Updated artificial intelligence or machine learning capability reporting

Abstract

Aspects of the present disclosure generally relate to wireless communications. In some aspects, a user equipment (UE) may send an update to a previously reported set of artificial intelligence (AI) or machine learning (ML) capabilities of the UE. The UE may receive a configuration associated with performance of the UE AI or ML operation based at least in part on the update. Numerous other aspects are described.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. non-provisional patent application No. 17 / 662,528, entitled “UPDATED ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING CAPABILITIES REPORTING,” filed on May 9, 2022, which is hereby expressly incorporated herein by reference. Technical Field

[0003] Aspects of the present disclosure relate generally to wireless communications, and to techniques and apparatus for updated artificial intelligence or machine learning capability reporting. Background Art

[0004] Wireless communication systems are widely deployed to provide a variety of 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 promulgated by the Third Generation Partnership Project (3GPP).

[0005] A wireless network may include one or more base stations (or network nodes) that support communications for one user equipment (UE) or multiple UEs. A UE may communicate with a base station via downlink communications and uplink communications. A "downlink" (or "DL") refers to the communication link from a base station to a UE, and an "uplink" (or "UL") refers to the communication link from a UE to a base station.

[0006] The above-mentioned multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate at a city, country, region and / or global level. New Radio (NR) (which may be referred to as 5G) is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with a cyclic prefix (CP) on the downlink, using CP-OFDM and / or single carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink, and supporting beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation to improve spectrum efficiency, reduce costs, improve services, utilize new spectrum, and better integrate with other open standards. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR and other radio access technologies remain useful. Summary of the invention

[0007] Some aspects described herein relate to a method of wireless communication performed by a user equipment (UE). The method may include sending an update to a previously reported set of UE artificial intelligence (AI) or machine learning (ML) capabilities. The method may include receiving a configuration associated with the performance of UE AI or ML operations based at least in part on the update.

[0008] Some aspects described herein relate to a method of wireless communication performed by a base station. The method may include receiving an update to a previously reported set of UE AI or ML capabilities. The method may include sending a configuration associated with the performance of UE AI or ML operations based at least in part on the update.

[0009] Some aspects described herein relate to a UE for wireless communication. The user equipment may include a memory and one or more processors, the one or more processors coupled to the memory. The one or more processors may be configured to send an update to a previously reported set of UE AI or ML capabilities. The one or more processors may be configured to receive a configuration associated with the performance of UE AI or ML operations based at least in part on the update.

[0010] Some aspects described herein relate to a base station for wireless communication. The base station may include a memory and one or more processors, the one or more processors coupled to the memory. The one or more processors may be configured to receive an update to a previously reported set of UE AI or ML capabilities. The one or more processors may be configured to send a configuration associated with the performance of UE AI or ML operations based at least in part on the update.

[0011] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a UE. The instruction set, when executed by one or more processors of the UE, may cause the UE to send an update to a previously reported set of UE AI or ML capabilities. The instruction set, when executed by one or more processors of the UE, may cause the UE to receive a configuration associated with the execution of UE AI or ML operations based at least in part on the update.

[0012] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication performed by a base station. The instruction set, when executed by one or more processors of the base station, may cause the base station to receive an update to a previously reported set of UE AI or ML capabilities. The instruction set, when executed by one or more processors of the base station, may cause the base station to send a configuration associated with the performance of UE AI or ML operations based at least in part on the update.

[0013] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for sending an update to a previously reported set of AI or ML capabilities. The apparatus may include means for receiving a configuration associated with the performance of an AI or ML operation based at least in part on the update.

[0014] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for receiving an update to a previously reported set of UE AI or ML capabilities. The apparatus may include means for sending a configuration associated with performance of UE AI or ML operations based at least in part on the update.

[0015] The various aspects generally include methods, apparatuses, 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 the drawings and description and as illustrated in the drawings and description.

[0016] The features and technical advantages of the examples according to the present disclosure have been outlined quite extensively above so that the following specific embodiments may be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for achieving the same purpose of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, both in terms of their organization and method of operation, and the associated advantages will be better understood by considering the following description in conjunction with the accompanying drawings. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description and not as a definition of limitations to the claims.

[0017] Although various aspects are described in the present disclosure by illustrating some examples, it will be understood by those skilled in the art that such aspects can be implemented in many different arrangements and scenarios. The technology described herein can be implemented using different platform types, devices, systems, shapes, sizes and / or packaging arrangements. For example, some aspects can be implemented via integrated chip implementations or other devices based on non-module components (e.g., end-user devices, vehicles, communication equipment, computing equipment, industrial equipment, retail / shopping equipment, medical equipment and / or artificial intelligence devices). Various aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components and / or system-level components. The equipment incorporating the various aspects and features described may include additional components and features for implementing and practicing the various aspects claimed and described. For example, the transmission and reception of wireless signals may include one or more components (e.g., hardware components, including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders and / or summers) for analog purposes and digital purposes. The various aspects described herein are intended to be practiced in various devices, components, systems, distributed arrangements and / or end-user devices of various sizes, shapes and configurations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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 illustrated 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 to be considered as limiting the scope thereof, as the specification may admit of other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0019] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0020] Figure 2 is a diagram illustrating an example of communication between a base station and a user equipment (UE) in a wireless network according to the present disclosure.

[0021] Figure 3 is a diagram illustrating an example decomposed base station architecture according to the present disclosure.

[0022] Figure 4 is a diagram illustrating an example associated with an updated artificial intelligence or machine learning capabilities report according to the present disclosure.

[0023] Figure 5 and Figure 6 is a diagram illustrating an example process associated with an updated artificial intelligence or machine learning capabilities report in accordance with the present disclosure.

[0024] Figure 7 and Figure 8 is a diagram of an example apparatus for wireless communications according to the present disclosure. DETAILED DESCRIPTION

[0025] The various aspects of the present disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms, and should not be interpreted as being limited to any specific structure or function presented throughout the present disclosure. On the contrary, 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. 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 disclosure disclosed herein, whether it is implemented independently or in combination with any other aspect of the disclosure. For example, any number of aspects set forth herein may be used to implement a device or practice method. In addition, the scope of the present disclosure is intended to cover such devices or methods that are practiced using other structures, functionality, or structures and functionality in addition to or different from 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 the claims.

[0026] 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.

[0027] Although various aspects may be described herein using terms generally associated with 5G or new radio (NR) radio access technology (RAT), various aspects of the present disclosure may be applicable to other RATs, such as 3G RAT, 4G RAT, and / or post-5G RATs (e.g., 6G).

[0028] Figure 11 is a diagram illustrating an example of a wireless network 100 according to the present disclosure. The wireless network 100 may be a 5G (e.g., NR) network and / or a 4G (e.g., Long Term Evolution (LTE)) network or may include elements of a 5G (e.g., NR) network and / or elements of a 4G (e.g., Long Term Evolution (LTE)) network, etc. The wireless network 100 may include one or more base stations 110 (shown as BS110a, BS110b, BS110c, and BS110d), a user equipment (UE) 120 or multiple UEs 120 (shown as UE 120a, UE120b, UE 120c, UE 120d, and UE 120e), and / or other network entities. The base station 110 is an entity that communicates with the UE 120. The base station 110 (sometimes referred to as a BS) may include, for example, an NR base station, an LTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G), an access point, and / or a transmit receive point (TRP). Each base station 110 may provide communication coverage for a particular geographic area. In the Third Generation Partnership Project (3GPP), the term "cell" may refer to the coverage area of ​​a base station 110 and / or a base station subsystem serving the coverage area, depending on the context in which the term is used.

[0029] Base station 110 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., a radius of several kilometers) and may allow unrestricted access by UE 120 with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UE 120 with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UE 120 associated with the femto cell (e.g., UE 120 in a closed subscriber group (CSG)). A base station 110 for a macro cell may be referred to as a macro base station. A base station 110 for a pico cell may be referred to as a pico base station. A base station 110 for a femto cell may be referred to as a femto base station or a home base station. In Figure 1 In the example shown, BS 110a may be a macro base station for macrocell 102a, BS 110b may be a pico base station for picocell 102b, and BS 110c may be a femto base station for femtocell 102c. A base station may support one or more (eg, three) cells.

[0030] In some examples, the cell may not necessarily be stationary, and the geographic area of ​​the cell may move depending on the location of the mobile base station 110 (e.g., a mobile base station). In some examples, the base stations 110 may be interconnected with each other and / or to one or more other base stations 110 or network nodes (not shown) in the wireless network 100 using any suitable transport network through various types of backhaul interfaces (such as direct physical connections or virtual networks).

[0031] The wireless network 100 may include one or more relay stations. A relay station is an entity that can receive transmissions of data from an upstream station (e.g., a base station 110 or a UE 120) and transmit transmissions of data to a downstream station (e.g., a UE 120 or a base station 110). A relay station may be a UE 120 that is capable of relaying transmissions for other UEs 120. Figure 1 In the example shown, BS 110d (eg, a relay base station) may communicate with BS 110a (eg, a macro base station) and UE 120d to facilitate communication between BS 110a and UE 120d. A base station 110 that relays communications may be referred to as a relay station, relay base station, relay, or the like.

