downlink carrier aggregation, in at least one implementation, the UE forms a first sub-DNN (e.g., sub-DNN  1218 ) to generate a first input, a second sub-DNN (e.g., sub-DNN  1220 ) to generate the second input, and a third sub DNN (e.g., sub-DNN  1222 ) to receive the first input and the second input and aggregate the first input and the second input to recover information exchanged over the wireless communication system. Alternately or additionally, the UE forms a single DNN (e.g., DNN  1204 ) that performs operations corresponding to multiple sub-DNNs. At times, the UE forms a DNN based on a portion of a distributed E2E ML configuration. In some implementations, the indication corresponds to an update to the first sub-DNN or the second sub-DNN, and the UE forms the DNN(s) by updating the first or second sub-DNN. 
     At  1715 , the UE uses the DNN(s) to process the information exchanged over the wireless communication system using the simultaneous connection to the multiple component carriers. In at least one implementation, the UE (e.g., UE  110 ) processes information exchanged using downlink carrier aggregation, such as that described with reference to  FIG. 12 . Alternately or additionally, the UE (e.g., UE  110 ) processes information exchanged using uplink carrier aggregation, such as that described with reference to  FIG. 13 . For instance, with reference to uplink carrier aggregation, the UE generates, using the DNN(s) (e.g., sub-DNN  1310 ), a first output associated with the first component carrier to exchange a first portion of the information over the wireless communication system. As part of the DNN(s) for uplink carrier aggregation, the UE generates, using the DNN(s) (e.g., sub-DNN  1310 ), a second output associated with the second component carrier to exchange a second portion of the information over the wireless communication system. At times, and with reference to downlink carrier aggregation, the UE processes, using a first DNN (e.g., sub-DNN  1218 ) a first portion of the information exchanged using the first component carrier to generate a first input, processes, using a second DNN (e.g., sub-DNN  1220 ) a second portion of the information exchanged using the second component carrier to generate a second input, and aggregates, using a third DNN (e.g., sub-DNN  1222 ), the first input and the second input to recover the information. 
     Generally, any of the components, modules, methods, and operations described herein can be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or any combination thereof. Some operations of the example methods may be described in the general context of executable instructions stored on computer-readable storage memory that is local and/or remote to a computer processing system, and implementations can include software applications, programs, functions, and the like. Alternatively or in addition, any of the functionality described herein can be performed, at least in part, by one or more hardware logic components, such as, and without limitation, Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SoCs), Complex Programmable Logic Devices (CPLDs), and the like. 
     Although techniques and devices for machine-learning architectures for simultaneous connection to multiple carriers, other types of communications that use multiple component carriers, and/or other split architecture communications, have been described in language specific to features and/or methods, the subject of the appended claims is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as example implementations of machine-learning architectures for simultaneous connection to multiple carriers. 
     In the following, several examples are described: 
     Example 1: A method performed by a network entity associated with a wireless communication system, the method comprising: determining at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over the wireless communication system using a simultaneous connection to multiple component carriers that include a first component carrier and a second component carrier, the at least one DNN configuration including a first portion of the at least one DNN configuration for forming a first DNN at the network entity, and a second portion of the at least one DNN configuration for forming a second DNN at the UE; forming the first DNN based on the first portion; communicating an indication of the second portion to the UE and directing the UE to form the second DNN based on the second portion; and using the first DNN to exchange, over the wireless communication system, the information with the UE using the simultaneous connection to the multiple component carriers. 
     Example 2: The method as recited in example 1, wherein the first component carrier is in a licensed band, and wherein the second component carrier is in an unlicensed band. 
     Example 3: The method as recited in example 1, wherein the simultaneous connection to the multiple component carriers is downlink carrier aggregation, and wherein determining the at least one DNN configuration comprises: determining, for the first portion, a first DNN configuration that forms the first DNN to process the information by generating a split output that includes a first output associated with the first component carrier and a second output associated with the second component carrier; and determining, for the second portion, a second DNN configuration that forms the second DNN to process the information by aggregating a first input associated with the first component carrier and a second input associated with the second component carrier. 
     Example 4: The method as recited in example 3, wherein determining the first DNN configuration comprises: determining a first sub-DNN configuration for forming a first sub-DNN that generates the split output; determining a second sub-DNN configuration for forming a second sub-DNN that processes, based on the first output, the information exchanged using the first component carrier; and determining a third sub-DNN configuration for forming a third sub-DNN that processes, based on the second output, the information exchanged using the second component carrier. 
     Example 5: The method as recited in example 1, wherein the simultaneous connection to the multiple component carriers is uplink carrier aggregation, and wherein determining the at least one DNN configuration comprises: determining, for the first portion, a first DNN configuration that forms the first DNN to process the information by aggregating multiple inputs that include a first input associated with the first component carrier and a second input associated with the second component carrier; and determining, for the second portion, a second DNN configuration that forms the second DNN to process the information by generating a split output that includes a first output associated with the first component carrier and a second component carrier. 
