Dispatch requests associated with AI information

Abstract

In general, various aspects of the present disclosure relate to wireless communications. In some aspects, a user device may send a scheduling request including information associated with an artificial intelligence module of the user device; and receive an uplink grant for resource allocation based at least in part on the artificial intelligence module. Numerous other aspects are provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to the following applications: U.S. Provisional Patent Application No. 62 / 948,126, filed on December 13, 2019, entitled “SCHEDULING REQUEST ASSOCIATED WITH ARTIFICIAL INTELLIGENCE INFORMATION”; and U.S. Non-Provisional Patent Application No. 16 / 949,916, filed on November 20, 2020, entitled “SCHEDULING REQUEST ASSOCIATED WITH ARTIFICIAL INTELLIGENCE INFORMATION”, and the above applications are hereby expressly incorporated herein by reference. Technical Field

[0003]

[0006] Generally speaking, aspects of the present disclosure relate to wireless communications and to techniques and apparatus for scheduling requests (SRs) associated with artificial intelligence (AI) information. Background Art

[0004] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). 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 released by the Third Generation Partnership Project (3GPP).

[0005] A wireless communication network may include multiple base stations (BSs) that can support communications for multiple user equipments (UEs). User equipments (UEs) may communicate with base stations (BSs) via downlinks and uplinks. A downlink (or forward link) refers to a communication link from a BS to a UE, while an uplink (or reverse link) refers to a communication link from a UE to a BS. As will be described in more detail herein, a BS may be referred to as a Node B, gNB, access point (AP), radio head, transmit receive point (TRP), new radio (NR) BS, 5G Node B, etc.

[0006] The above multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different user equipment to communicate at the city, country, region, and even global level. New Radio (NR) (which may also be referred to as 5G) is an enhancement set to the LTE mobile standard released by the Third Generation Partnership Project (3GPP). NR is designed to better integrate with other open standards by improving spectrum efficiency, reducing costs, improving services, utilizing new spectrum, and using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with cyclic prefix (CP) on the downlink (DL), using CP-OFDM and / or SC-FDM (e.g., also referred to as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink (UL), so as to better support mobile broadband Internet access, as well as support beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation. However, as the demand for mobile broadband access continues to grow, there is a need for further improvements in LTE and NR technologies. Preferably, these improvements should be applicable to other multiple access technologies and telecommunication standards that adopt these technologies. Summary of the invention

[0007] In some aspects, a method of wireless communication performed by a user equipment (UE) may include: sending a scheduling request including information associated with an artificial intelligence module of the UE; and receiving an uplink grant for resource allocation based at least in part on the artificial intelligence module.

[0008] In some aspects, a method of wireless communication performed by a base station may include receiving a scheduling request including information associated with an artificial intelligence module associated with a UE; and sending an uplink grant to the UE for resource allocation based at least in part on the artificial intelligence module.

[0009] In some aspects, a UE for wireless communication may include a memory and one or more processors coupled to the memory. The memory and the one or more processors may be configured to: send a scheduling request including information associated with an artificial intelligence module of the UE; and receive an uplink grant for resource allocation based at least in part on the artificial intelligence module.

[0010] In some aspects, a base station for wireless communication may include a 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 including information associated with an artificial intelligence module associated with a UE; and send an uplink grant to the UE for resource allocation based at least in part on the artificial intelligence module.

[0011] 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: send a scheduling request including information associated with an artificial intelligence module of the UE; and receive an uplink grant for resource allocation based at least in part on the artificial intelligence module.

[0012] 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 including information associated with an artificial intelligence module associated with a UE; and send an uplink grant to the UE for resource allocation based at least in part on the artificial intelligence module.

[0013] In some aspects, an apparatus for wireless communication may include means for sending a scheduling request including information associated with an artificial intelligence module of the apparatus; and means for receiving an uplink grant for resource allocation based at least in part on the artificial intelligence module.

[0014] In some aspects, an apparatus for wireless communication may include means for receiving a scheduling request including information associated with an artificial intelligence module associated with a UE; and means for sending, to the UE, an uplink grant for resource allocation based at least in part on the artificial intelligence module.

[0015] In summary, aspects include methods, apparatus, systems, computer program products, non-transitory computer readable media, user equipment, base stations, wireless communication devices, and / or processing systems as fully described herein with reference to and as illustrated by the accompanying drawings.

[0016] The foregoing has been fairly broadly summarized according to the features and technical advantages of the examples of the present disclosure, so that the following detailed description can be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples can be easily used as the basis for modifying or designing other structures for the same purpose of achieving the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. When considered in conjunction with the accompanying drawings, the characteristics of the concepts disclosed herein (both their organization and method of operation) and the associated advantages will be better understood according to the description below. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description, and is not intended to be a definition of the limitations of the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to fully understand the above-mentioned features of the present disclosure, a more specific description of the invention briefly summarized above can be obtained by referring to various aspects (some of which are shown in the accompanying drawings). However, it should be noted that the accompanying drawings only illustrate certain typical aspects of the present disclosure and are therefore not considered to limit the scope of the present disclosure, as the description may allow for other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0018] Figure 1 is a diagram illustrating an example of a wireless communication network in accordance with various aspects of the present disclosure.

[0019] Figure 2 is a diagram illustrating an example of a base station communicating with a UE in a wireless communication network according to various aspects of the present disclosure.

[0020] Figure 3 is a diagram illustrating an example of scheduling uplink communications associated with an AI module according to various aspects of the present disclosure.