[0032] The wireless network 100 may be a heterogeneous network including different types of base stations 110, such as macro base stations, pico base stations, femto base stations, relay base stations, etc. These different types of base stations 110 may have different transmit power levels, different coverage areas, and / or different impacts on interference in the wireless network 100. For example, a macro base station may have a high transmit power level (e.g., 5 watts to 40 watts), while a pico base station, a femto base station, and a relay base station may have a lower transmit power level (e.g., 0.1 watt to 2 watts).

[0033] A network controller 130 may be coupled to or in communication with a set of base stations 110 and may provide coordination and control for the base stations 110. The network controller 130 may communicate with the base stations 110 via backhaul communication links. The base stations 110 may also communicate directly with each other or indirectly via wireless or wired backhaul communication links.

[0034] UE 120 may be distributed throughout wireless network 100, and each UE 120 may be stationary or mobile. UE 120 may include, for example, an access terminal, a terminal, a mobile station, and / or a subscriber unit. UE 120 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 computer, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring or a smart bracelet)), an entertainment device (e.g., a music device, a video device, and / or a satellite radio), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing equipment, a global positioning system device, and / or any other suitable device configured to communicate via a wireless medium.

[0035] Some UEs 120 may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags that may communicate with a base station, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered as Internet of Things (IoT) devices and / or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered as customer premises equipment. UE 120 may be included inside a housing that houses components of UE 120, such as a processor component and / or a memory component. In some examples, the processor component and the memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., a memory) may be operably coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0036] In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a specific RAT and may operate on one or more frequencies. RAT may be referred to as a radio technology, air interface, etc. Frequency may be referred to as a carrier, frequency channel, etc. Each frequency may 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 may be deployed.

[0037] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly (e.g., without using base station 110 as an intermediary to communicate with each other) using one or more side link channels. 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, or vehicle-to-pedestrian (V2P) protocols), and / or mesh networks. In such examples, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by base station 110.

[0038] The devices of the wireless network 100 may communicate using an electromagnetic spectrum that may be subdivided into various categories, bands, channels, etc., based on frequency or wavelength. For example, the devices of the wireless network 100 may communicate using one or more operating bands. In 5G NR, two initial operating bands have been identified as frequency ranges designated FR1 (410 MHz–7.125 GHz) and FR2 (24.25 GHz–52.6 GHz). It should be understood that, although portions of FR1 are greater than 6 GHz, FR1 is often (interchangeably) referred to as the “below 6 GHz” band in various documents and articles. Similar naming issues sometimes occur with respect to FR2, which is often (interchangeably) referred to as the “millimeter wave” band in documents and articles, although different from the extremely high frequency (EHF) band (30 GHz–300 GHz) identified as the “millimeter wave” band by the International Telecommunication Union (ITU).

[0039] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz–24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and therefore can effectively extend the features of FR1 and / or FR2 to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6GHz–71GHz), FR4 (52.6GHz–114.25GHz), and FR5 (114.25GHz–300GHz). Each of these higher frequency bands falls within the EHF band.

[0040] Considering the above examples, unless otherwise specifically stated, it should be understood that if the term "below 6 GHz" or the like is used herein, the term may broadly refer to frequencies that may be lower than 6 GHz, may be within FR1, or may include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that if the term "millimeter wave" or the like is used herein, the term may broadly refer to frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band. It is contemplated that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0041] In some aspects, UE 120 may include a communications manager 140. As described in greater detail elsewhere herein, communications manager 140 may send an update to a previously reported set of UE AI or ML capabilities; and receive a configuration associated with the performance of UE AI or ML operations based at least in part on the update. Additionally or alternatively, communications manager 140 may perform one or more other operations described herein.

[0042] In some aspects, a network node (e.g., a base station 110, or a device associated with the base station 110 or a portion thereof) may include a communication manager 150. As described in more detail elsewhere herein, the communication manager 150 may receive an update to a previously reported set of UE AI or ML capabilities; and send a configuration associated with the performance of UE AI or ML operations based at least in part on the update. Additionally or alternatively, the communication manager 150 may perform one or more other operations described herein.

[0043] In some aspects, the term "base station" (e.g., base station 110) or "network node" or "network entity" may refer to a converged base station, a decomposed base station (e.g., a combined Figure 8110), integrated access and backhaul (IAB) nodes, relay nodes, and / or one or more components thereof. For example, in some aspects, a "base station", "network node", or "network entity" may refer to a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC, or a combination thereof. In some aspects, the term "base station", "network node", or "network entity" may refer to a device configured to perform one or more functions (such as those described herein in conjunction with base station 110). In some aspects, the term "base station", "network node", or "network entity" may refer to a plurality of devices configured to perform one or more functions. For example, in some distributed systems, each of a plurality of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to repeat the execution of at least a portion of the function, and the term "base station", "network node", or "network entity" may refer to any one or more of those different devices. In some aspects, the term "base station", "network node", or "network entity" may refer to one or more virtual base stations and / or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the terms "base station," "network node," or "network entity" may refer to one of the base station functions but not another base station function. In this way, a single device may include more than one base station.

[0044] As indicated above, Figure 1 are provided in the form of examples. Other examples may be found in connection with Figure 1 The examples described are different.

[0045] Figure 2 2 is a diagram illustrating an example 200 of base station 110 communicating with UE 120 in wireless network 100 according to the present disclosure. Base station 110 may be equipped with a set of antennas 234a to 234t, such as T antennas (T≥1). UE 120 may be equipped with a set of antennas 252a to 252r, such as R antennas (R≥1).

[0046] At the base station 110, the transmit processor 220 may receive data intended for the UE 120 (or a set of UEs 120) from the data source 212. The transmit processor 220 may select one or more modulation and coding schemes (MCS) for the UE 120 based at least in part on one or more channel quality indicators (CQI) received from the UE 120. The base station 110 may process (e.g., encode and modulate) the data for the UE 120 based at least in part on the MCS selected for the UE 120, and provide data symbols for the UE 120. The transmit processor 220 may process system information (e.g., for semi-static resource allocation information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling), and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS) or demodulation reference signals (DMRS)) and synchronization signals (e.g., primary synchronization signals (PSS) or secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., pre-coding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems) (shown as modems 232a to 232t). For example, each output symbol stream may be provided to a modulator component (shown as MOD) of the modem 232. Each modem 232 may process a corresponding output symbol stream (e.g., for OFDM) using a corresponding modulator component to obtain an output sample stream. Each modem 232 may also process (e.g., convert to analog, amplify, filter, and / or up-convert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a to 232t may transmit a set of downlink signals (e.g., T downlink signals) via a corresponding set of antennas 234 (e.g., T antennas) (shown as antennas 234a to 234t).

[0047] At the UE 120, a set of antennas 252 (shown as antennas 252a to 252r) may receive downlink signals from the base station 110 and / or other base stations 110, and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) (shown as modems 254a to 254r). For example, each received signal may be provided to a demodulator component (shown as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modem 254, may perform MIMO detection on the received symbols where applicable, and may provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, may provide decoded data for the UE 120 to the data sink 260, and may provide decoded control information and system information to the controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other things. In some examples, one or more components of the UE 120 may be included in the housing 284.

[0048] The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 may include, for example, one or more devices in a core network. The network controller 130 may communicate with the base station 110 via the communication unit 294.

[0049] One or more antennas (e.g., antennas 234a to 234t and / or antennas 252a to 252r) may include or be included within one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and / or one or more antenna arrays, etc. Antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements (in a single housing or multiple housings), sets of coplanar antenna elements, sets of non-coplanar antenna elements, and / or may be coupled to one or more transmit and / or receive components (such as, Figure 2 One or more antenna elements of one or more components in.

[0050] On the uplink, at the UE 120, the transmit processor 264 may receive and process data from the data source 262 and control information from the controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be pre-decoded by the TX MIMO processor 266, where applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted to the base station 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, and / or a TX MIMO processor 266. The transceiver may be used by a processor (eg, controller / processor 280) and memory 282 to perform the functions described herein (eg, with reference to Figures 4 to 8 ) or any aspect of any of the methods described herein.

[0051] At the base station 110, uplink signals from the UE 120 and / or other UEs may be received by the antenna 234, processed by the modem 232 (e.g., a demodulator component of the modem 232 shown as DEMOD), detected by the MIMO detector 236 where 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 the decoded data to the data sink 239 and the decoded control information to the controller / processor 240. The base station 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The base station 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink and / or uplink communications. In some examples, the modem 232 of the base station 110 may include a modulator and a demodulator. In some examples, the base station 110 includes a transceiver. The transceiver may include any combination of antenna 234, modem 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform operations described herein (e.g., with reference to Figures 4 to 8 ) or any aspect of any of the methods described herein.

