Patent Publication Number: US-2021185700-A1

Title: Scheduling request associated with artificial intelligence information

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This Patent Application claims priority to U.S. Provisional Patent Application No. 62/948,126, filed on Dec. 13, 2019, entitled “SCHEDULING REQUEST ASSOCIATED WITH ARTIFICIAL INTELLIGENCE INFORMATION,” and assigned to the assignee hereof. The disclosure of the prior Application is considered part of and is incorporated by reference into this Patent Application. 
    
    
     FIELD OF THE DISCLOSURE 
     Aspects of the present disclosure generally relate to wireless communication and to techniques and apparatuses for a scheduling request (SR) associated with artificial intelligence (AI) information. 
     BACKGROUND 
     Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, and/or the like). Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency-division multiple access (FDMA) systems, orthogonal frequency-division multiple access (OFDMA) systems, single-carrier frequency-division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and Long Term Evolution (LTE). LTE/LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP). 
     A wireless communication network may include a number of base stations (BSs) that can support communication for a number of user equipment (UEs). A user equipment (UE) may communicate with a base station (BS) via the downlink and uplink. The downlink (or forward link) refers to the communication link from the BS to the UE, and the uplink (or reverse link) refers to the communication link from the UE to the BS. As will be described in more detail herein, a BS may be referred to as a Node B, a gNB, an access point (AP), a radio head, a transmit receive point (TRP), a New Radio (NR) BS, a 5G Node B, and/or the like. 
     The above multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different user equipment to communicate on a municipal, national, regional, and even global level. New Radio (NR), which may also be referred to as 5G, is a set of enhancements to the LTE mobile standard promulgated by the Third Generation Partnership Project (3GPP). NR is designed to better support mobile broadband Internet access by improving spectral efficiency, lowering costs, improving services, making use of new spectrum, and better integrating with other open standards using orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) (CP-OFDM) on the downlink (DL), using CP-OFDM and/or SC-FDM (e.g., also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink (UL), as well as supporting beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation. However, as the demand for mobile broadband access continues to increase, there exists a need for further improvements in LTE and NR technologies. Preferably, these improvements should be applicable to other multiple access technologies and the telecommunication standards that employ these technologies. 
     SUMMARY 
     In some aspects, a method of wireless communication, performed by a user equipment (UE), may include transmitting a scheduling request that includes information associated with an artificial intelligence module of the UE; and receiving an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, a method of wireless communication, performed by a base station, may include receiving a scheduling request that includes information associated with an artificial intelligence module associated with a UE; and transmitting, to the UE, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, a UE for wireless communication may include memory and one or more processors coupled to the memory. The memory and the one or more processors may be configured to transmit a scheduling request that includes information associated with an artificial intelligence module of the UE; and receive an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, a base station for wireless communication may include memory and one or more processors coupled to the memory. The memory and the one or more processors may be configured to receive a scheduling request that includes information associated with an artificial intelligence module associated with a UE; and transmit, to the UE, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, a non-transitory computer-readable medium may store one or more instructions for wireless communication. The one or more instructions, when executed by one or more processors of a UE, may cause the one or more processors to: transmit a scheduling request that includes information associated with an artificial intelligence module of the UE; and receive an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, a non-transitory computer-readable medium may store one or more instructions for wireless communication. The one or more instructions, when executed by one or more processors of a base station, may cause the one or more processors to: receive a scheduling request that includes information associated with an artificial intelligence module associated with a UE; and transmit, to the UE, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, an apparatus for wireless communication may include means for transmitting a scheduling request that includes information associated with an artificial intelligence module associated with the apparatus; and means for receiving an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     In some aspects, an apparatus for wireless communication may include means for receiving a scheduling request that includes information associated with an artificial intelligence module associated with a UE; and means for transmitting, to the UE, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     Aspects generally include a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, wireless communication device, and/or processing system as substantially described herein with reference to and as illustrated by the accompanying drawings. 
     The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. Characteristics of the concepts disclosed herein, both their organization and method of operation, together with associated advantages will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purposes of illustration and description, and not as a definition of the limits of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the above-recited features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, may be had by reference to aspects, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects. The same reference numbers in different drawings may identify the same or similar elements. 
         FIG. 1  is a diagram illustrating an example of a wireless communication network, in accordance with various aspects of the present disclosure. 
         FIG. 2  is a diagram illustrating an example of a base station in communication with a UE in a wireless communication network, in accordance with various aspects of the present disclosure. 
         FIG. 3  is a diagram illustrating an example of scheduling an uplink communication associated with an AI module, in accordance with various aspects of the present disclosure. 
         FIG. 4  is a diagram illustrating an example process performed, for example, by a UE, in accordance with various aspects of the present disclosure. 
         FIG. 5  is a diagram illustrating an example process performed, for example, by a base station, in accordance with various aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Various aspects of the disclosure are described more fully hereinafter with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Based on the teachings herein one skilled in the art should appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure disclosed herein, whether implemented independently of or combined with any other aspect of the disclosure. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method which is practiced using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim. 
     Several aspects of telecommunication systems will now be presented with reference to various apparatuses and techniques. These apparatuses and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, and/or the like (collectively referred to as “elements”). These elements may be implemented using hardware, software, or combinations thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. 