[0021] Figure 4 is a diagram illustrating example processes performed, for example, by a UE, according to various aspects of the present disclosure.

[0022] Figure 5 is a diagram illustrating example processes performed, for example, by a base station in accordance with various aspects of the present disclosure. DETAILED DESCRIPTION

[0023] The following is a more complete description of various aspects of the present disclosure with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms and should not be construed as being limited to any specific structure or function presented throughout the present disclosure. More specifically, these aspects are provided so that the present disclosure will be thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art. Based on the teachings herein, it should be understood by those skilled in the art that the scope of the present disclosure is intended to cover any aspect of the present disclosure disclosed herein, whether the aspect is implemented independently of any other aspect of the present disclosure or implemented in combination with any other aspect. For example, using any number of aspects set forth herein, a device can be implemented or a method can be implemented. In addition, the scope of the present disclosure is intended to cover such a device or method implemented using other structures, functions, or structures and functions other than the various aspects of the present disclosure set forth herein or different from the various aspects of the present disclosure set forth herein. It should be understood that any aspect of the present disclosure disclosed herein can be embodied by one or more elements of the claims.

[0024] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices 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, etc. (collectively referred to as "elements"). These elements may be implemented using hardware, software, or a combination thereof. Whether such elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0025] It should be noted that although various aspects may be described herein using terms commonly associated with 3G and / or 4G wireless technologies, various aspects of the present disclosure may be applied to communication systems based on other generations, such as 5G and beyond (including NR technology).

[0026] Figure 1 1 is a diagram showing a wireless network 100 in which various aspects of the present disclosure may be implemented. The wireless network 100 may be an LTE network or some other wireless network (e.g., a 5G or NR network). The wireless network 100 may include a plurality of BSs 110 (shown as BSs 110a, BSs 110b, BSs 110c, and BSs 110d) and other network entities. A BS is an entity that communicates with a user equipment (UE) and may also be referred to as a base station, NR BS, Node B, gNB, 5G Node B (NB), access point, transmit receive point (TRP), etc. Each BS may provide communication coverage for a particular geographic area. In 3GPP, the term "cell" may refer to a coverage area of ​​a BS and / or a BS subsystem serving the coverage area, depending on the context in which the term is used.

[0027] 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 residence) and may allow restricted access by UEs associated with the femto cell (e.g., UEs in a closed subscriber group (CSG)). A BS for a macro cell may be referred to as a macro BS. A BS 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 Figure 1In the example shown in , BS 110a may be a macro BS for macro cell 102a, BS 110b may be a pico BS for pico cell 102b, and BS 110c may be a femto BS for femto cell 102c. A BS may support one or more (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.

[0028] In some aspects, the cells may not necessarily be stationary, and the geographic area of ​​the cells may move depending on the location of the mobile BS. In some aspects, the BSs may be interconnected with each other and / or with one or more other BSs or network nodes (not shown) in the wireless network 100 through various types of backhaul interfaces (e.g., direct physical connections, virtual networks, etc.) using any suitable transport network.

[0029] The wireless network 100 may also include a relay station. A relay station is an entity that can receive data transmissions from an upstream station (e.g., a BS or a UE) and send data transmissions to a downstream station (e.g., a UE or a BS). A relay station may also be a UE that is capable of relaying transmissions for other UEs. Figure 1 In the example shown in , a relay BS 110d may communicate with a macro BS 110a and a UE 120d to facilitate communication between the BS 110a and the UE 120d. A relay BS may also be referred to as a relay station, a relay base station, a relay, or the like.

[0030] The wireless network 100 may be a heterogeneous network including different types of BSs (e.g., macro BSs, pico BSs, femto BSs, relay BSs, etc.). These different types of BSs may have different transmit power levels, different coverage areas, and different effects on interference in the wireless network 100. For example, a macro BS may have a high transmit power level (e.g., 5 to 40 watts), while a pico BS, a femto BS, and a relay BS may have a lower transmit power level (e.g., 0.1 to 2 watts).

[0031] A network controller 130 may be coupled to a set of BSs and may provide coordination and control for these BSs. The network controller 130 may communicate with the BSs via a backhaul. The BSs may also communicate with each other directly or indirectly, for example, via a wireless or wired backhaul.

[0032] UE 120 (e.g., 120a, 120b, 120c) can be dispersed throughout the wireless network 100, and each UE can be stationary or mobile. UE can also be referred to as an access terminal, terminal, mobile station, subscriber unit, station, etc. UE can 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 device, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or apparatus, a biometric sensor / device, a wearable device (smart watch, smart clothing, smart glasses, smart wristband, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio unit), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing device, a global positioning system device, or any other suitable device configured to communicate via a wireless or wired medium.

[0033] Some UEs may be considered as 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, etc., which may communicate with a base station, another device (e.g., a remote device), or some other entity. A wireless node may provide a connection to a network (e.g., a wide area network such as the Internet or a cellular network) or a connection to a network, for example, via a wired or wireless communication link. Some UEs may be considered as Internet of Things (IoT) devices, and / or may be implemented as NB-IoT (narrowband Internet of Things) devices. Some UEs may be considered as customer premises equipment (CPE). UE 120 may be included inside a housing that houses components of UE 120 (such as a processor component, a memory component, etc.).

[0034] Generally, any number of wireless networks can be deployed in a given geographic area. Each wireless network can support a specific radio access technology (RAT) and can operate on one or more frequencies. RAT can also be referred to as radio technology, air interface, etc. Frequency can also be referred to as carrier, frequency channel, etc. Each frequency can support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks can be deployed.