[0052] The controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2Any other components of may perform one or more techniques associated with updated AI or ML capability reporting, as described in more detail elsewhere herein. In some aspects, the network node described herein is Figure 2 The base station 110 is shown, is included in the base station 110, or includes one or more components of the base station 110. 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 5 The process of 500 Figure 6 600 and / or operations of other processes as described herein. Memory 242 and memory 282 may store data and program codes for base station 110 and UE 120, respectively. In some examples, memory 242 and / or memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, one or more instructions, when executed by one or more processors of base station 110 and / or UE 120 (e.g., directly, or after compilation, conversion, and / or interpretation), may cause one or more processors, UE 120, and / or base station 110 to perform or direct, for example, Figure 5 The process of 500 Figure 6 The process 600 and / or operations of other processes as described herein. In some examples, executing instructions may include running instructions, converting instructions, compiling instructions, and / or interpreting instructions, etc.

[0053] In some aspects, the UE includes means for sending an update to a previously reported set of UE AI or ML capabilities; and / or means for receiving a configuration associated with the performance of UE AI or ML operations based at least in part on the update. Means for the UE to perform operations described herein may include, for example, one or more of the following: communication manager 140, antenna 252, modem 254, MIMO detector 256, receive processor 258, transmit processor 264, TX MIMO processor 266, controller / processor 280, or memory 282.

[0054] In some aspects, the network node includes means for receiving an update to a previously reported set of UE AI or ML capabilities; and / or means for sending a configuration associated with the performance of UE AI or ML operations based at least in part on the update. Means for the network node to perform operations described herein may include, for example, one or more of the following: communication manager 150, transmit processor 220, TX MIMO processor 230, modem 232, antenna 234, MIMO detector 236, receive processor 238, controller / processor 240, memory 242, or scheduler 246.

[0055] Although Figure 2 The blocks in the 2000 and 2010 are illustrated as distinct components, but the functionality described above for the blocks may be implemented in a single hardware, software, or combined component or in various combinations of components. For example, the functionality described for the transmit processor 264, the receive processor 258, and / or the TX MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0056] As indicated above, Figure 2 are provided in the form of examples. Other examples may be found in connection with Figure 2 The examples described are different.

[0057] Figure 3 is a diagram illustrating an example 300 of a disaggregated base station architecture according to the present disclosure.

[0058] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or constituent parts in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element or a network equipment (such as a base station (BS, e.g., base station 110), or one or more units (or one or more components) performing base station functionality can be implemented in an aggregated or decomposed architecture. For example, a BS (such as a Node B (NB), an eNB, an NR BS, a 5G NB, an access point (AP), a TRP, a cell, etc.) can be implemented as an aggregated base station (also called a stand-alone BS or a monolithic BS) or a decomposed base station.

[0059] A converged base station may be configured to utilize a radio protocol stack physically or logically integrated within a single RAN node. A decomposed base station may be configured to utilize a protocol stack physically or logically distributed between two or more units, such as one or more central or CUs, one or more DUs, or one or more RUs. In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed in one or more other RAN nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU may also be implemented as a virtual unit, i.e., a virtual centralized unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0060] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a disaggregated base station may be used in an IAB network, an O-RAN (such as a network configuration sponsored by the O-RAN Alliance), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Decomposition may include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality of at least one unit, which may enable flexibility in network design. Individual units of a disaggregated base station or disaggregated RAN architecture may be configured for wired or wireless communication with at least one other unit.

[0061] Figure 3 The decomposed base station architecture shown may include one or more CUs 310 that may communicate directly with a core network 320 via a backhaul link, or indirectly with a core network 320 through one or more decomposed base station units, such as a near-RT RIC 325 via an E2 link, or a non-RT RIC 315 associated with a service management and orchestration (SMO) framework 305, or both. The CU 310 may communicate with one or more DUs 330 via respective midhaul links, such as an F1 interface. The DU 330 may communicate with one or more RUs 340 via respective fronthaul links. The RU 340 may communicate with respective UEs 120 via one or more RF access links. In some implementations, a UE 120 may be served simultaneously by multiple RUs 340.

[0062] Each of the units (e.g., CU 310, DU 330, RU 340), as well as the near-RT RIC 325, non-RTRIC 315, and SMO framework 305 may include one or more interfaces, or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively referred to as signals) via a wired transmission medium or a wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to the communication interface of the unit may be configured to communicate with one or more of the other units via a transmission medium. For example, the unit may include a wired interface that is configured to receive or send signals to one or more of the other units via a wired transmission medium. Additionally, the unit may include a wireless interface that may include a receiver, a transmitter, or a transceiver (such as an RF transceiver) that is configured to receive or send signals or both to one or more of the other units via a wireless transmission medium.

[0063] In some aspects, CU 310 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function may be implemented using an interface configured to communicate signals with other control functions hosted by CU 310. CU310 may be configured to handle user plane functionality (e.g., central unit-user plane (CU-UP)), control plane functionality (e.g., central unit-control plane (CU-CP)), or a combination thereof. In some specific implementations, CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, CU 310 may be implemented to communicate with DU 330 for network control and signaling.

[0064] DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RU 340. In some aspects, DU 330 may host one or more of the following depending at least in part on functional splits such as those defined by 3GPP: a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.). In some aspects, DU 330 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by DU 330 or with control functions hosted by CU 310.

[0065] The lower layer functionality may be implemented by one or more RUs 340. In some deployments, a RU 340 controlled by a DU 330 may correspond to a logical node that hosts RF processing functions or low PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional split (such as a lower layer functional split). In such an architecture, the RU 340 may be implemented to handle over-the-air (OTA) communications with one or more UEs 120. In some implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable the DU 330 and CU 310 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0066] The SMO framework 305 may be configured to support RAN deployment and configuration of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 335) to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, and near-RT RIC 325. In some specific implementations, the SMO framework 305 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 311) via the O1 interface. Additionally, in some specific implementations, the SMO framework 305 may communicate directly with one or more RUs 340 via the O1 interface. The SMO framework 305 may also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305 .

[0067] The non-RT RIC 315 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 may be coupled to or in communication with the near-RT RIC 325 (such as via an A1 interface). The near-RT RIC 325 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 310, one or more DUs 330, or both, and the O-eNB with the near-RT RIC 325.

[0068] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from a non-network data source or from a network function at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of O1) or via creation of RAN management policies (such as A1 policies).

[0069] As indicated above, Figure 3 are provided in the form of examples. Other examples may be found in connection with Figure 3 The examples described are different.

[0070] In some networks, a UE and a network node (e.g., a RAN network node, such as a base station, CU, DU, and / or RU) may exchange information to establish a configuration for communicating via a wireless network. For example, a network node may provide configuration information for performing an operation at the UE and / or for reporting the UE's ability to perform an operation. The UE may send an indication of the UE's capabilities (e.g., a capability report) indicating operations supported by the UE, parameters of the operations supported by the UE, and / or resources that may be used to support the operations, etc. For example, the UE may send the UE capability information as an RRC message during an initial registration procedure (e.g., as part of a connection procedure).

[0071] In some networks, the UE may provide capability information associated with the execution of UE AI or ML operations. The capability information may be based at least in part on the amount of available power resources and / or available computing resources at the UE. However, after the UE sends the capability information, the amount of available power resources and / or available computing resources at the UE may change. For example, after a certain time, the UE may not have the ability to support a UE AI or ML model configured at the UE based at least in part on a change in the UE AI or ML capability. In this case, the UE may not be able to meet the timing requirements (e.g., inference deadlines and / or training deadlines) associated with the UE AI or ML model, which may cause the UE AI or ML model to fail. The failure of the UE AI or ML model may be based at least in part on the loss efficiency that may have been obtained by using the UE AI or ML model (or replacing the UE AI or ML model) and / or at least in part on the detection and / or correction of communication errors caused by the failure, etc., and consume computing, power, network and / or communication resources.

[0072] In some aspects described herein, a UE may send an update to a previously reported set of UE AI or ML capabilities. For example, the UE may send the update using a UE assistance information (UAI) based solution and / or dynamic UE AI / ML capability signaling. The UE may receive a configuration associated with the performance of UE AI or ML operations based at least in part on sending the update.

[0073] In some aspects, the UE AI or ML capability set may indicate parameters such as processing power, memory capacity, general hardware acceleration capabilities, supported libraries, supported AI and / or ML model formats, supported AI and / or ML models, maximum number of concurrent AI and / or ML operations supported, tested model combinations and / or quantization of AI and / or ML operations, and the like.