     It should be noted that while aspects may be described herein using terminology commonly associated with 3G and/or 4G wireless technologies, aspects of the present disclosure can be applied in other generation-based communication systems, such as 5G and later, including NR technologies. 
       FIG. 1  is a diagram illustrating a wireless network  100  in which aspects of the present disclosure may be practiced. The wireless network  100  may be an LTE network or some other wireless network, such as a  5 G or NR network. The wireless network  100  may include a number of BSs  110  (shown as BS  110   a , BS  110   b , BS  110   c , and BS  110   d ) and other network entities. ABS is an entity that communicates with user equipment (UEs) and may also be referred to as a base station, a NR BS, a Node B, a gNB, a 5G node B (NB), an access point, a transmit receive point (TRP), and/or the like. Each BS may provide communication coverage for a particular geographic area. In 3GPP, the term “cell” can refer to a coverage area of a BS and/or a BS subsystem serving this coverage area, depending on the context in which the term is used. 
     A BS may provide communication coverage for a macro cell, a pico cell, a femto cell, and/or another type of cell. A macro cell may cover a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by UEs with service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs with service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs having association with the femto cell (e.g., UEs in a closed subscriber group (CSG)). ABS for a macro cell may be referred to as a macro BS. ABS for a pico cell may be referred to as a pico BS. A BS for a femto cell may be referred to as a femto BS or a home BS. In the example shown in  FIG. 1 , a BS  110   a  may be a macro BS for a macro cell  102   a , a BS  110   b  may be a pico BS for a pico cell  102   b , and a BS  110   c  may be a femto BS for a femto cell  102   c . A BS may support one or multiple (e.g., three) cells. The terms “eNB”, “base station”, “NR BS”, “gNB”, “TRP”, “AP”, “node B”, “5G NB”, and “cell” may be used interchangeably herein. 
     In some aspects, a cell may not necessarily be stationary, and the geographic area of the cell may move according to the location of a mobile BS. In some aspects, the BSs may be interconnected to one another and/or to one or more other BSs or network nodes (not shown) in the wireless network  100  through various types of backhaul interfaces such as a direct physical connection, a virtual network, and/or the like using any suitable transport network. 
     Wireless network  100  may also include relay stations. A relay station is an entity that can receive a transmission of data from an upstream station (e.g., a BS or a UE) and send a transmission of the data to a downstream station (e.g., a UE or a BS). A relay station may also be a UE that can relay transmissions for other UEs. In the example shown in  FIG. 1 , a relay BS  110   d  may communicate with macro BS  110   a  and a UE  120   d  in order to facilitate communication between BS  110   a  and UE  120   d . A relay BS may also be referred to as a relay station, a relay base station, a relay, and/or the like. 
     Wireless network  100  may be a heterogeneous network that includes BSs of different types, e.g., macro BSs, pico BSs, femto BSs, relay BSs, and/or the like. These different types of BSs may have different transmit power levels, different coverage areas, and different impacts on interference in wireless network  100 . For example, macro BSs may have a high transmit power level (e.g., 5 to 40 Watts) whereas pico BSs, femto BSs, and relay BSs may have lower transmit power levels (e.g., 0.1 to 2 Watts). 
     A network controller  130  may couple to a set of BSs and may provide coordination and control for these BSs. Network controller  130  may communicate with the BSs via a backhaul. The BSs may also communicate with one another, e.g., directly or indirectly via a wireless or wireline backhaul. 
     UEs  120  (e.g.,  120   a ,  120   b ,  120   c ) may be dispersed throughout wireless network  100 , and each UE may be stationary or mobile. A UE may also be referred to as an access terminal, a terminal, a mobile station, a subscriber unit, a station, and/or the like. A UE may be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, biometric sensors/devices, wearable devices (smart watches, smart clothing, smart glasses, smart wrist bands, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicular component or sensor, smart meters/sensors, industrial manufacturing equipment, a global positioning system device, or any other suitable device that is configured to communicate via a wireless or wired medium. 
     Some UEs may be considered machine-type communication (MTC) or evolved or enhanced machine-type communication (eMTC) UEs. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, and/or the like, that may communicate with a base station, another device (e.g., remote device), or some other entity. A wireless node may provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communication link. Some UEs may be considered Internet-of-Things (IoT) devices, and/or may be implemented as NB-IoT (narrowband internet of things) devices. Some UEs may be considered a Customer Premises Equipment (CPE). UE  120  may be included inside a housing that houses components of UE  120 , such as processor components, memory components, and/or the like. 
     In general, any number of wireless networks may be deployed in a given geographic area. Each wireless network may support a particular radio access technology (RAT) and may operate on one or more frequencies. A RAT may also be referred to as a radio technology, an air interface, and/or the like. A frequency may also be referred to as a carrier, a frequency channel, and/or the like. Each frequency may support a single RAT in a given geographic area in order to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed. 
     In some aspects, two or more UEs  120  (e.g., shown as UE  120   a  and UE  120   e ) may communicate directly using one or more sidelink channels (e.g., without using a base station  110  as an intermediary to communicate with one another). For example, the UEs  120  may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the like), a mesh network, and/or the like. In this case, the UE  120  may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the base station  110 . 