[0035] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using base station 110 as an intermediary to communicate with each other). For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, etc.), mesh networks, etc. In this case, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by base station 110.

[0036] As noted above, Figure 1 is provided as an example. Other examples may differ from those described above. Figure 1 Examples described.

[0037] Figure 2 Base station 110 and UE 120 (which may be Figure 1 1. Block diagram of a design 200 of a base station 110 and a UE 120. Base station 110 may be equipped with T antennas 234a through 234t, and UE 120 may be equipped with R antennas 252a through 252r, where in general, T≥1 and R≥1.

[0038] At the base station 110, the transmit processor 220 may receive data for one or more UEs from the data source 212, select one or more modulation and coding schemes (MCS) for each UE based at least in part on a channel quality indicator (CQI) received from the UE, process (e.g., encode and modulate) the data for the UE based at least in part on the MCS selected for each UE, and provide data symbols for all UEs. The transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI), etc.) and control information (e.g., CQI requests, grants, upper layer signaling, etc.), and provide overhead symbols and control symbols. The transmit processor 220 may also generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS)) and synchronization signals (e.g., primary synchronization signals (PSS) and secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, if applicable, and may provide T output symbol streams to T modulators (MOD) 232a to 232t. Each modulator 232 may process a corresponding output symbol stream (e.g., for OFDM, etc.) to obtain an output sample stream. Each modulator 232 may further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. T downlink signals from modulators 232a to 232t may be transmitted via T antennas 234a to 234t, respectively. According to various aspects described in more detail below, a synchronization signal may be generated using position coding to transmit additional information.

[0039] At UE 120, antennas 252a to 252r may receive downlink signals from base station 110 and / or other base stations, and may provide received signals to demodulators (DEMODs) 254a to 254r, respectively. Each demodulator 254 may condition (e.g., filter, amplify, downconvert, and digitize) the received signal to obtain input samples. Each demodulator 254 may further process the input samples (e.g., for OFDM, etc.) to obtain received symbols. MIMO detector 256 may obtain received symbols from all R demodulators 254a to 254r, perform MIMO detection on the received symbols (if applicable), and provide detected symbols. Receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for UE 120 to data sink 260, and provide decoded control information and system information to controller / processor 280. The channel processor may determine reference signal received power (RSRP), received signal strength indicator (RSSI), reference signal received quality (RSRQ), channel quality indicator (CQI), etc. In some aspects, one or more components of UE 120 may be included in a housing.

[0040] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information from a controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, CQI, etc.). The transmit processor 264 may also generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by a TX MIMO processor 266 (if applicable), further processed by modulators 254a through 254r (e.g., for DFT-s-OFDM, CP-OFDM, etc.), and transmitted to the base station 110. At the base station 110, uplink signals from the UE 120 and other UEs may be received by the antenna 234, processed by the demodulator 232, detected by the MIMO detector 236 (if applicable), and further processed by the receive processor 238 to obtain decoded data and control information transmitted by the UE 120. The receive processor 238 may provide decoded data to a data sink 239 and provide decoded control information to a controller / processor 240. The base station 110 may include a communication unit 244 and communicate with the network controller 130 via the communication unit 244. The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292.

[0041] The controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2Any other component in the system may perform one or more techniques associated with scheduling requests for artificial intelligence (AI) information, as described in more detail elsewhere herein. For example, the controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component in may perform or direct e.g. Figure 4 The process of 400 Figure 5 The operations of the process 500 and / or other processes as described herein. The memories 242 and 282 may store data and program codes for the base station 110 and the UE 120, respectively. In some aspects, the memory 242 and / or the memory 282 may include a non-transitory computer-readable medium storing one or more instructions for wireless communication. For example, when the one or more instructions are executed by one or more processors of the base station 110 and / or the UE 120, they may perform or direct, for example, Figure 4 The process of 400 Figure 5 The scheduler 246 may schedule UEs for data transmission on the downlink and / or uplink.

[0042] In some aspects, UE 120 may include: means for sending a scheduling request including information associated with an AI module of the UE; means for receiving an uplink grant for resource allocation based at least in part on the AI ​​module; means for executing the AI ​​module; means for providing information regarding resource allocation determined using the AI ​​module; and the like. In some aspects, such means may include combining Figure 2 One or more components of UE 120 are depicted, such as controller / processor 280, transmit processor 264, TX MIMO processor 266, MOD 254, antenna 252, DEMOD 254, MIMO detector 256, receive processor 258, etc.

[0043] In some aspects, the base station 110 may include: a unit for receiving a scheduling request including information associated with an AI module associated with the UE; a unit for sending an uplink grant to the UE for resource allocation based at least in part on the AI ​​module; a unit for determining a selected AI operation based at least in part on the AI ​​module indicated by the scheduling request; a unit for executing the selected AI module; a unit for receiving information about resource allocation determined using the AI ​​module; and the like. In some aspects, such a unit may include a unit for combining Figure 2One or more components of base station 110 are depicted, 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 the like.

[0044] As noted above, Figure 2 is provided as an example. Other examples may differ from those described above. Figure 2 Examples described.

[0045] A Scheduling Request (SR) is a message sent from a UE to a base station requesting an uplink grant identifying resources for data transmission by the UE, for example via a Physical Uplink Shared Channel (PUSCH). The SR may be sent via a Physical Uplink Control Channel (PUCCH) or via Uplink Control Information (UCI) carried by the PUSCH. The uplink grant may be transmitted to the UE via Downlink Control Information (DCI). Multiple SR resources may be configured, each of which may be associated with a different logical channel. An SR resource is a resource configured for the transmission of an SR. For example, a base station may desire to receive an SR on an SR resource rather than on resources other than the SR.