[0074] In some aspects, the UE may use UAI to indicate whether the UE cannot meet the inference deadline and / or training deadline associated with the configured AI or ML operation, and the UE may provide an update of the AI / ML capabilities to the network. In the UAI, the UE may include an indication of whether the UE is interested in performing AI / ML-based procedures (e.g., it has been determined that the UE will benefit from communication by performing AI / ML-based procedures), an update of the UE AI or ML capabilities, a list of affected model IDs, a list of model combinations that may or may not be supported concurrently and their corresponding quantization levels, and / or a list of neural network IDs that may or may not be supported due to overheating, power, and / or computational power issues, and the like.

[0075] In some aspects, updates to AI or ML capabilities may indicate updates to processing capabilities, memory capabilities, general hardware acceleration capabilities, supported libraries, supported models, maximum number of concurrent AI / ML tasks supported, model combinations tested, and / or quantization levels, etc. In some aspects, a list of affected model IDs may indicate a list of model IDs that suffer due to reduced processing, memory, and / or acceleration capabilities, etc. Additionally or alternatively, if the UE supports meeting an inference deadline or training deadline for one or more models, the UE may indicate a model ID, quantization level, and / or model identifier. In some aspects, the UE may indicate that the UE supports model IDs and quantization levels that meet an inference deadline or training deadline for one or more models.

[0076] The network node may configure one or more parameters for the UE to send updates (e.g., via UAI). For example, the network node may configure (e.g., using AIML-UEAssistance) the UE using the one or more parameters. In some aspects, the network node may send the configuration using an RRC message (e.g., in otherConfig under RRCReconfig). The one or more parameters may include a prohibition timer (e.g., T34x) indicating that the UE is not allowed to send additional updates to the capability information until a specified amount of time has passed since the capability information was sent (e.g., the prohibition time starts when the update is sent). Additionally or alternatively, one or more parameters may define event-based UAI reporting. For example, a network node may define one or more trigger conditions for the UE to report UAI for AI or ML capabilities (e.g., updates to the UE AI or ML capabilities).

[0077] The one or more trigger conditions may include the UE's processing power being reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's available memory being reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's hardware acceleration being reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's ability to run a maximum number of concurrent AI or ML operations (e.g., tasks) being reduced by a configured threshold (e.g., if the UE can run only at most {'Y1', ''Y2, ..., 'Y K} number of AI / ML models), and / or for a configured number of operations (e.g., a single iteration with a missed deadline or multiple iterations with a missed deadline), a number (e.g., one or more than one) of configured models or a configured number (e.g., one or more than one) of model combinations that failed to meet a deadline (e.g., an inference / training time requirement).

[0078] Based at least in part on the network node receiving an update on the UE AI or ML capabilities, the network node may send a configuration associated with the performance of the UE AI or ML operation to the UE. For example, if the UE indicates that the UE is no longer interested in AI / ML-based procedures, the network node may release one or more AI and / or ML configurations. In some examples, the network node may send the AI ​​and / or ML configurations to the UE when the UE indicates that the UE is ready to perform and / or is interested in performing AI and / or ML operations.

[0079] In some aspects, if the UE indicates updated capabilities, the network node may deactivate and / or release model configurations for which inference and / or training results cannot be obtained in time within a deadline, and / or instruct the UE to fall back to an old or previously configured procedure. In some aspects, if the UE indicates updated capabilities, the network node may deactivate and / or release model configurations for which inference and / or training results cannot be obtained in time within a deadline, and send an updated configuration based at least in part on an update to the UE AI or ML capabilities.

[0080] In some aspects, the UE may send an update to the UE AI or ML capabilities using an RRC message (e.g., a UE AI or ML capability transfer message). In some aspects, the UE may send the RRC message in a dynamic manner. For example, the UE may send the RRC message as a UE-triggered AI / ML capability report. The UE may dynamically indicate to the network node whether the UE is interested in one or more UE AI or ML operations. And / or the UE may autonomously update the UE AI or ML capabilities (e.g., if required by the network node or communication protocol, etc.).

[0081] In another example, the UE may autonomously update the UE AI or ML capabilities with application restrictions. For example, the UE may dynamically indicate to the network node whether the UE is interested in procedures based on UE AI or ML operations, the UE may autonomously update the UE AI or ML capabilities (e.g., if needed), and the UE may start a prohibit timer that restricts the sending of updates of the UE AI or ML capabilities until the prohibit timer expires, etc. The prohibit timer may be configured at least in part based on a communication protocol and / or at least in part based on a configuration indicated from the network node (e.g., using RRCReconfiguration).

[0082] In another example, the UE may send updates to the UE AI or ML capabilities based at least in part on an event trigger configured by the network. For example, the network node may configure the UE to indicate when the UE sends updates to the UE AI or ML capabilities (e.g., the update may indicate whether the UE is interested in UE AI operations or UE ML operations). The network node may configure the UE to report updates to the UE AI or ML capabilities based at least in part on: the UE's processing power is reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's available memory is reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's hardware acceleration is reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's ability to run a maximum number of concurrent AI or ML operations (e.g., tasks) is reduced by a configured threshold (e.g., if the UE can run only at most {'Y1', ''Y2, ..., 'Y K} number of AI / ML models), and / or for a configured number of operations (e.g., a single iteration with a missed deadline or multiple iterations with a missed deadline), a number (e.g., one or more than one) of configured models or a configured number (e.g., one or more than one) of model combinations that failed to meet a deadline (e.g., an inference / training time requirement).

[0083] Based at least in part on the UE sending an update to the UE AI or ML capabilities, the network node may update a configuration and / or an AI or ML model for use by the UE in communications with the network node, based at least in part on the updated UE AI or ML capabilities. In this way, the UE may avoid configuration of an AI or ML operation that may fail based at least in part on the reduced UE AI or ML capabilities. Avoiding configuration of an AI or ML operation that may fail may be based at least in part on lost efficiencies that may have been gained by using the UE AI or ML model (or replacing the UE AI or ML model) and / or based at least in part on detecting and / or correcting communication errors caused by the failure, etc., and saving computational, power, network, and / or communication resources that may have otherwise been consumed.

[0084] Figure 4 4 is a diagram of an example 400 associated with an updated AI or ML capability report according to the present disclosure. Figure 4 As shown, a network node (e.g., base station 110, CU, DU, and / or RU) may communicate with a UE (e.g., UE 120). In some aspects, the network node and the UE may be part of a wireless network (e.g., wireless network 100). The UE and the network node may be in Figure 4 The illustrated operations are performed after a radio connection has been established. For example, the UE and / or the network node may have exchanged one or more messages of a random access channel (RACH) procedure.

[0085] As indicated by reference numeral 405, the network node may send configuration information, and the UE may receive the configuration information. In some aspects, the UE may receive the configuration information via one or more of RRC signaling, one or more MAC control elements (CEs), and / or downlink control information (DCI), etc. In some aspects, the configuration information may include an indication of one or more configuration parameters for the UE to select (e.g., known to the UE and / or previously indicated by the network node or other network device) and / or explicit configuration information for the UE to configure the UE, etc.

[0086] In some aspects, the configuration information may indicate that the UE is to send updates to the UE AI or ML capabilities. In some aspects, the configuration information may indicate a type of communication by which the UE is to send updates to the UE AI or ML capabilities. For example, the configuration information may indicate that the UE is to send updates via UAI and / or dynamic UE AI / ML capabilities signaling.

[0087] In some aspects, the configuration information may indicate one or more parameters for sending updates. For example, the configuration information may indicate one or more trigger conditions for sending updates. Additionally or alternatively, the configuration information may indicate parameters (e.g., duration) for an inhibit timer that indicates that the UE is not allowed to send additional updates to the capability information until a specified amount of time has passed since the capability information was sent. For example, an inhibit timer (e.g., an update inhibit timer) may indicate a minimum amount of time between sending updates to the UE AI or ML capabilities.

[0088] The UE may configure itself based at least in part on the configuration information.In some aspects, the UE may be configured to perform one or more operations described herein based at least in part on the configuration information.

[0089] As indicated by reference numeral 410, the UE may send a capability report indicating the UE AI and / or ML capabilities, and the network node may receive the capability report. In some aspects, the capability report may indicate that the UE supports sending updates to the UE AI or ML capabilities.

[0090] In some aspects, UE AI or ML capabilities indicate parameters such as processing power, memory capabilities, general hardware acceleration capabilities, supported libraries, supported AI and / or ML model formats, supported AI and / or ML models, maximum number of concurrent AI and / or ML operations supported, tested model combinations and / or quantization of AI and / or ML operations, and the like.

[0091] As indicated by reference numeral 415, the UE and the network node may perform network connection reconfiguration. For example, the UE or the network node may initiate a process for reestablishing one or more configurations for subsequent communications between the UE and the network node.

[0092] As shown by reference numeral 420, the UE may receive a request for an update to a previously reported set of UE AI or ML capabilities. In some aspects, the UE may receive the request based at least in part on a network connection reconfiguration. In some aspects, the UE may receive the request independently of (e.g., in the absence of) a connection reconfiguration. For example, the connection reconfiguration may be omitted.