     As indicated above,  FIG. 1  is provided as an example. Other examples may differ from what is described with regard to  FIG. 1 . 
       FIG. 2  shows a block diagram of a design  200  of base station  110  and UE  120 , which may be one of the base stations and one of the UEs in  FIG. 1 . Base station  110  may be equipped with T antennas  234   a  through  234   t , and UE  120  may be equipped with R antennas  252   a  through  252   r , where in general T≥1 and R≥1. 
     At base station  110 , a transmit processor  220  may receive data from a data source  212  for one or more UEs, select one or more modulation and coding schemes (MCS) for each UE based at least in part on channel quality indicators (CQIs) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS(s) selected for the UE, and provide data symbols for all UEs. Transmit processor  220  may also process system information (e.g., for semi-static resource partitioning information (SRPI) and/or the like) and control information (e.g., CQI requests, grants, upper layer signaling, and/or the like) and provide overhead symbols and control symbols. Transmit processor  220  may also generate reference symbols for reference signals (e.g., the cell-specific reference signal (CRS)) and synchronization signals (e.g., the primary synchronization signal (PSS) and secondary synchronization signal (SSS)). A transmit (TX) multiple-input multiple-output (MIMO) processor  230  may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide T output symbol streams to T modulators (MODs)  232   a  through  232   t . Each modulator  232  may process a respective output symbol stream (e.g., for OFDM and/or the like) to obtain an output sample stream. Each modulator  232  may further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink signal. T downlink signals from modulators  232   a  through  232   t  may be transmitted via T antennas  234   a  through  234   t , respectively. According to various aspects described in more detail below, the synchronization signals can be generated with location encoding to convey additional information. 
     At UE  120 , antennas  252   a  through  252   r  may receive the downlink signals from base station  110  and/or other base stations and may provide received signals to demodulators (DEMODs)  254   a  through  254   r , respectively. Each demodulator  254  may condition (e.g., filter, amplify, downconvert, and digitize) a received signal to obtain input samples. Each demodulator  254  may further process the input samples (e.g., for OFDM and/or the like) to obtain received symbols. A MIMO detector  256  may obtain received symbols from all R demodulators  254   a  through  254   r , perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor  258  may process (e.g., demodulate and decode) the detected symbols, provide decoded data for UE  120  to a data sink  260 , and provide decoded control information and system information to a controller/processor  280 . A channel processor may determine reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), and/or the like. In some aspects, one or more components of UE  120  may be included in a housing. 
     On the uplink, at UE  120 , a transmit processor  264  may receive and process data from a data source  262  and control information (e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) from controller/processor  280 . Transmit processor  264  may also generate reference symbols for one or more reference signals. The symbols from transmit processor  264  may be precoded by a TX MIMO processor  266  if applicable, further processed by modulators  254   a  through  254   r  (e.g., for DFT-s-OFDM, CP-OFDM, and/or the like), and transmitted to base station  110 . At base station  110 , the uplink signals from UE  120  and other UEs may be received by antennas  234 , processed by demodulators  232 , detected by a MIMO detector  236  if applicable, and further processed by a receive processor  238  to obtain decoded data and control information sent by UE  120 . Receive processor  238  may provide the decoded data to a data sink  239  and the decoded control information to controller/processor  240 . Base station  110  may include communication unit  244  and communicate to network controller  130  via communication unit  244 . Network controller  130  may include communication unit  294 , controller/processor  290 , and memory  292 . 
     Controller/processor  240  of base station  110 , controller/processor  280  of UE  120 , and/or any other component(s) of  FIG. 2  may perform one or more techniques associated with a scheduling request for artificial intelligence (AI) information, as described in more detail elsewhere herein. For example, controller/processor  240  of base station  110 , controller/processor  280  of UE  120 , and/or any other component(s) of  FIG. 2  may perform or direct operations of, for example, process  400  of  FIG. 4 , process  500  of  FIG. 5 , and/or other processes as described herein. Memories  242  and  282  may store data and program codes for base station  110  and UE  120 , respectively. In some aspects, memory  242  and/or memory  282  may comprise a non-transitory computer-readable medium storing one or more instructions for wireless communication. For example, the one or more instructions, when executed by one or more processors of the base station  110  and/or the UE  120 , may perform or direct operations of, for example, process  400  of  FIG. 4 , process  500  of  FIG. 5 , and/or other processes as described herein. A scheduler  246  may schedule UEs for data transmission on the downlink and/or uplink. 
     In some aspects, UE  120  may include means for transmitting a scheduling request that includes information associated with an AI module of the UE; means for receiving an uplink grant for a resource allocation that is based at least in part on the AI module; means for performing the AI module; means for providing information determined using the AI module on the resource allocation; and/or the like. In some aspects, such means may include one or more components of UE  120  described in connection with  FIG. 2 , such as controller/processor  280 , transmit processor  264 , TX MIMO processor  266 , MOD  254 , antenna  252 , DEMOD  254 , MIMO detector  256 , receive processor  258 , and/or the like. 