[0046] The UE may perform AI operations based at least in part on the AI ​​module, for example, to determine information to be provided to the base station. For example, some AI modules use a neural network (NN) for an encoder (such as an autoencoder) to encode the uplink communication of the UE. The encoder (e.g., UE) can use an encoder network (e.g., NN) to encode the input into an encoded message, and the decoder (e.g., BS) can 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., encoder network) is located at one node (e.g., UE) and another part of the AI ​​module (e.g., decoder network) is located at another node (e.g., BS). Another example of an AI module is an independent AI module. An independent AI module can be located at a single node (e.g., UE or BS).

[0047] In some examples, the encoder network and the decoder network can be trained jointly. One advantage of this approach is that the autoencoder may not require knowledge of the underlying data distribution of the input or explicit identification of the structure of the input. An example of an application for NN-based encoders / decoders is channel state information (CSI) feedback in large-scale multiple-input multiple-output (MIMO) systems. Since CSI feedback in MIMO frequency division duplex (FDD) systems is typically associated with significant overhead and is related to sparse channels, significant compression gains can be achieved using NN-based encoders / decoders.

[0048] In some aspects, the UE and the BS may communicate using multiple different AI modules. For example, the UE and the 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 of different sizes (e.g., encoded messages, etc.), and may require corresponding decoder AI modules for decoding. As another example, the decoder may benefit from knowing the weights and parameters of the encoder, or may not be able to perform decoding without knowing the weights and parameters of the encoder. In addition, it may be beneficial to provide the weights and parameters of independent AI modules for use by other nodes using independent AI modules. Furthermore, different weights or parameter sets may have different sizes, so a uniform resource allocation size may not be efficient for providing different weights or parameter sets.

[0049] Some techniques and apparatus described herein provide an 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 the size or configuration of a resource allocation for an uplink grant associated with the 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 may select a resource allocation of an appropriate size, and the UE may provide the weights and parameters for the AI ​​module to the base station via the resource allocation. In some aspects, the base station may determine the selected AI module corresponding to the AI ​​module (e.g., a decoding AI module associated with the encoding AI module). In some aspects, the SR may indicate to the base station the weights or parameters for the AI ​​module so that the base station may reuse the weights or parameters with another UE, thereby saving computational resources that would otherwise be used to train the AI ​​modules of the base station and another UE. In addition, the base station may identify a resource allocation of an appropriate size to provide weights or parameters to another UE.

[0050] In this way, an SR may be used to indicate an AI module associated with 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 insufficient resource allocation sizes. Further, the base station may reuse weights or parameters indicated by an SR for one UE for another UE, thereby saving computational resources that would otherwise be used to train the encoding and decoding AI modules of the base station and the other UE.

[0051] Figure 3 1 is a diagram illustrating an example 300 of scheduling uplink communications associated with an AI module in accordance with various aspects of the present disclosure. As shown, example 300 includes UE 120-1, UE 120-2, and BS 110.

[0052] As in Figure 3 In and as shown by reference numeral 305, 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, receiving processor 258, controller / processor 280, etc.). In some aspects, although the AI ​​operation is to generate a coded message, the AI ​​operation may be used for any purpose. For example, although the AI ​​module may be an autoencoder AI module for generating a coded message (such as CSI feedback for a massive MIMO configuration), the coded message may be associated with any kind of AI module. In some aspects, the coded message may refer to any set of information generated by the AI ​​module. It should be noted that the operation shown by reference numeral 305 is optional. For example, UE 120 may perform the operation of example 300 without performing an AI operation and / or using an AI module (e.g., to provide weights or parameters associated with the AI ​​module). The AI ​​operation may be performed at least in part based on the AI ​​module. For example, the AI ​​module may indicate inputs to the AI ​​operation, one or more operations of the AI ​​operation, weights and parameters associated with the AI ​​operation, etc.

[0053] UE 120-1 may use specific weights and parameters for AI modules. For example, for cross-node AI modules, UE 120-1 may use a specific configuration of the encoder NN to generate an encoded message, where the configuration is a set of weights and parameters. In this case, UE 120-1 may select a specific configuration from multiple configurations of the encoder NN. In other words, UE 120-1 may select an AI module from multiple AI modules associated with corresponding weights and / or parameters. In some aspects, UE 120 may select an encoder NN from multiple encoder NNs associated with corresponding weights and / or parameters. In some aspects, UE 120-1 and BS 110 may train the encoder NN to determine specific weights and parameters. Different encoder NNs may be associated with different weights and parameter sets, which may have different sizes, for example, based at least in part on the size or configuration of the NN.

[0054] As shown in reference numeral 310, UE 120-1 may determine an SR associated with an AI module (e.g., using controller / processor 280, transmit processor 264, TX MIMO processor 266, MOD 254, antenna 252, etc.). For example, UE 120-1 may generate an SR indicating the AI ​​module. The SR may request resource allocation on which to send information to BS 110 (such as information about an AI module used by UE 120-1 (e.g., an index of the AI ​​module), one or more weights or parameters for the AI ​​module, etc.). Additionally or alternatively, the SR may indicate information about the AI ​​module and / or one or more weights or parameters. In some aspects, the index of the AI ​​module may be configured (e.g., pre-configured) or standardized. For example, UE 120-1 may be configured with multiple AI modules or AI operations associated with corresponding identifiers or indexes.