[0093] As shown by reference numeral 425, the UE may detect a change in UE AI or ML capabilities. For example, the UE may detect a change in the power level of the UE, an additional process being executed on the UE that consumes additional computational and / or memory resources, and / or a change in settings at the UE, etc.

[0094] In some aspects, the UE may detect a change in UE AI or ML capabilities independently of (e.g., in the absence of) a connection reconfiguration and / or a request for an update described in conjunction with reference numerals 415 and / or 420. For example, a connection reconfiguration and / or a request for an update may be omitted.

[0095] As indicated by reference numeral 430, the UE may identify satisfaction of a triggering condition for sending an update to the UE AI or ML capabilities. For example, the triggering condition may include detecting that the UE is unable to meet a timing requirement (e.g., an inference or training deadline, etc.) associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on a previously reported set of UE AI or ML capabilities.

[0096] In some aspects, the UE may identify the satisfaction of a trigger condition as a trigger for sending an update to a previously reported set of UE AI or ML capabilities (e.g., as described in connection with reference numeral 435). In some aspects, the UE may send the update based at least in part on detecting that a change in the UE AI or ML capabilities satisfies a threshold change amount.

[0097] The one or more trigger conditions may include the UE's processing power being reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's available memory being reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's hardware acceleration being reduced to a configured percentage value (e.g., 75%, 50%, or 25%, etc.), the UE's ability to run a maximum number of concurrent AI or ML operations (e.g., tasks) being reduced by a configured threshold (e.g., if the UE can run only at most {'Y1', ''Y2, ..., 'Y K} number of AI / ML models), and / or for a configured number of operations (e.g., a single iteration with a missed deadline or multiple iterations with a missed deadline), a number (e.g., one or more) of configured models or a configured number (e.g., one or more) of model combinations that failed to meet a deadline (e.g., an inference / training time requirement). In some aspects, the UE may send an update based at least in part on satisfying multiple trigger conditions (e.g., 1 or more) and / or satisfying multiple occasions when the trigger conditions are satisfied (e.g., 1 or more occasions when the UE AI or ML model is used), etc.

[0098] As indicated by reference numeral 435, the UE may send an update to a previously reported set of UE AI and / or ML capabilities, and the network node may receive an update to a previously reported set of UE AI and / or ML capabilities. In some aspects, the UE may send the update via UAI and / or via dynamic capability update signaling (e.g., an RRC message). In some aspects, the UE may send the update based at least in part on detecting a change in UE AI or ML capabilities (e.g., a threshold change amount), identifying satisfaction of a trigger condition, and / or expiration of an inhibit timer (e.g., an inhibit time does not limit the sending of updates), etc.

[0099] In some aspects, dynamic capability update signaling includes RRC signaling, an indication of whether the UE supports one or more UE AI or ML operations, autonomous signaling, and / or trigger condition based signaling.

[0100] In some aspects, the update may indicate whether the UE supports performance of one or more UE AI or ML operations, an update to a previously reported set of UE AI or ML capabilities (e.g., updated indications of available resources and / or performance parameters, individually or when used in a combination of models, etc.), one or more UE AI or ML models affected by the update to the previously reported set of UE AI or ML capabilities (e.g., one or more models having degraded performance based at least in part on a change in UE AI or ML capabilities), one or more additional UE AI or ML models not affected by the update to the previously reported set of UE AI or ML capabilities (e.g., one or more models that may continue to operate with the change in UE AI or ML capabilities), and / or a performance parameter update for one or more of the UE AI or ML models, etc. In some aspects, the performance parameter update may support meeting timing requirements (e.g., inference deadlines and / or training deadlines) associated with one or more UE AI or ML models. For example, the performance parameter update may indicate a model quantization level for one or more UE AI or ML models, etc.

[0101] In some aspects, the update may indicate the following: one or more model combinations supported by the UE based at least in part on an update to a previously reported set of UE AI or ML capabilities; performance parameters associated with one or more model combinations supported by the UE based at least in part on an update to a previously reported set of UE AI or ML capabilities; and / or one or more model combinations not supported by the UE based at least in part on an update to a previously reported set of UE AI or ML capabilities, etc. In some aspects, the update may indicate performance parameters associated with one or more model combinations not supported by the UE based at least in part on an update to a previously reported set of UE AI or ML capabilities.

[0102] As indicated by reference numeral 440, the UE may initiate an inhibit timer. The inhibit timer may indicate a minimum amount of time between sending updates to the UE AI or ML capabilities. In some aspects, the inhibit timer may be configured in a communication protocol and / or via configuration information from a network node or another network node.

[0103] As indicated by reference numeral 445, the network node may identify UE AI or ML operations and / or parameters for the UE to use for communicating with the network node. In some aspects, the network node may identify configuring the UE to cease one or more of the UE AI or ML operations (e.g., all of the UE AI or ML operations), configuring updated configurations of the UE AI or ML operations, and / or configuring the UE to use additional UE AI or ML operations (e.g., replacing the UE AI or ML operations), etc. based at least in part on an update to a previously reported set of UE AI and / or ML capabilities.

[0104] As shown at reference numeral 450, the UE may receive a configuration associated with the performance of the UE AI or ML operation, and the network node may send a configuration associated with the performance of the UE AI or ML operation. For example, the UE may receive the configuration based at least in part on the update sent in conjunction with reference numeral 435 and / or the identification of the UE AI or ML capability described in conjunction with reference numeral 440, etc.

[0105] As indicated by reference numeral 455, the UE may update the UE AI or ML operations based at least in part on the configuration. For example, the UE may stop (e.g., disable) all of the UE AI or ML operations in the UE AI or ML operations, the UE may stop one or more of the UE AI or ML operations (e.g., the UE AI or ML operations indicated as being affected by the change in the UE AI or ML capabilities), and / or the UE may use one or more additional UE AI or ML operations, etc. In some aspects, the UE may replace the UE AI or ML model affected by the change in the UE AI or ML capabilities with the additional UE AI or ML operations, or may replace the configuration of the UE AI or ML model affected by the change in the UE AI or ML capabilities, etc. For example, the UE may replace a quantization of the configuration of the UE AI or ML model affected by the change in the UE AI or ML capabilities such that the UE AI or ML capabilities are configured to allow the UE to meet the timing requirements of the UE AI or ML model.

[0106] As indicated by reference numeral 460, the UE and the network node may communicate based at least in part on updates to UE AI or ML operations.

[0107] Based at least in part on the UE sending an update to the UE AI or ML capabilities, the network node may update a configuration and / or an AI or ML model for use by the UE in communications with the network node, based at least in part on the updated UE AI or ML capabilities. In this way, the UE may avoid configuration of an AI or ML operation that may fail based at least in part on the reduced UE AI or ML capabilities. Avoiding configuration of an AI or ML operation that may fail may be based at least in part on lost efficiencies that may have been gained by using the UE AI or ML model (or replacing the UE AI or ML model) and / or based at least in part on detecting and / or correcting communication errors caused by the failure, etc., and saving computational, power, network, and / or communication resources that may have otherwise been consumed.

[0108] As indicated above, Figure 4 are provided in the form of examples. Other examples may be found in connection with Figure 4 The examples described are different.

[0109] Figure 5 5 is a diagram illustrating an example process 500, performed, for example, by a UE, according to the present disclosure. Example process 500 is an example in which a UE (eg, UE 120) performs operations associated with updated AI or ML capability reporting.

[0110] like Figure 5 As shown, in some aspects, process 500 may include sending an update to a previously reported set of UE AI or ML capabilities (block 510). For example, a UE (e.g., using Figure 7 The communication manager 140 and / or the sending component 704 depicted in FIG. 1 may send an update to a previously reported set of UE AI or ML capabilities, as described above.

[0111] like Figure 5 As further shown in FIG. 5 , in some aspects, process 500 may include receiving a configuration associated with the performance of the UE AI or ML operation based at least in part on the update (block 520). Figure 7 The communications manager 140 and / or receiving component 702 depicted in FIG. 1 may receive a configuration associated with execution of UE AI or ML operations based at least in part on the update, as described above.

[0112] 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.

[0113] In a first aspect, sending an update to a previously reported set of UE AI or ML capabilities includes one or more of sending an indication via a UAI or sending an update via dynamic capability update signaling.

[0114] In a second aspect, alone or in combination with the first aspect, the dynamic capability update signaling includes one or more of RRC signaling, an indication of whether the UE supports one or more UE AI or ML operations, autonomous signaling, or trigger condition-based signaling.

[0115] In a third aspect, alone or in combination with one or more of the first and second aspects, an update to a previously reported UE AI or ML capability set indicates one or more of: whether the UE supports execution of one or more UE AI or ML operations; an update to a previously reported UE AI or ML capability set; one or more UE AI or ML models affected by the update to the previously reported UE AI or ML capability set; one or more additional UE AI or ML models not affected by the update to the previously reported UE AI or ML capability set; or a performance parameter update for one or more of the UE AI or ML models, which supports meeting timing requirements associated with the one or more UE AI or ML models.