     In some aspects, base station  110  may include means for receiving a scheduling request that includes information associated with an AI module associated with a UE; means for transmitting, to the UE, an uplink grant for a resource allocation that is based at least in part on the AI module; means for determining a selected AI operation based at least in part on the AI module indicated by the scheduling request; means for performing the selected AI module; means for receiving information determined using the AI module on the resource allocation; and/or the like. In some aspects, such means may include one or more components of base station  110  described in connection with  FIG. 2 , such as antenna  234 , DEMOD  232 , MIMO detector  236 , receive processor  238 , controller/processor  240 , transmit processor  220 , TX MIMO processor  230 , MOD  232 , antenna  234 , and/or the like. 
     As indicated above,  FIG. 2  is provided as an example. Other examples may differ from what is described with regard to  FIG. 2 . 
     A scheduling request (SR) is a message transmitted from a UE to a base station that requests an uplink grant identifying resources for data transmission by the UE, such as via a physical uplink shared channel (PUSCH). An SR may be transmitted via a physical uplink control channel (PUCCH) or via an uplink control information (UCI) carried by a PUSCH. The uplink grant may be conveyed to the UE via downlink control information (DCI). Multiple SR resources can be configured, where each SR resource may be associated with a different logical channel. An SR resource is a resource configured for transmission of an SR. For example, a base station may expect to receive an SR on an SR resource, and not on a resource other than an SR. 
     A UE may perform an AI operation based at least in part on an AI module, such as to determine information to be provided to a base station. For example, some AI modules use neural networks (NNs) for an encoder, such as an autoencoder, to encode uplink communications of the UE. An encoder (e.g., the UE) may use an encoder network (e.g., a NN) to encode an input into an encoded message, and a decoder (e.g., a BS) may use a decoder network to decode the encoded message and approximately reconstruct the input. This is an example of a cross-node AI module (e.g., ML module, NN module), meaning that one part of the AI module (e.g., the encoder network) is located at one node (e.g., the UE) and another part of the AI module (e.g., the decoder network) is located at another node (e.g., the BS). Another example of an AI module is a standalone AI module. A standalone AI module may be located at a single node (e.g., a UE or a BS). 
     In some examples, an encoder network and a decoder network may be trained jointly. One advantage of this approach is that autoencoders may not require knowledge of the underlying data distribution of the input or an explicit identification of a structure of the input. One example of an application for an NN based encoder/decoder is channel state information (CSI) feedback in massive multiple input multiple output (MIMO) systems. Since CSI feedback in MIMO frequency division duplex (FDD) systems is typically associated with significant overhead and relates to sparse channels, significant compression gains can be realized using an NN based encoder/decoder. 
     In some aspects, a UE and a BS may use multiple different AI modules to communicate. For example, a UE and a BS may have multiple different AI modules with corresponding parameters (e.g., biases) and weights. Different AI modules with different parameters and weights may produce outputs (e.g., encoded messages and/or the like) of different sizes and may require corresponding decoder AI modules for decoding. As another example, a decoder may benefit from knowing the weights and parameters of an encoder, or may not be capable of performing decoding without knowing the weights and parameters of the encoder. Further, it may be beneficial for weights and parameters of a standalone AI module to be provided for use by other nodes that use the standalone AI module. Still further, different sets of weights or parameters may have different sizes, so a uniform size of resource allocation may not be efficient for providing different sets of weights or parameters. 
     Some techniques and apparatuses described herein provide indication of an AI module associated with an SR or information associated with the AI module. For example, the information associated with the AI module may include an index of the AI module or a weight or parameter associated with the AI module. The base station may determine a size or configuration of a resource allocation for an uplink grant associated with an SR based at least in part on the AI module indicated by the SR. In some aspects, the SR may indicate the AI module so that the base station can select an appropriately-sized resource allocation via which the UE can provide weights and parameters for the AI module to the base station. In some aspects, the base station may determine a selected AI module corresponding to the AI module (e.g., a decoding AI module associated with an encoding AI module). In some aspects, the SR may indicate weights or parameters for an AI module to the base station, so that the base station can reuse the weights or parameters with another UE, thereby conserving computing resources that would otherwise be used to train AI modules of the base station and the other UE. Furthermore, the base station can identify a resource allocation of an appropriate size to provide the weights or parameters to the other UE. 
     In this way, an AI module associated with an SR may be indicated using the SR. This may enable the base station to determine an appropriate resource allocation size or configuration for the resource allocation associated with the SR, thereby reducing overhead associated with wasted resource allocations or undersized resource allocations. Still further, the base station can reuse weights or parameters indicated by one UE&#39;s SR for another UE, thereby conserving computing resources that would otherwise be used to train encoding and decoding AI modules of the base station and the other UE. 
       FIG. 3  is a diagram illustrating an example  300  of scheduling an uplink communication associated with an AI module, in accordance with various aspects of the present disclosure. As shown, example  300  includes a UE  120 - 1 , a UE  120 - 2 , and a BS  110 . 