[0055] As indicated by reference numeral 315, UE 120-1 may send an SR (e.g., using controller / processor 280, transmit processor 264, TX MIMO processor 266, MOD 254, antenna 252, etc.). As further shown, the SR may indicate an AI module. In some aspects, the SR may indicate which AI module is used by 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 weight or parameter set associated with the AI ​​module, which is described in more detail elsewhere herein. In some aspects, the resource used to send the SR may indicate the AI ​​module. For example, BS 110 may configure UE 120 with SR resources corresponding to the corresponding AI module. UE 120-1 may use SR resources corresponding to an AI operation or AI module for the SR. In some aspects, the resource may correspond to two or more AI modules. In this case, the characteristics of the SR may be used to distinguish the AI ​​modules indicated by the SR (e.g., phase rotation of the SR, etc.). In some aspects, the SR may include multiple bits indicating the AI ​​module or the weight or parameter set. For example, a field of the PUCCH for the SR may indicate an AI module or a set of weights or parameters.In some aspects, the SR may include or be associated with any form of AI-related information.

[0056] As shown in reference numeral 320, BS 110 may identify an 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, etc.). For example, BS 110 may determine which AI module (e.g., an independent AI module or a cross-node AI module) is used to generate an encoded message based at least in part on the SR. In some aspects, BS 110 may identify an AI module based at least in part on which SR resource is used to send the SR. In some aspects, BS 110 may identify an AI module based at least in part on a phase rotation or another characteristic of the SR. In some aspects, BS 110 may identify an AI module based at least in part on a parameter or weight identified by the SR and associated with the AI ​​module.

[0057] In some aspects, 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 the AI ​​module, one or more weights or parameters associated with the AI ​​module, etc.). 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, etc.

[0058] As shown in reference numeral 325, BS 110 may determine resource allocation based at least in part on the AI ​​module (e.g., using controller / processor 240, etc.). For example, different AI modules may be associated with different weights and / or parameter sets. BS 110 may provide resource allocations of a size corresponding to the weights and / or parameter sets of the AI ​​modules associated with the SR. For example, the size of the resource allocation may be sufficient for UE 120 to send AI-related information associated with the AI ​​module. In this way, BS 110 may more efficiently communicate AI-related information with UE 120-1, and may provide weights and parameters to another UE 120-1 using appropriately sized downlink resource allocations.

[0059] As shown at reference numeral 330, BS 110 may send an uplink grant indicating a 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, etc.). As shown at reference numeral 335, UE 120-1 may send a transmission using the uplink grant. For example, UE 120 may send AI-related information (such as one or more weights or parameters for the AI ​​module), or may send other information about the uplink grant (e.g., an encoded message, etc.).

[0060] As shown by reference numeral 340, in some aspects, BS 110 may provide one or more weights or parameters and / or AI module indexes to UE 120-2 (e.g., using controller / processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, etc.). BS 110 may provide one or more weights or parameters to UE 120-2 using any form of signaling (such as downlink control information, radio resource control signaling, medium access control elements, higher layer signaling, etc.). In some aspects, UE 120-2 may have a similar configuration as UE 120-1. For example, UE 120-1 and UE 120-2 may have the same antenna configuration, may have the same processor, may have the same brand and / or model, etc. In some aspects, BS 110 may provide one or more weights or parameters and / or an AI module index for one or more weights and parameters to UE 120-2 based at least in part on UE 120-1 and UE 120-2 having similar configurations. In some aspects, BS 110 may provide weights or parameters for multiple different AI modules to UE 120-2. By providing weights or parameters for AI modules to UE 120-2, BS 110 enables UE 120-2 to use the AI ​​modules (e.g., for independent AI operations) without jointly training models for executing the AI ​​modules, thereby saving computing resources of BS 110 and UE 120-2.

[0061] In some aspects, BS 110 and UE 120-2 may train the AI ​​module based at least in part on the weights, parameters, and / or AI module index. For example, BS 110 and UE 210-2 may use the weights and / or parameters as initial values ​​for training the AI ​​module corresponding to the AI ​​module index to perform the AI ​​operation. In this way, BS 110 and UE 120 may save computing resources that would otherwise be used to train the AI ​​module from scratch.

[0062] As shown at reference numeral 345, BS 110 and UE 120-2 may communicate based at least in part on the AI ​​module. For example, BS 110 and UE 120-2 may use weights and / or parameters of the AI ​​module to perform Figure 3 to perform an AI operation.

[0063] As noted above, Figure 3 is provided as an example. Other examples may differ from those described above. Figure 3 Examples described.

[0064] Figure 44 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 of operations in which a UE (eg, UE 120, etc.) performs SR for AI-related information.

[0065] like Figure 4 As shown in , in some aspects, process 400 may include sending a scheduling request including information associated with an artificial intelligence module of a user equipment (block 410). For example, a UE (e.g., using controller / processor 280, transmit processor 264, TX MIMO processor 266, MOD 254, antenna 252, etc.) may send a scheduling request including information associated with an artificial intelligence module of the UE, as described above.

[0066] like Figure 4 As further shown in FIG. 4 , in some aspects, process 400 may include receiving an uplink grant for resource allocation 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, etc.) may receive an uplink grant for resource allocation based at least in part on the artificial intelligence module, as described above.

[0067] Process 400 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0068] With respect to process 400, in a first aspect, the artificial intelligence module is one of a plurality of artificial intelligence modules executable by the UE, and the artificial intelligence module is selected by the UE.

[0069] With respect to process 400, in a second aspect, either alone or in combination with the first aspect, the artificial intelligence module includes a channel state information feedback encoding operation.