[0116] In a fourth aspect, alone or in combination with one or more of the first to third aspects, an update to a previously reported UE AI or ML capability set indicates one or more of the following: one or more model combinations supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set; performance parameters associated with one or more model combinations supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set; one or more model combinations not supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set; or performance parameters associated with one or more model combinations not supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set.

[0117] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, sending an update to a previously reported set of UE AI or ML capabilities is based at least in part on detecting that the UE is unable to meet timing requirements associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on the previously reported set of UE AI or ML capabilities.

[0118] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, process 500 includes receiving an indication of a configuration for sending an update to a previously reported set of UE AI or ML capabilities.

[0119] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the configuration for sending updates to a previously reported set of UE AI or ML capabilities indicates one or more of: an update prohibit timer indicating a minimum amount of time between sending updates to the UE AI or ML capabilities; or one or more trigger conditions for sending updates to the UE AI or ML capabilities.

[0120] In an eighth aspect, either alone or in combination with one or more of the first to seventh aspects, process 500 includes detecting a change in UE AI or ML capabilities, wherein sending an update to a previously reported set of UE AI or ML capabilities is based at least in part on detecting the change.

[0121] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, sending an update to a previously reported set of UE AI or ML capabilities is based at least in part on detecting that a change in UE AI or ML capabilities satisfies a threshold change amount.

[0122] In a tenth aspect, alone or in combination with one or more of aspects 1 to 9, a configuration associated with the execution of a UE AI or ML operation indicates one or more of: stopping the UE AI or ML operation, stopping one or more of the UE AI or ML operations, updating the configuration of the UE AI or ML operation, or using additional UE AI or ML operations.

[0123] although Figure 5 An example block diagram of process 500 is shown, but in some aspects, process 500 may include Figure 5 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 of the blocks in the process 500 may be performed in parallel.

[0124] Figure 6 600 is a diagram illustrating an example process 600 performed, for example, by a network node according to the present disclosure. Example process 600 is an example in which a network node (e.g., base station 110, CU, DU, and / or RU) performs operations associated with updated AI or ML capability reporting.

[0125] like Figure 6 As shown, in some aspects, process 600 may include receiving an update to a previously reported set of UE AI or ML capabilities (block 610). For example, a network node (e.g., using Figure 8 The communications manager 150 and / or receiving component 802 depicted in FIG. 8 may receive updates to a previously reported set of UE AI or ML capabilities, as described above.

[0126] like Figure 6 As further shown in FIG. 6 , in some aspects, process 600 may include sending a configuration associated with the execution of the UE AI or ML operation based at least in part on the update (block 620). Figure 8 The communications manager 150 and / or sending component 804 depicted in FIG. 8 may send a configuration associated with execution of UE AI or ML operations based at least in part on the update, as described above.

[0127] Process 600 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.

[0128] In a first aspect, receiving an update to a previously reported set of UE AI or ML capabilities includes one or more of receiving an indication via a UAI or receiving an update via dynamic capability update signaling.

[0129] In a second aspect, alone or in combination with the first aspect, the dynamic capability update signaling includes one or more of RRC signaling, an indication of whether the UE supports one or more UE AI or ML operations, autonomous signaling, or trigger condition-based signaling.

[0130] In a third aspect, alone or in combination with one or more of the first and second aspects, an update to a previously reported UE AI or ML capability set indicates one or more of: whether the UE supports execution of one or more UE AI or ML operations; an update to a previously reported UE AI or ML capability set; one or more UE AI or ML models affected by the update to the previously reported UE AI or ML capability set; one or more additional UE AI or ML models not affected by the update to the previously reported UE AI or ML capability set; or a performance parameter update for one or more of the UE AI or ML models, which supports meeting timing requirements associated with the one or more UE AI or ML models.

[0131] In a fourth aspect, alone or in combination with one or more of the first to third aspects, an update to a previously reported UE AI or ML capability set indicates one or more of the following: one or more model combinations supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set; performance parameters associated with one or more model combinations supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set; one or more model combinations not supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set; or performance parameters associated with one or more model combinations not supported by the UE based at least in part on the update to the previously reported UE AI or ML capability set.

[0132] In a fifth aspect, alone or in combination with one or more of aspects 1 to 4, receiving an update to a previously reported set of UE AI or ML capabilities is based at least in part on detecting that the UE is unable to meet timing requirements associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on the previously reported set of UE AI or ML capabilities.

[0133] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, process 600 includes sending an indication of a configuration for sending an update to a previously reported set of UE AI or ML capabilities.

[0134] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the configuration for sending updates to a previously reported set of UE AI or ML capabilities indicates one or more of: an update prohibit timer indicating a minimum amount of time between sending updates to the UE AI or ML capabilities; or one or more trigger conditions for sending updates to the UE AI or ML capabilities.

[0135] In an eighth aspect, alone or in combination with one or more of aspects 1 to 7, a configuration associated with the execution of UE AI or ML operations indicates one or more of: stopping all UE AI or ML operations in the UE AI or ML operations, stopping one or more of the UE AI or ML operations, updating the configuration of the UE AI or ML operations, or using additional UE AI or ML operations.

[0136] although Figure 6 An example block diagram of process 600 is shown, but in some aspects, process 600 may include Figure 6Additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 600. Additionally or alternatively, two or more of the blocks in the process 600 may be performed in parallel.

[0137] Figure 7 700 is a diagram of an example apparatus 700 for wireless communication. Apparatus 700 may be a UE, or a UE may include apparatus 700. In some aspects, apparatus 700 includes a receiving component 702 and a transmitting component 704, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 700 may communicate with another apparatus 706 (such as a UE, a network node, or another wireless communication device) using receiving component 702 and transmitting component 704. As further shown, apparatus 700 may include a communication manager 708 (e.g., communication manager 140).

[0138] In some aspects, the apparatus 700 may be configured to perform Figure 4 Additionally or alternatively, the apparatus 700 may be configured to perform one or more processes described herein, such as Figure 5 In some aspects, the apparatus 700 and / or Figure 7 One or more of the components shown in the figure may include a combination of Figure 2 One or more components of the UE. Additionally or alternatively, Figure 7 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the component set may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as an instruction or code that is stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0139] The receiving component 702 may receive communications from the device 706, such as reference signals, control information, data communications, or combinations thereof. The receiving component 702 may provide the received communications to one or more other components of the device 700. In some aspects, the receiving component 702 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, etc.) on the received communications and may provide the processed signals to one or more other components of the device 700. In some aspects, the receiving component 702 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receiving processors, controllers / processors, memories or combinations thereof of the UE.

[0140] The transmitting component 704 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 706. In some aspects, one or more other components of the device 700 may generate communications and may provide the generated communications to the transmitting component 704 for transmission to the device 706. In some aspects, the transmitting component 704 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 706. In some aspects, the transmitting component 704 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the UE. In some aspects, the transmit component 704 can be co-located with the receive component 702 in a transceiver.

[0141] Transmitting component 704 can transmit an update to a previously reported set of UE AI or ML capabilities. Receiving component 702 can receive a configuration associated with performance of UE AI or ML operations based at least in part on the update.

[0142] Receiving component 702 can receive an indication of a configuration for sending an update to a previously reported set of UE AI or ML capabilities.

[0143] The communications manager 708 may detect a change in UE AI or ML capabilities, wherein sending an update to a previously reported set of UE AI or ML capabilities is based at least in part on detecting the change.

[0144] Figure 7 The number and arrangement of components shown are provided by way of example. Figure 7 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Figure 7 Two or more components shown may be implemented in a single component, or Figure 7 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Figure 7 The component set (one or more components) shown may be described as being executable by Figure 7 Another component shown may be a collection of components that perform one or more functions.

[0145] Figure 8800 is a diagram of an example apparatus 800 for wireless communication. Apparatus 800 may be a network node, or a network node may include apparatus 800. In some aspects, apparatus 800 includes a receiving component 802 and a transmitting component 804, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 800 may communicate with another apparatus 806 (such as a UE, a network node, or another wireless communication device) using receiving component 802 and transmitting component 804. As further shown, apparatus 800 may include a communication manager 808 (e.g., communication manager 150).

[0146] In some aspects, the apparatus 800 may be configured to perform Figure 4 Additionally or alternatively, the apparatus 800 may be configured to perform one or more processes described herein, such as Figure 6 The process 600. In some aspects, Figure 8 The apparatus 800 and / or one or more components shown in FIG. 8 may include a combination of Figure 2 One or more components of the network node. Additionally or alternatively, Figure 8 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the component set may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as an instruction or code that is stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0147] The receiving component 802 may receive communications from the device 806, such as reference signals, control information, data communications, or combinations thereof. The receiving component 802 may provide the received communications to one or more other components of the device 800. In some aspects, the receiving component 802 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, etc.) on the received communications and may provide the processed signals to one or more other components of the device 800. In some aspects, the receiving component 802 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receiving processors, controllers / processors, memories or combinations thereof of the network node.