     Example 6: The method as recited in example 1, wherein the determining the at least one DNN configuration is based, at least in part, on a count of component carriers included in the multiple component carriers. 
     Example 7: The method as recited in example 1, wherein the determining the at least one DNN configuration further comprises: determining an end-to-end machine-learning configuration (E2E ML configuration) as the at least one DNN configuration. 
     Example 8: The method as recited in example 7, wherein the first portion comprises a first partition of the E2E ML configuration, and wherein the second portion comprises a second partition of the E2E ML configuration. 
     Example 9: The method as recited in example 1, wherein the second portion of the at least one DNN configuration includes a sub-DNN configuration that forms a sub-DNN to process the information exchanged using the second component carrier, and wherein the communicating the indication of the second portion to the UE comprises: communicating, using the first component carrier, the sub-DNN configuration that forms the sub-DNN to process the information exchanged using the second component carrier. 
     Example 10: The method as recited in example 1, further comprising: receiving feedback from the UE; determining a modification to the at least one DNN configuration by analyzing the feedback; and communicating the modification to the UE and directing the UE to update the second DNN based on the modification. 
     Example 11: A method performed by a user equipment (UE) associated with a wireless communication system, the method comprising: receiving an indication of at least one deep neural network (DNN) configuration for processing information exchanged over the wireless communication system using a simultaneous connection to multiple component carriers that include a first component carrier and a second component carrier; forming, at the UE, at least one DNN based on the indication; and using the at least one DNN to process the information exchanged over the wireless communication system using the simultaneous connection to the multiple component carriers. 
     Example 12: The method as recited in example 11, wherein the simultaneous connection to the multiple component carriers comprises downlink carrier aggregation, and wherein using the at least one DNN to process the information comprises: processing, using the at least one DNN, a first portion of the information exchanged using the first component carrier to generate a first input; processing, using the at least one DNN, a second portion of the information exchanged using the second component carrier to generate a second input; and aggregating, using the at least one DNN, the first input and the second input to recover the information. 
     Example 13: The method as recited in example 12, wherein processing the first portion of the information comprises processing the information using a first sub-DNN to generate the first input; wherein processing the second portion comprises processing the information using a second sub-DNN to generate the second input; and wherein aggregating the first input and the second input comprises using a third sub DNN to receive the first input and the second input and aggregate the first input and the second input to recover the information. 
     Example 14: The method as recited in example 13, wherein the indication comprises a first indication, and the method further comprises: receiving a second indication that indicates a modification to the first sub-DNN, the second sub-DNN, or the third sub-DNN; and updating the first sub-DNN, the second sub-DNN, or the third sub-DNN based on the second indication. 
     Example 15: The method as recited in example 11, wherein the simultaneous connection to the multiple component carriers comprises uplink carrier aggregation, and wherein using the at least one DNN to process the information comprises: generating, using the at least one DNN, a first output associated with the first component carrier to exchange a first portion of the information over the wireless communication system; and generating, using the at least one DNN, a second output associated with the second component carrier to exchange a second portion of the information over the wireless communication system. 
     Example 16: The method as recited in example 15, wherein forming the at least one DNN comprises: forming the at least one DNN based on a portion of a distributed end-to-end machine-learning configuration. 
     Example 17: The method as recited in example 11, wherein the first component carrier is in a licensed band, and wherein the second component carrier is in an unlicensed band. 
     Example 18: A network entity apparatus comprising: a processor; and computer-readable storage media comprising instructions that direct the network entity apparatus to perform operations comprising: determining at least one deep neural network (DNN) configuration for processing information exchanged with a user equipment (UE) over a wireless communication system using a simultaneous connection to multiple component carriers that include a first component carrier and a second component carrier, the at least one DNN configuration including a first portion for forming a first DNN at the network entity apparatus, and a second portion for forming a second DNN at the UE; communicating an indication of the second portion to the UE and directing the UE to form the second DNN based on the second portion; forming the first DNN based on the first portion of the DNN configuration; and using the first DNN to exchange the information, over the wireless communication system and with the UE, using the simultaneous connection to the multiple component carriers. 
     Example 19: The network entity apparatus as recited in example 18, the operations further comprising: receiving feedback from the UE; determining a modification to the at least one DNN configuration by analyzing the feedback; and communicating the modification to the UE and directing the UE to update the second DNN based on the modification. 
     Example 20: The network entity apparatus as recited in example 19, wherein the determining the modification comprises: determining one or more architectural changes to the at least one DNN configuration; or determining one or more parameter changes to the at least one DNN configuration. 
     Example 21: The network entity apparatus as recited in example 18, wherein determining the at least one DNN configuration comprises: determining, for the first portion, a first DNN configuration that forms the first DNN to process the information by generating a split output that includes a first output associated with the first component carrier and a second output associated with the second component carrier; and determining, for the second portion, a second DNN configuration that forms the second DNN to process the information by aggregating a first input associated with the first component carrier and a second input associated with the second component carrier.