[0070] Regarding process 400, in a third aspect, alone or in combination with one or more of the first and second aspects, the information indicative of the artificial intelligence module indicates at least one of the one or more weights or one or more parameters for the artificial intelligence module.

[0071] Regarding process 400, in a fourth aspect, alone or in combination with one or more of the first to third aspects, process 400 includes: executing an artificial intelligence module; and using resource allocation to provide at least one of one or more weights or one or more parameters.

[0072] With respect to process 400, in a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the artificial intelligence module is a preferred artificial intelligence module selected by the user device.

[0073] With respect to process 400, in a sixth aspect, alone or in combination with one or more of the first to fifth aspects, a size of a resource allocation or a configuration of a resource allocation is based at least in part on information indicative of an artificial intelligence module.

[0074] Regarding process 400, in a seventh aspect, alone or in combination with one or more of the first to sixth aspects, a phase indication of transmission of a scheduling request: information indicating an artificial intelligence module.

[0075] Regarding process 400, in an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the information indicating the artificial intelligence module includes one or more bits of a scheduling request.

[0076] With respect to process 400, in a ninth aspect, alone or in combination with one or more of the first to eighth aspects, resource allocation is based at least in part on which scheduling request resource is used to send the scheduling request.

[0077] With respect to process 400, in a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the scheduling request indicates an artificial intelligence module based at least in part on a scheduling request resource used to send the scheduling request.

[0078] Although Figure 4 Example blocks of process 400 are shown, but in some aspects process 400 may include Figure 4 The blocks depicted in the process 400 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 400. Additionally or alternatively, two or more blocks in the blocks of process 400 may be executed in parallel.

[0079] Figure 5 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 of operations in which a base station (eg, BS 110, etc.) performs SR associated with AI-related information.

[0080] like Figure 5As shown in , in some aspects, process 500 may include: receiving a scheduling request including information associated with an artificial intelligence module of a user device (block 510). For example, a base station (e.g., using antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller / processor 240, etc.) may receive a scheduling request including information associated with an artificial intelligence module of a user device, as described above.

[0081] like Figure 5 As further shown in FIG. 5 , in some aspects, process 500 may include sending an uplink grant to a user equipment for resource allocation based at least in part on the artificial intelligence module (block 520). For example, a base station (e.g., using controller / processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, etc.) may send an uplink grant to a user equipment for resource allocation based at least in part on the artificial intelligence module, as described above.

[0082] Process 500 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0083] With respect to process 500 , in a first aspect, process 500 includes: determining a selected artificial intelligence module based at least in part on an artificial intelligence module indicated by a scheduling request; and executing the selected artificial intelligence module.

[0084] With respect to process 500, in a second aspect, either alone or in combination with the first aspect, the artificial intelligence module indicated by the scheduling request includes a channel state information feedback encoding operation, and the selected artificial intelligence module includes a channel state information feedback decoding operation.

[0085] Regarding 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 executable by a user device, and the artificial intelligence module is selected by the user device.

[0086] Regarding process 500 , in a fourth aspect, alone or in combination with one or more of the first to third aspects, the information indicative of the artificial intelligence module indicates one or more weights or one or more parameters for the artificial intelligence module.

[0087] Regarding process 500, in a fifth aspect, alone or in combination with one or more of the first to fourth aspects, process 500 includes: receiving information about resource allocation determined using an artificial intelligence module.

[0088] With respect to process 500, in a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the artificial intelligence module is a preferred artificial intelligence module selected by the user device.

[0089] With respect to process 500, in a seventh aspect, alone or in combination with one or more of the first to sixth aspects, a size of a resource allocation or a configuration of a resource allocation is based at least in part on information indicative of an artificial intelligence module.

[0090] Regarding process 500, in an eighth aspect, alone or in combination with one or more of the first to seventh aspects, a phase indication of transmission of a scheduling request: information indicating an artificial intelligence module.

[0091] Regarding process 500, in a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the information indicating the artificial intelligence module includes one or more bits of a scheduling request.

[0092] Regarding process 500, in the tenth aspect, alone or in combination with one or more of the first to ninth aspects, process 500 also includes: providing information associated with the artificial intelligence module to another UE (eg, UE 120-2).

[0093] Regarding process 500, in an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, a size of a downlink resource allocation for providing information associated with an artificial intelligence module to another UE is based at least in part on information indicative of the artificial intelligence module.

[0094] Although Figure 5 Example blocks of process 500 are shown, but in some aspects process 500 may include Figure 5 The blocks depicted in the process 500 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 500. Additionally or alternatively, two or more blocks in the blocks of process 500 may be executed in parallel.

[0095] Implementation examples are described in the following numbered aspects:

[0096] Aspect 1: A method of wireless communication performed by a user equipment, comprising: sending a scheduling request including information associated with an artificial intelligence module of the user equipment; and receiving an uplink grant for resource allocation based at least in part on the artificial intelligence module.

[0097] Aspect 2: The method according to Aspect 1, wherein the artificial intelligence module is one of a plurality of artificial intelligence modules associated with the user device, and wherein the artificial intelligence module is selected by the user device.

[0098] Aspect 3: The method according to any one of Aspects 1-2, wherein the artificial intelligence module includes a channel state information feedback encoding operation.

[0099] Aspect 4: The method according to any one of Aspects 1-3, wherein the size of the resource allocation or the configuration of the resource allocation is based at least in part on the information indicative of the artificial intelligence module.

[0100] Aspect 5: The method according to any one of Aspects 1-4, wherein the phase indication of the transmission of the scheduling request: indicates the information of the artificial intelligence module.