[0148] The transmitting component 804 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 806. In some aspects, one or more other components of the device 800 may generate communications and may provide the generated communications to the transmitting component 804 for transmission to the device 806. In some aspects, the transmitting component 804 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 806. In some aspects, the transmitting component 804 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the network node. In some aspects, the transmit component 804 can be co-located with the receive component 802 in a transceiver.

[0149] Receiving component 802 can receive an update to a previously reported set of UE AI or ML capabilities. Sending component 804 can send a configuration associated with performance of UE AI or ML operations based at least in part on the update.

[0150] Transmitting component 804 may transmit an indication of a configuration for transmitting an update to a previously reported set of UE AI or ML capabilities.

[0151] Figure 8 The number and arrangement of components shown are provided by way of example. Figure 8 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Figure 8 Two or more components shown may be implemented in a single component, or Figure 8 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Figure 8 The component set (one or more components) shown may be described as being executable by Figure 8 Another component shown may be a collection of components that perform one or more functions.

[0152] The following provides an overview of some aspects of the disclosure:

[0153] Aspect 1: A wireless communication method performed by a user equipment (UE), the method comprising: sending an update to a previously reported set of UE artificial intelligence (AI) or machine learning (ML) capabilities; and receiving a configuration associated with performance of UE AI or ML operations based at least in part on the update.

[0154] Aspect 2: The method according to aspect 1, wherein sending the update to the previously reported UE AI or ML capability set comprises one or more of the following: sending the update via UE assistance information (UAI), or sending the update via dynamic capability update signaling.

[0155] Aspect 3: The method according to aspect 2, wherein the dynamic capability update signaling includes one or more of the following: radio resource control (RRC) signaling, an indication of whether the UE supports one or more UE AI or ML operations, autonomous signaling, or signaling based on trigger conditions.

[0156] Aspect 4: A method according to any one of Aspects 1 to 3, wherein the update to the previously reported UE AI or ML capability set indicates one or more of the following: whether the UE supports the execution of one or more UE AI or ML operations; the update to the previously reported UE AI or ML capability set; one or more UE AI or ML models affected by the update to the previously reported UE AI or ML capability set; one or more additional UE AI or ML models not affected by the update to the previously reported UE AI or ML capability set; or a performance parameter update for one or more of the UE AI or ML models, wherein the performance parameter update supports meeting timing requirements associated with the one or more UE AI or ML models.

[0157] Aspect 5: A method according to any one of Aspects 1 to 4, wherein the update to the previously reported UE AI or ML capability set indicates one or more of the following: one or more model combinations supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set; performance parameters associated with one or more model combinations supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set; one or more model combinations not supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set; or performance parameters associated with one or more model combinations not supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set.

[0158] Aspect 6: A method according to any one of Aspects 1 to 5, wherein sending the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting that the UE is unable to meet timing requirements associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on the previously reported set of UE AI or ML capabilities.

[0159] Aspect 7: According to any one of aspects 1 to 6, the method further includes: receiving an indication for sending the updated configuration of the previously reported UE AI or ML capability set.

[0160] Aspect 8: A method according to aspect 7, wherein the configuration for sending the update to the previously reported set of UE AI or ML capabilities indicates one or more of the following: an update prohibit timer, the update prohibit timer indicating a minimum amount of time between sending updates to UE AI or ML capabilities; or one or more trigger conditions, the one or more trigger conditions being used to send updates to UE AI or ML capabilities.

[0161] Aspect 9: According to any one of aspects 1 to 8, the method also includes: detecting a change in UE AI or ML capabilities, wherein the sending of the update to the previously reported set of UE AI or ML capabilities is at least partially based on detecting the change.

[0162] Aspect 10: The method of aspect 9, wherein sending the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting that the change in UE AI or ML capabilities satisfies a threshold change amount.

[0163] Aspect 11: A method according to any one of Aspects 1 to 10, wherein the configuration associated with the execution of UE AI or ML operations indicates one or more of the following: stopping all UE AI or ML operations in the UE AI or ML operations, stopping one or more of the UE AI or ML operations, updating the configuration of the UE AI or ML operations, or using additional UE AI or ML operations.

[0164] Aspect 12: A method of wireless communication performed by a network node, the method comprising: receiving an update to a previously reported set of user equipment (UE) artificial intelligence (AI) or machine learning (ML) capabilities; and sending a configuration associated with execution of UE AI or ML operations based at least in part on the update.

[0165] Aspect 13: The method according to aspect 12, wherein receiving the update to the previously reported set of UE AI or ML capabilities comprises one or more of: receiving the update via UE assistance information (UAI), or receiving the update via dynamic capability update signaling.

[0166] Aspect 14: The method according to Aspect 13, wherein the dynamic capability update signaling includes one or more of the following: radio resource control (RRC) signaling, an indication of whether the UE supports one or more UE AI or ML operations, autonomous signaling, or signaling based on trigger conditions.

[0167] Aspect 15: A method according to any one of Aspects 12 to 14, wherein the update to the previously reported UE AI or ML capability set indicates one or more of the following: whether the UE supports the execution of one or more UE AI or ML operations; the update to the previously reported UE AI or ML capability set; one or more UE AI or ML models affected by the update to the previously reported UE AI or ML capability set; one or more additional UE AI or ML models not affected by the update to the previously reported UE AI or ML capability set; or a performance parameter update for one or more of the UE AI or ML models, wherein the performance parameter update supports meeting timing requirements associated with the one or more UE AI or ML models.

[0168] Aspect 16: A method according to any one of Aspects 12 to 15, wherein the update to the previously reported UE AI or ML capability set indicates one or more of the following: one or more model combinations supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set; performance parameters associated with one or more model combinations supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set; one or more model combinations not supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set; or performance parameters associated with one or more model combinations not supported by the UE at least in part based on the update to the previously reported UE AI or ML capability set.

[0169] Aspect 17: A method according to any one of Aspects 12 to 16, wherein receiving the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting that the UE is unable to meet timing requirements associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on the previously reported set of UE AI or ML capabilities.

[0170] Aspect 18: The method according to any one of aspects 12 to 17, the method further comprising: sending an indication for sending the updated configuration of the previously reported UE AI or ML capability set.

[0171] Aspect 19: A method according to aspect 18, wherein the configuration for sending the update to the previously reported set of UE AI or ML capabilities indicates one or more of the following: an update prohibit timer, the update prohibit timer indicating a minimum amount of time between sending updates to UE AI or ML capabilities; or one or more trigger conditions, the one or more trigger conditions being used to send updates to UE AI or ML capabilities.

[0172] Aspect 20: A method according to any one of Aspects 12 to 19, wherein the configuration associated with the execution of UE AI or ML operations indicates one or more of the following: stopping all UE AI or ML operations in the UE AI or ML operations, stopping one or more of the UE AI or ML operations, updating the configuration of the UE AI or ML operations, or using additional UE AI or ML operations.

[0173] Aspect 21: An apparatus for performing wireless communications at a device, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 1 to 20.

[0174] Aspect 22: A device for wireless communication, the device comprising: a memory; and one or more processors, the one or more processors coupled to the memory, the one or more processors configured to execute the method according to one or more of aspects 1 to 20.

[0175] Aspect 23: An apparatus for wireless communication, the apparatus comprising: at least one means for performing the method according to one or more of aspects 1 to 20.

[0176] Aspect 24: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 1 to 20.

[0177] Aspect 25: 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 device, cause the device to perform one or more of the methods described in aspects 1 to 20.

[0178] 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 may be made in light of the foregoing disclosure or may be acquired from practice of the various aspects.

[0179] As used herein, the term "component" is intended to be broadly interpreted as a combination of hardware and / or hardware and software. Whether referred to as software, firmware, middleware, microcode, hardware description language or other names, "software" should be broadly interpreted as representing instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures and / or functions, etc. As used herein, a "processor" is implemented in a combination of hardware and / or hardware and software. It will be apparent that the systems and / or methods described herein can be implemented by a combination of hardware and / or hardware and software in different forms. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit various aspects. Therefore, there is no reference to a specific software code herein to describe the operation and behavior of the system and / or method, because those skilled in the art will understand that software and hardware can be designed to implement the system and / or method based at least in part on the description herein.

[0180] 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.

[0181] Although the specific combination of features is set forth in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features can be combined in a manner that is not specifically described in the claims and / or is not disclosed in the specification. The disclosure of various aspects includes each dependent claim combined with each other claim in the claim set. As used herein, the phrase "at least one of" the item list refers to any combination of these items (it includes a single member). 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, and any combination with multiple identical elements (for example, 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 sorting of a, b and c).