     As shown in  FIG. 3 , and by reference number  305 , the UE  120 - 1  may perform an AI operation based at least in part on an AI module (e.g., using antenna  252 , DEMOD  254 , MIMO detector  256 , receive processor  258 , controller/processor  280 , and/or the like). In some aspects, the AI operation is to generate an encoded message, though the AI operation may be for any purpose. For example, the AI module may be an autoencoder AI module to generate an encoded message, such as CSI feedback for a massive MIMO configuration, though the encoded message may be associated with any sort of AI module. In some aspects, the encoded message may refer to any set of information that is generated by the AI module. It should be noted that the operation shown by reference number  305  is optional. For example, the UE  120  can perform the operations of example  300  without performing an AI operation and/or using an AI module (e.g., in order to provide weights or parameters associated with the AI module). The AI operation may be performed based at least in part on an AI module. For example, an AI module may indicate an input of the AI operation, one or more operations of the AI operation, weights and parameters associated with the AI operation, and/or the like. 
     The UE  120 - 1  may use particular weights and parameters for the AI module. For example, for a cross-node AI module, the UE  120 - 1  may use a particular configuration of an encoder NN to generate an encoded message, where a configuration is a set of weights and parameters. In this case, the UE  120 - 1  may select the particular configuration from a plurality of configurations of the encoder NN. In other words, the UE  120 - 1  may select the AI module from a plurality of AI modules associated with respective weights and/or parameters. In some aspects, the UE  120  may select the encoder NN from a plurality of encoder NNs associated with respective weights and/or parameters. In some aspects, the UE  120 - 1  and the BS  110  may train the encoder NN to determine the particular weights and parameters. Different encoder NNs may be associated with different sets of weights and parameters, which may have different sizes, for example, based at least in part on a size or configuration of the NN. 
     As shown by reference number  310 , the UE  120 - 1  may determine an SR associated with the AI module (e.g., using controller/processor  280 , transmit processor  264 , TX MIMO processor  266 , MOD  254 , antenna  252 , and/or the like). For example, the UE  120 - 1  may generate an SR that indicates the AI module. The SR may request a resource allocation on which to transmit information to the BS  110 , such as information regarding an AI module used by the UE  120 - 1  (e.g., an index of the AI module), one or more weights or parameters used for the AI module, and/or the like. Additionally, or alternatively, the SR may indicate the information regarding the AI module and/or the one or more weights or parameters. In some aspects, the index of the AI module may be configured (e.g., preconfigured) or standardized. For example, the UE  120 - 1  may be configured with a plurality of AI modules or AI operations associated with corresponding identifiers or indices. 
     As shown by reference number  315 , the UE  120 - 1  may transmit the SR (e.g., using controller/processor  280 , transmit processor  264 , TX MIMO processor  266 , MOD  254 , antenna  252 , and/or the like). As further shown, the SR may indicate the AI module. In some aspects, the SR may indicate which AI module was used by the UE  120 - 1 . For example, the SR may include or indicate an identifier (e.g., an index) corresponding to the AI module. In some aspects, the SR may indicate a set of weights or parameters associated with the AI module, which is described in more detail elsewhere herein. In some aspects, a resource used to transmit the SR may indicate the AI module. For example, the BS  110  may configure the UE  120  with SR resources that correspond to respective AI modules. The UE  120 - 1  may use an SR resource corresponding to the AI operation or AI module for the SR. In some aspects, a resource may correspond to two or more AI modules. In this case, a property of the SR may be used to differentiate the AI module indicated by the SR (e.g., a phase rotation of the SR and/or the like). In some aspects, the SR may include multiple bits that indicate the AI module or the set of weights or parameters. For example, a field of the PUCCH used for the SR may indicate the AI module or the set of weights or parameters. In some aspects, the SR may include or be associated with any form of AI-related information. 
     As shown by reference number  320 , the BS  110  may identify the AI module based at least in part on the SR (e.g., using antenna  234 , DEMOD  232 , MIMO detector  236 , receive processor  238 , controller/processor  240 , and/or the like). For example, the BS  110  may determine which AI module (e.g., a standalone AI module or a cross-node AI module) was used to generate the encoded message based at least in part on the SR. In some aspects, the BS  110  may identify the AI module based at least in part on which SR resource was used to transmit the SR. In some aspects, the BS  110  may identify the AI module based at least in part on a phase rotation or another property of the SR. In some aspects, the BS  110  may identify the AI module based at least in part on a parameter or weight identified by the SR and associated with the AI module. 
     In some aspects, the BS  110  may identify the AI module based at least in part on an explicit indication included in the SR. For example, the SR may include a field indicating AI-related information, such as an AI module, one or more weights or parameters associated with an AI module, or the like. One or more bits of the field may indicate the AI-related information. In some aspects, the field may be a field of a PUCCH or the like. 
     As shown by reference number  325 , the BS  110  may determine a resource allocation based at least in part on the AI module (e.g., using controller/processor  240  and/or the like). For example, different AI modules may be associated with different sets of weights and/or parameters. The BS  110  may provide a resource allocation of a size corresponding to a set of weights and/or parameters of the AI module associated with the SR. For example, the size of the resource allocation may be sufficient for the UE  120  to transmit AI-related information associated with the AI module. In this way, the BS  110  may more efficiently communicate AI-related information with the UE  120 - 1 , and may provide the weights and parameters to another UE  120 - 1  using an appropriately-sized downlink resource allocation. 