[0101] Aspect 6: The method according to any one of aspects 1-5, wherein the information indicating the artificial intelligence module includes one or more bits of the scheduling request.

[0102] Aspect 7: The method according to any one of aspects 1-6, wherein the resource allocation is based at least in part on which scheduling request resource is used to send the scheduling request.

[0103] Aspect 8: The method according to any one 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 send the scheduling request.

[0104] Aspect 9: The method according to any one 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.

[0105] Aspect 10: A method according to any one of aspects 1-9, wherein the method includes: performing artificial intelligence operations based at least in part on the artificial intelligence module; and providing information about the resource allocation determined using the artificial intelligence module.

[0106] Aspect 11: The method according to any one of Aspects 1-10, wherein the artificial intelligence module is a preferred artificial intelligence module selected by the user equipment.

[0107] Aspect 12: A method of wireless communication performed by a base station, comprising: receiving a scheduling request including information associated with an artificial intelligence module of a user equipment; and sending an uplink grant to the user equipment for resource allocation based at least in part on the artificial intelligence module.

[0108] Aspect 13: The method according to aspect 12 also includes: 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.

[0109] Aspect 14: A method according to any one of Aspects 12-13, wherein the artificial intelligence module indicated by the scheduling request includes a channel state information feedback encoding operation, and the selected artificial intelligence module includes a channel state information feedback decoding operation.

[0110] Aspect 15: A method according to any one of Aspects 12-14, wherein the artificial intelligence module is one of a plurality of artificial intelligence modules associated with the user device, and wherein the artificial intelligence module is selected by the user device.

[0111] Aspect 16: The method according to any one of Aspects 12-15, wherein the information indicative of the artificial intelligence module indicates one or more weights or one or more parameters for the artificial intelligence module.

[0112] Aspect 17: The method according to any one of Aspects 12-16 further includes: receiving information about the resource allocation determined using the artificial intelligence module.

[0113] Aspect 18: The method according to any one of Aspects 12-17, wherein the artificial intelligence module is a preferred artificial intelligence module selected by the user equipment.

[0114] Aspect 19: The method according to any one of Aspects 12-18, wherein the size of the resource allocation or the configuration of the resource allocation is based at least in part on the information indicative of the artificial intelligence module.

[0115] Aspect 20: The method according to any one of Aspects 12-19, wherein the phase indication of the transmission of the scheduling request: indicates the information of the artificial intelligence module.

[0116] Aspect 21: A method according to any one of aspects 12-20, wherein the information indicating the artificial intelligence module includes one or more bits of the scheduling request.

[0117] Aspect 22: The method according to any one of aspects 12-21 further includes: providing information associated with the artificial intelligence module to another UE.

[0118] Aspect 23: A method according to any one of aspects 12-22, wherein the size of the 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.

[0119] Aspect 24: The method according to any one 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 send the scheduling request.

[0120] Aspect 25: An apparatus for wireless communication at a UE, comprising a memory and one or more processors coupled to the memory, the memory and the one or more processors being configured to execute the method according to one or more of aspects 1-11.

[0121] Aspect 26: An apparatus for wireless communication, comprising at least one unit for performing the method according to one or more of aspects 1-11.

[0122] Aspect 27: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions, which, when executed by one or more processors of a UE, causes the device to perform a method according to one or more of aspects 1-11.

[0123] Aspect 28: An apparatus for wireless communication at a base station, comprising a memory and one or more processors coupled to the memory, the memory and the one or more processors being configured to perform the method according to one or more of aspects 12-24.

[0124] Aspect 29: An apparatus for wireless communication, comprising at least one unit for performing the method according to one or more of aspects 12-24.

[0125] Aspect 30: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions, which, when executed by one or more processors of a base station, causes the base station to perform a method according to one or more of aspects 12-24.

[0126] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the various aspects.

[0127] As used herein, the term "component" is intended to be broadly interpreted as hardware, firmware, and / or a combination of hardware and software. As used herein, a "processor" is implemented with hardware, firmware, and / or a combination of hardware and software.

[0128] As used herein, satisfying a threshold may refer to a value being greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0129] It will be apparent that the systems and / or methods described herein may be implemented with various forms of hardware, firmware, and / or combinations of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting in any respect. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, it being understood that software and hardware may be designed to implement the systems and / or methods based, at least in part, on the description herein.

[0130] Even if the specific combination of features is recorded 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 can be combined in a manner that is not specifically recorded in the claims and / or specifically disclosed in the specification. Although each dependent claim listed below can only directly depend on one claim, the disclosure of various aspects includes the combination of each dependent claim with each other claim in the claim set. The phrase "at least one of" referring to the list of items refers to any combination of those items, including a single member. For example, "at least one of a, b or c" is intended to cover a, b, c, ab, ac, bc and abc, and any combination with multiples of the same element (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc and ccc or any other ordering of a, b and c).

[0131] None of the elements, actions or instructions used herein should be interpreted as key or necessary, unless explicitly described as such. In addition, as used herein, the articles "a" and "an" are intended to include one or more projects, and can be used interchangeably with "one or more". In addition, as used herein, the terms "set" and "group" are intended to include one or more projects (e.g., related projects, unrelated projects, combinations of related projects and unrelated projects, etc.), and can be used interchangeably with "one or more". In the case of only one project being expected, the phrase "only one" or similar language is used. In addition, as used herein, the terms "has", "have", "having" and / or similar terms are intended to be open terms. In addition, unless otherwise explicitly stated, the phrase "based on" is intended to mean "based at least in part on".