[0182] Any element, action or instruction used herein should not be interpreted as key or necessary, unless explicitly stated. In addition, as used herein, the article "one" is intended to include one or more items, and can be used interchangeably with "one or more". In addition, as used herein, the article "said" is intended to include one or more items connected to the article "said", 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 items, and can be used interchangeably with "one or more". In the case of only intending to refer to an item, the phrase "only one" or similar terms will be used. Moreover, as used herein, the terms "have", "have" etc. are intended to be open terms, which do not limit the elements they modify (for example, "an element with" A can also have B). In addition, the phrase "based on" is intended to represent "based at least in part on", unless explicitly stated otherwise. Furthermore, as used herein, the term "or" when used in a series is intended to be open-ended and used interchangeably with "and / or" unless explicitly stated otherwise (e.g., if used in conjunction with "either" or "only one of").

Claims

1. A user equipment (UE) for wireless communication, the user equipment (UE) comprising: Memory; and one or more processors coupled to the memory and configured to: Sending updates to a previously reported set of UE artificial intelligence (AI) or machine learning (ML) capabilities; and A configuration associated with performance of the UE AI or ML operation is received based at least in part on the update.

2. The UE of claim 1 , wherein the one or more processors for sending the update to the previously reported set of UE AI or ML capabilities are configured to: The update is sent via UE Assistance Information (UAI), or The update is sent via dynamic capability update signaling.

3. The UE according to claim 2, wherein the dynamic capability update signaling comprises one or more of the following: Radio Resource Control (RRC) signaling, an indication of whether the UE supports one or more UE AI or ML operations, Autonomous signaling, or Signaling based on trigger conditions.

4. The UE of claim 1 , wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: whether the UE supports execution of one or more UE AI or ML operations, said update to said previously reported set of UE AI or ML capabilities, one or more UE AI or ML models affected by said update to said previously reported set of UE AI or ML capabilities, one or more additional UE AI or ML models that are not affected by said update to said previously reported set of UE AI or ML capabilities, or and a performance parameter update for one or more of the UE AI or ML models, the performance parameter update supporting meeting timing requirements associated with the one or more UE AI or ML models.

5. The UE of claim 1 , wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: one or more model combinations supported by the UE based at least in part on the update to a previously reported set of UE AI or ML capabilities, performance parameters associated with one or more model combinations supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, One or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, or and performance parameters associated with one or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities.

6. The UE of claim 1 , wherein sending the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting that the UE is unable to meet timing requirements associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on the previously reported set of UE AI or ML capabilities.

7. The UE of claim 1, wherein the one or more processors are further configured to: Receiving an indication for sending the updated configuration of the previously reported set of UE AI or ML capabilities.

8. The UE of claim 7, wherein the configuration for sending the update to the previously reported set of UE AI or ML capabilities indicates one or more of: an update prohibit timer indicating the minimum amount of time between sending updates to the UE AI or ML capabilities, or One or more trigger conditions for sending updates to UE AI or ML capabilities.

9. The UE of claim 1, wherein the one or more processors are further configured to: Detect changes in UE AI or ML capabilities, Wherein sending of the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting the change.

10. The UE of claim 9, wherein sending the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting that the change in UE AI or ML capabilities satisfies a threshold change amount.

11. The UE of claim 1 , wherein the configuration associated with the execution of UE AI or ML operations indicates one or more of the following: Stop all UE AI or ML operations in said UE AI or ML operation, stopping one or more of the UE AI or ML operations, updating the configuration of the UE AI or ML operation, or For additional UE AI or ML operations.

12. A network node for wireless communication, the network node comprising: Memory; and one or more processors coupled to the memory and configured to: Receiving an update to a previously reported set of user equipment (UE) artificial intelligence (AI) or machine learning (ML) capabilities; and A configuration associated with performance of the UE AI or ML operation is sent based at least in part on the update.

13. The network node of claim 12, wherein the one or more processors for receiving the update to the previously reported set of UE AI or ML capabilities are configured to: receiving the update via UE Assistance Information (UAI), or The update is received via dynamic capability update signaling.

14. The network node of claim 13, wherein the dynamic capability update signaling comprises one or more of the following: Radio Resource Control (RRC) signaling, an indication of whether the UE supports one or more UE AI or ML operations, Autonomous signaling, or Signaling based on trigger conditions.

15. The network node of claim 12, wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: whether the UE supports execution of one or more UE AI or ML operations, said update to said previously reported set of UE AI or ML capabilities, one or more UE AI or ML models affected by said update to said previously reported set of UE AI or ML capabilities, one or more additional UE AI or ML models that are not affected by said update to said previously reported set of UE AI or ML capabilities, or and a performance parameter update for one or more of the UE AI or ML models, the performance parameter update supporting meeting timing requirements associated with the one or more UE AI or ML models.

16. The network node of claim 12, wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: one or more model combinations supported by the UE based at least in part on the update to a previously reported set of UE AI or ML capabilities, performance parameters associated with one or more model combinations supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, One or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, or and performance parameters associated with one or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities.

17. The network node of claim 12, wherein receiving the update to the previously reported set of UE AI or ML capabilities is based at least in part on detecting that the UE is unable to meet timing requirements associated with performing one or more UE AI or ML operations that the UE is configured to perform based at least in part on the previously reported set of UE AI or ML capabilities.

18. The network node of claim 12, wherein the one or more processors are further configured to: Sending an indication for sending the updated configuration of the previously reported set of UE AI or ML capabilities.

19. The network node of claim 18, wherein the configuration for sending the update to the previously reported set of UE AI or ML capabilities indicates one or more of: an update prohibit timer indicating the minimum amount of time between sending updates to the UE AI or ML capabilities, or One or more trigger conditions for sending updates to UE AI or ML capabilities.

20. The network node of claim 12, wherein the configuration associated with the execution of the UE AI or ML operation indicates one or more of the following: Stop all UE AI or ML operations in said UE AI or ML operation, stopping one or more of the UE AI or ML operations, updating the configuration of the UE AI or ML operation, or For additional UE AI or ML operations.

21. A method of wireless communication performed by a user equipment (UE), the method comprising: Send updates to a previously reported set of UE artificial intelligence (AI) or machine learning (ML) capabilities; as well as A configuration associated with performance of the UE AI or ML operation is received based at least in part on the update.

22. The method of claim 21 , wherein sending the update to the previously reported set of UE AI or ML capabilities comprises one or more of: The update is sent via UE Assistance Information (UAI), or The update is sent via dynamic capability update signaling.

23. The method of claim 22, wherein the dynamic capability update signaling comprises one or more of the following: Radio Resource Control (RRC) signaling, an indication of whether the UE supports one or more UE AI or ML operations, Autonomous signaling, or Signaling based on trigger conditions.

24. The method of claim 21 , wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: whether the UE supports execution of one or more UE AI or ML operations, said update to said previously reported set of UE AI or ML capabilities, one or more UE AI or ML models affected by said update to said previously reported set of UE AI or ML capabilities, one or more additional UE AI or ML models that are not affected by said update to said previously reported set of UE AI or ML capabilities, or and a performance parameter update for one or more of the UE AI or ML models, the performance parameter update supporting meeting timing requirements associated with the one or more UE AI or ML models.

25. The method of claim 21 , wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: one or more model combinations supported by the UE based at least in part on the update to a previously reported set of UE AI or ML capabilities, performance parameters associated with one or more model combinations supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, One or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, or and performance parameters associated with one or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities.

26. A method of wireless communication performed by a network node, the method comprising: Receiving updates to a previously reported set of user equipment (UE) artificial intelligence (AI) or machine learning (ML) capabilities; as well as A configuration associated with performance of the UE AI or ML operation is sent based at least in part on the update.

27. The method of claim 26, wherein receiving the update to the previously reported set of UE AI or ML capabilities comprises one or more of: receiving the update via UE Assistance Information (UAI), or The update is received via dynamic capability update signaling.

28. The method of claim 27, wherein the dynamic capability update signaling comprises one or more of the following: Radio Resource Control (RRC) signaling, an indication of whether the UE supports one or more UE AI or ML operations, Autonomous signaling, or Signaling based on trigger conditions.

29. The method of claim 26, wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: whether the UE supports execution of one or more UE AI or ML operations, said update to said previously reported set of UE AI or ML capabilities, one or more UE AI or ML models affected by said update to said previously reported set of UE AI or ML capabilities, one or more additional UE AI or ML models that are not affected by said update to said previously reported set of UE AI or ML capabilities, or and a performance parameter update for one or more of the UE AI or ML models, the performance parameter update supporting meeting timing requirements associated with the one or more UE AI or ML models.

30. The method of claim 26, wherein the update to the previously reported set of UE AI or ML capabilities indicates one or more of: one or more model combinations supported by the UE based at least in part on the update to a previously reported set of UE AI or ML capabilities, performance parameters associated with one or more model combinations supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, One or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities, or and performance parameters associated with one or more model combinations not supported by the UE based at least in part on the update to the previously reported set of UE AI or ML capabilities.

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