     As shown by reference number  330 , the BS  110  may transmit the uplink grant indicating the resource allocation selected based at least in part on the AI module (e.g., using controller/processor  240 , transmit processor  220 , TX MIMO processor  230 , MOD  232 , antenna  234 , and/or the like). As shown by reference number  335 , the UE  120 - 1  may transmit a transmission using the uplink grant. For example, the UE  120  may transmit AI-related information, such as one or more weights or parameters for the AI module, or may transmit other information on the uplink grant (e.g., an encoded message and/or the like). 
     As shown by reference number  340 , in some aspects, the BS  110  may provide the one or more weights or parameters and/or an AI module index to the UE  120 - 2  (e.g., using controller/processor  240 , transmit processor  220 , TX MIMO processor  230 , MOD  232 , antenna  234 , and/or the like). The BS  110  may provide the one or more weights or parameters to the UE  120 - 2  using any form of signaling, such as downlink control information, radio resource control signaling, a medium access control control element, higher-layer signaling, and/or the like. In some aspects, the UE  120 - 2  may have a similar configuration as the UE  120 - 1 . For example, the UE  120 - 1  and the UE  120 - 2  may have a same antenna configuration, may have a same processor, may be of a same make and/or model, and/or the like. In some aspects, the BS  110  may provide the one or more weights or parameters and/or the AI module index for the one or more weights and parameters to the UE  120 - 2  based at least in part on the UE  120 - 1  and the UE  120 - 2  having similar configurations. In some aspects, the BS  110  may provide weights or parameters for multiple different AI modules to the UE  120 - 2 . By providing the weights or parameters for the AI module to the UE  120 - 2 , the BS  110  enables the UE  120 - 2  to use the AI module (such as for a standalone AI operation) without jointly training a model used to perform the AI module, thereby conserving computing resources of the BS  110  and the UE  120 - 2 . 
     In some aspects, the BS  110  and the UE  120 - 2  may train an AI module based at least in part on the weights, parameters, and/or AI module index. For example, the BS  110  and the UE  210 - 2  may use the weights and/or the parameters as initial values for training an AI module, corresponding to the AI module index, to perform the AI operation. In this way, the BS  110  and the UE  120  may conserve computing resources that would otherwise be used to train the AI module from scratch. 
     As shown by reference number  345 , the BS  110  and the UE  120 - 2  may communicate based at least in part on the AI module. For example, the BS  110  and the UE  120 - 2  may perform one or more of the operations described in  FIG. 3  using the weights and/or parameters of the AI module to perform the AI operation. 
     As indicated above,  FIG. 3  is provided as an example. Other examples may differ from what is described with respect to  FIG. 3 . 
       FIG. 4  is a diagram illustrating an example process  400  performed, for example, by a UE, in accordance with various aspects of the present disclosure. Example process  400  is an example where the UE (e.g., UE  120  and/or the like) performs operations associated with an SR for AI-related information. 
     As shown in  FIG. 4 , in some aspects, process  400  may include transmitting a scheduling request that includes information associated with an artificial intelligence module of the user equipment (block  410 ). For example, the UE (e.g., using controller/processor  280 , transmit processor  264 , TX MIMO processor  266 , MOD  254 , antenna  252 , and/or the like) may transmit a scheduling request that includes information associated with an artificial intelligence module of the UE, as described above. 
     As further shown in  FIG. 4 , in some aspects, process  400  may include receiving an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module (block  420 ). For example, the UE (e.g., using antenna  252 , DEMOD  254 , MIMO detector  256 , receive processor  258 , controller/processor  280 , and/or the like) may receive an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module, as described above. 
     Process  400  may include additional aspects, such as any single aspect or any combination of aspects described below and/or in connection with one or more other processes described elsewhere herein. 
     With respect to process  400 , in a first aspect, the artificial intelligence module is one of a plurality of artificial intelligence modules that can be performed by the UE, and the artificial intelligence module is selected by the UE. 
     With respect to process  400 , in a second aspect, alone or in combination with the first aspect, the artificial intelligence module comprises a channel state information feedback encoding operation. 
     With respect to process  400 , in a third aspect, alone or in combination with one or more of the first and second aspects, the information indicating the artificial intelligence module indicates at least one of one or more weights or one or more parameters for the artificial intelligence module. 
     With respect to process  400 , in a fourth aspect, alone or in combination with one or more of the first through third aspects, process  400  includes performing the artificial intelligence module, and providing at least one of one or more weights or one or more parameters using the resource allocation. 
     With respect to process  400 , in a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the artificial intelligence module is a preferred artificial intelligence module selected by the user equipment. 
     With respect to process  400 , in a sixth aspect, alone or in combination with one or more of the first through fifth aspects, a size of the resource allocation or a configuration of the resource allocation is based at least in part on the information indicating the artificial intelligence module. 
     With respect to process  400 , in a seventh aspect, alone or in combination with one or more of the first through sixth aspects, a phase of a transmission of the scheduling request indicates the information indicating the artificial intelligence module. 
     With respect to process  400 , in an eighth aspect, alone or in combination with one or more of the first through seventh aspects, the information indicating the artificial intelligence module comprises one or more bits of the scheduling request. 