Claims

1. An apparatus for wireless communication at a user equipment, comprising: Memory; as well as one or more processors coupled to the memory, the memory and the one or more processors being configured to: sending a scheduling request for requesting resource allocation, wherein the scheduling request includes information associated with an artificial intelligence module of a plurality of artificial intelligence modules of the user equipment, and the information indicates one or more weights and / or parameters for encoding uplink communications of the user equipment by means of an encoder neural network associated with a corresponding set of weights and / or parameters; and An uplink grant for the resource allocation is received via downlink control information, wherein the resource allocation has a size corresponding to the set of weights and / or parameters.

2. The device according to claim 1, wherein: The artificial intelligence module is selected by the user device.

3. The device according to claim 1, wherein: The artificial intelligence module includes a channel state information feedback encoding operation.

4. The device according to claim 1, wherein: The size of the resource allocation or the configuration of the resource allocation is based at least in part on the information indicative of the artificial intelligence module.

5. The device according to claim 1, wherein: Phase indication of transmission of the scheduling request: indicating the information of the artificial intelligence module.

6. The device according to claim 1, wherein: The information indicative of the artificial intelligence module includes one or more bits of the scheduling request.

7. The device according to claim 1, wherein: The resource allocation is based at least in part on which scheduling request resource is used to send the scheduling request.

8. The device according to claim 1, wherein: The scheduling request instructs the artificial intelligence module based at least in part on a scheduling request resource used to send the scheduling request.

9. The device according to claim 1, wherein: The one or more processors are configured to: performing artificial intelligence operations based at least in part on the artificial intelligence module; and Information regarding the resource allocation determined using the artificial intelligence module is provided.

10. The device according to claim 1, wherein: The artificial intelligence module is a preferred artificial intelligence module selected by the user device.

11. An apparatus for wireless communication at a network entity, comprising: Memory; as well as one or more processors coupled to the memory, the memory and the one or more processors being configured to: Receiving a scheduling request for requesting resource allocation, wherein the scheduling request includes information associated with an artificial intelligence module of a plurality of artificial intelligence modules of a user equipment, and the information indicates one or more weights and / or parameters for encoding uplink communications of the user equipment by means of an encoder neural network associated with a corresponding set of weights and / or parameters; determining a resource allocation having a size corresponding to the set of weights and / or parameters; and An uplink grant for the resource allocation is sent to the user equipment via downlink control information.

12. The device according to claim 11, wherein The one or more processors are configured to: determining a selected artificial intelligence module based at least in part on the artificial intelligence module indicated by the scheduling request; and An operation is performed based at least in part on the selected artificial intelligence module.

13. The device according to claim 12, wherein: The artificial intelligence module indicated by the scheduling request includes a channel state information feedback encoding operation, and the selected artificial intelligence module includes a channel state information feedback decoding operation.

14. The device according to claim 11, wherein: The artificial intelligence module is selected by the user device.

15. The device according to claim 11, wherein The one or more processors are configured to: Information regarding the resource allocation determined using the artificial intelligence module is received.

16. The device according to claim 11, wherein The artificial intelligence module is a preferred artificial intelligence module selected by the user device.

17. The device according to claim 11, wherein: The size of the resource allocation or the configuration of the resource allocation is based at least in part on the information indicative of the artificial intelligence module.

18. The device according to claim 11, wherein Phase indication of transmission of the scheduling request: indicating the information of the artificial intelligence module.

19. The device according to claim 11, wherein: The information indicative of the artificial intelligence module includes one or more bits of the scheduling request.

20. The device according to claim 11, wherein The one or more processors are configured to: Information associated with the artificial intelligence module is provided to another UE.

21. The device according to claim 20, 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 indicative of the artificial intelligence module.

22. The device according to claim 11, wherein The scheduling request instructs the artificial intelligence module based at least in part on a scheduling request resource used to send the scheduling request.

23. A method of wireless communication performed by a user equipment, comprising: sending a scheduling request for requesting resource allocation, wherein the scheduling request includes information associated with an artificial intelligence module of a plurality of artificial intelligence modules of the user equipment, and the information indicates one or more weights and / or parameters for encoding uplink communications of the user equipment by means of an encoder neural network associated with a corresponding set of weights and / or parameters; and An uplink grant for the resource allocation is received via downlink control information, wherein the resource allocation has a size corresponding to the set of weights and / or parameters.

24. The method according to claim 23, wherein: The artificial intelligence module includes a channel state information feedback encoding operation.

25. The method according to claim 23, wherein: The size of the resource allocation or the configuration of the resource allocation is based at least in part on the information indicative of the artificial intelligence module.

26. A method of wireless communication performed by a network entity, comprising: Receiving a scheduling request for requesting resource allocation, wherein the scheduling request includes information associated with an artificial intelligence module of a plurality of artificial intelligence modules of a user equipment, and the information indicates one or more weights and / or parameters for encoding uplink communications of the user equipment by means of an encoder neural network associated with a corresponding set of weights and / or parameters; determining a resource allocation having a size corresponding to the set of weights and / or parameters; and An uplink grant for the resource allocation is sent to the user equipment via downlink control information.

27. The method according to claim 26, further comprising: determining a selected artificial intelligence module based at least in part on the artificial intelligence module indicated by the dispatch request; as well as An operation is performed based at least in part on the selected artificial intelligence module.

28. The method according to claim 26, wherein: The size of the resource allocation or the configuration of the resource allocation is based at least in part on the information indicative of the artificial intelligence module.

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