     With respect to process  400 , in a ninth aspect, alone or in combination with one or more of the first through eighth aspects, the resource allocation is based at least in part on which scheduling request resource is used to transmit the scheduling request. 
     With respect to process  400 , in a tenth aspect, alone or in combination with one or more of the first through ninth aspects, the scheduling request indicates the artificial intelligence module based at least in part on a scheduling request resource used to transmit the scheduling request. 
     Although  FIG. 4  shows example blocks of process  400 , in some aspects, process  400  may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in  FIG. 4 . Additionally, or alternatively, two or more of the blocks of process  400  may be performed in parallel. 
       FIG. 5  is a diagram illustrating an example process  500  performed, for example, by a base station, in accordance with various aspects of the present disclosure. Example process  500  is an example where the base station (e.g., BS  110  and/or the like) performs operations associated with an SR associated with AI-related information. 
     As shown in  FIG. 5 , in some aspects, process  500  may include receiving a scheduling request that includes information associated with an artificial intelligence module of a user equipment (block  510 ). For example, the base station (e.g., using antenna  234 , DEMOD  232 , MIMO detector  236 , receive processor  238 , controller/processor  240 , and/or the like) may receive a scheduling request that includes information associated with an artificial intelligence module of a user equipment, as described above. 
     As further shown in  FIG. 5 , in some aspects, process  500  may include transmitting, to the user equipment, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module (block  520 ). For example, the base station (e.g., using controller/processor  240 , transmit processor  220 , TX MIMO processor  230 , MOD  232 , antenna  234 , and/or the like) may transmit, to the user equipment, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module, as described above. 
     Process  500  may include additional aspects, such as any single aspect or any combination of aspects described below and/or in connection with one or more other processes described elsewhere herein. 
     With respect to process  500 , in a first aspect, process  500  includes determining a selected artificial intelligence module based at least in part on the artificial intelligence module indicated by the scheduling request, and performing the selected artificial intelligence module. 
     With respect to process  500 , in a second aspect, alone or in combination with the first aspect, the artificial intelligence module indicated by the scheduling request comprises a channel state information feedback encoding operation, and the selected artificial intelligence module comprises a channel state information feedback decoding operation. 
     With respect to process  500 , in a third aspect, alone or in combination with one or more of the first and second aspects, the artificial intelligence module is one of a plurality of artificial intelligence modules that can be performed by the user equipment, and the artificial intelligence module was selected by the user equipment. 
     With respect to process  500 , in a fourth aspect, alone or in combination with one or more of the first through third aspects, the information indicating the artificial intelligence module indicates one or more weights or one or more parameters for the artificial intelligence module. 
     With respect to process  500 , in a fifth aspect, alone or in combination with one or more of the first through fourth aspects, process  500  includes receiving information determined using the artificial intelligence module on the resource allocation. 
     With respect to process  500 , in a sixth aspect, alone or in combination with one or more of the first through fifth aspects, the artificial intelligence module is a preferred artificial intelligence module that was selected by the user equipment. 
     With respect to process  500 , in a seventh aspect, alone or in combination with one or more of the first through sixth aspects, a size of the resource allocation or a configuration of the resource allocation is based at least in part on the information indicating the artificial intelligence module. 
     With respect to process  500 , in an eighth aspect, alone or in combination with one or more of the first through seventh aspects, a phase of a transmission of the scheduling request indicates the information indicating the artificial intelligence module. 
     With respect to process  500 , in a ninth aspect, alone or in combination with one or more of the first through eighth aspects, the information indicating the artificial intelligence module comprises one or more bits of the scheduling request. 
     With respect to process  500 , in a tenth aspect, alone or in combination with one or more of the first through ninth aspects, the process  500  further comprises providing information associated with the artificial intelligence module to another UE (e.g., UE  120 - 2 ). 
     With respect to process  500 , in an eleventh aspect, alone or in combination with one or more of the first through tenth aspects, a size of a downlink resource allocation used to provide the information associated with the artificial intelligence module to the other UE is based at least in part on the information indicating the artificial intelligence module. 
     Although  FIG. 5  shows example blocks of process  500 , in some aspects, process  500  may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in  FIG. 5 . Additionally, or alternatively, two or more of the blocks of process  500  may be performed in parallel. 
     Implementation examples are described in the following numbered aspects: 
     Aspect 1: A method of wireless communication performed by a user equipment, comprising: transmitting a scheduling request that includes information associated with an artificial intelligence module of the user equipment; and receiving an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     Aspect 2: The method of Aspect 1, wherein the artificial intelligence module is one of a plurality of artificial intelligence modules associated with the user equipment, and wherein the artificial intelligence module is selected by the user equipment. 
     Aspect 3: The method of any of Aspects 1-2, wherein the artificial intelligence module comprises a channel state information feedback encoding operation. 
     Aspect 4: The method of any of Aspects 1-3, wherein a size of the resource allocation or a configuration of the resource allocation is based at least in part on the information indicating the artificial intelligence module. 
     Aspect 5: The method of any of Aspects 1-4, wherein a phase of a transmission of the scheduling request indicates the information indicating the artificial intelligence module. 
     Aspect 6: The method of any of Aspects 1-5, wherein the information indicating the artificial intelligence module comprises one or more bits of the scheduling request. 
     Aspect 7: The method of any of Aspects 1-6, wherein the resource allocation is based at least in part on which scheduling request resource is used to transmit the scheduling request. 
     Aspect 8: The method of any of Aspects 1-7, wherein the scheduling request indicates the artificial intelligence module based at least in part on a scheduling request resource used to transmit the scheduling request. 
     Aspect 9: The method of any of Aspects 1-8, wherein the information indicating the artificial intelligence module indicates at least one of one or more weights or one or more parameters for the artificial intelligence module. 
     Aspect 10: The method of any of Aspects 1-9, wherein the method includes performing an artificial intelligence operation based at least in part on the artificial intelligence module; and providing information determined using the artificial intelligence module on the resource allocation. 
     Aspect 11: The apparatus of any of Aspects 1-10, wherein the artificial intelligence module is a preferred artificial intelligence module selected by the user equipment. 
     Aspect 12: A method of wireless communication performed by a base station, comprising: receiving a scheduling request that includes information associated with an artificial intelligence module of a user equipment; and transmitting, to the user equipment, an uplink grant for a resource allocation that is based at least in part on the artificial intelligence module. 
     Aspect 13: The method of Aspect 12, further comprising: determining a selected artificial intelligence module based at least in part on the artificial intelligence module indicated by the scheduling request; and performing an operation based at least in part on the selected artificial intelligence module. 
     Aspect 14: The method of any of Aspects 12-13, wherein the artificial intelligence module indicated by the scheduling request comprises a channel state information feedback encoding operation and the selected artificial intelligence module comprises a channel state information feedback decoding operation. 
     Aspect 15: The method of any of Aspects 12-14, wherein the artificial intelligence module is one of a plurality of artificial intelligence modules associated with the user equipment, and wherein the artificial intelligence module was selected by the user equipment. 
     Aspect 16: The method of any of Aspects 12-15, wherein the information indicating the artificial intelligence module indicates one or more weights or one or more parameters for the artificial intelligence module. 
     Aspect 17: The method of any of Aspects 12-16, further comprising receiving information determined using the artificial intelligence module on the resource allocation. 
     Aspect 18: The method of any of Aspects 12-17, wherein the artificial intelligence module is a preferred artificial intelligence module that was selected by the user equipment. 
     Aspect 19: The method of any of Aspects 12-18, wherein a size of the resource allocation or a configuration of the resource allocation is based at least in part on the information indicating the artificial intelligence module. 
     Aspect 20: The method of any of Aspects 12-19, wherein a phase of a transmission of the scheduling request indicates the information indicating the artificial intelligence module. 
     Aspect 21: The method of any of Aspects 12-20, wherein the information indicating the artificial intelligence module comprises one or more bits of the scheduling request. 
     Aspect 22: The method of any of Aspects 12-21, further comprising: providing information associated with the artificial intelligence module to another UE. 
     Aspect 23: The method of any of Aspects 12-22, wherein a size of a downlink resource allocation used to provide the information associated with the artificial intelligence module to the other UE is based at least in part on the information indicating the artificial intelligence module 
     Aspect 24: The method of any of Aspects 12-23, wherein the scheduling request indicates the artificial intelligence module based at least in part on a scheduling request resource used to transmit the scheduling request. 
     Aspect 25: An apparatus for wireless communications at a UE, comprising a memory and one or more processors coupled to the memory, the memory and the one or more processors configured to perform the method of one or more aspects of Aspects 1-11. 
     Aspect 26: An apparatus for wireless communication, comprising at least one means for performing the method of one or more aspects of Aspects 1-11. 
     Aspect 27: A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising one or more instructions that, when executed by one or more processors of a UE, cause the device to perform the method of one or more aspects of Aspects 1-11. 
     Aspect 28: An apparatus for wireless communications at a base station, comprising a memory and one or more processors coupled to the memory, the memory and the one or more processors configured to perform the method of one or more aspects of Aspects 12-24. 
     Aspect 29: An apparatus for wireless communication, comprising at least one means for performing the method of one or more aspects of Aspects 12-24. 
     Aspect 30: A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising one or more instructions that, when executed by one or more processors of a base station, cause the base station to perform the method of one or more aspects of Aspects 12-24. 
     The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise form disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the aspects. 
     As used herein, the term “component” is intended to be broadly construed as hardware, firmware, and/or a combination of hardware and software. As used herein, a processor is implemented in hardware, firmware, and/or a combination of hardware and software. 
     As used herein, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, and/or the like. 
     It will be apparent that systems and/or methods described herein may be implemented in different forms of hardware, firmware, and/or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the aspects. Thus, the operation and behavior of the systems and/or methods were described herein without reference to specific software code—it being understood that software and hardware can be designed to implement the systems and/or methods based, at least in part, on the description herein. 
     Even though particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of various aspects includes each dependent claim in combination with every other claim in the claim set. A phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c). 
     No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Furthermore, as used herein, the terms “set” and “group” are intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, and/or the like), and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” and/or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.