Machine learning (ML) data input configuration and reporting

Abstract

Certain aspects of the present disclosure provide techniques for wireless communication by a user equipment (UE). The UE receives a configuration of at least one machine learning function name (MLFN). The UE receives machine learning (ML) data associated with the at least one MLFN. The UE uses the ML data as input to at least one of: operation or training of an ML model associated with the at least one MLFN.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. patent application serial number 17 / 885,144 filed on August 10, 2022, which is hereby incorporated by reference into this document. Background Art Technical Field

[0003] Aspects of the present disclosure relate to wireless communications, and more particularly to techniques for machine learning (ML) data input configuration and reporting.

[0004] Related technologies

[0005] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, broadcasts or other similar types of services. These wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available wireless communication system resources with those users.

[0006] Although wireless communication systems have made tremendous technological advances over the years, challenges still exist. For example, complex and dynamic environments can still attenuate or block signals between wireless transmitters and wireless receivers. Therefore, there is a continued desire to improve the technical performance of wireless communication systems, including, for example: increasing the speed and data carrying capacity of communications, improving the efficiency of using shared communication media, reducing the power used by transmitters and receivers when performing communications, improving the reliability of wireless communications, avoiding redundant transmission and / or reception and related processing, expanding the coverage area of ​​wireless communications, increasing the number and types of devices that can access wireless communication systems, improving the ability of different types of devices to communicate with each other, and increasing the number and types of wireless communication media available for use. Therefore, there is a need to further improve wireless communication systems to overcome the above-mentioned technical challenges and other challenges. Summary of the invention

[0007] One aspect provides a method for wireless communication by a user equipment (UE). The method includes receiving a configuration of at least one machine learning function name (MLFN); receiving machine learning (ML) data associated with the at least one MLFN; and using the ML data as input to at least one of: operation or training of an ML model associated with the at least one MLFN.

[0008] In another aspect, a method for wireless communication by a network entity is provided. The method includes: sending a configuration of at least one MLFN to at least one UE; determining ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operating or training an ML model associated with the at least one MLFN; and sending the ML data to the at least one UE.

[0009] Another aspect provides a method for wireless communication by a UE. The method includes: sending UE capability information to a network entity; receiving from the network entity a configuration of at least one MLFN and a request for ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; and sending the ML data to the network entity in response to the request.

[0010] Another aspect provides a method for wireless communication by a network entity. The method includes receiving UE capability information from a UE; sending to the UE a configuration of at least one MLFN and a request for ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; and receiving the ML data from the UE in response to the request.

[0011] Other aspects provide an apparatus operable to, configured to, or otherwise adapted to perform any one or more of the foregoing methods and / or those described elsewhere herein; a non-transitory computer-readable medium comprising instructions that, when executed by a processor of the apparatus, cause the apparatus to perform the foregoing methods and those described elsewhere herein; a computer program product embodied on a computer-readable storage medium comprising code for performing the foregoing methods and those described elsewhere herein; and / or an apparatus comprising components for performing the foregoing methods and those described elsewhere herein. By way of example, an apparatus may include a processing system, a device having a processing system, or a processing system cooperating through one or more networks.

[0012] For purposes of illustration, the following description and drawings set forth certain features. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings depict certain features of the aspects described herein and should not be considered to limit the scope of the disclosure.

[0014] Figure 1An example wireless communication network is depicted.

[0015] Figure 2 An example decomposed base station (BS) architecture is depicted.

[0016] Figure 3 Aspects of an example BS and example user equipment (UE) are depicted.

[0017] Figure 4A , Figure 4B , Figure 4C and Figure 4D Various example aspects of data structures for a wireless communication network are described.

[0018] Figure 5 Depicted is a process flow diagram illustrating example communications between multiple UEs and a network entity.

[0019] Figure 6 Depicted is a process flow diagram illustrating example communications between a UE and a network entity.

[0020] Figure 7 Depicted is a process flow diagram illustrating example communications between a UE and a network entity.

[0021] Figure 8 Methods for wireless communications by a UE are described.

[0022] Fig. 9 Methods for wireless communications by a network entity are described.

[0023] Fig.10 Depicted is a process flow diagram illustrating example communications between a UE and a network entity.

[0024] Fig.11 Methods for wireless communications by a UE are described.

[0025] Fig.12 Methods for wireless communications by a network entity are described.

[0026] Fig.13 Aspects of an example communications device are depicted. DETAILED DESCRIPTION

[0027] Aspects of the present disclosure provide apparatus, methods, processing systems, and computer-readable media for machine learning (ML) data input configuration and reporting.

[0028] User equipment (UE) and network entity vendors may develop ML models for different UE and network entity settings / configurations (e.g., number of antennas). In some cases, for the ML model development and training phases, the UE may need to signal the network entity of the UE's settings / configurations so that the network entity can mark the ML data for a given UE and network entity settings / configurations. The network entity may also need to signal the UE of the network entity's settings / configurations so that the UE can mark the ML data for a given network entity and UE settings / configurations. In some cases, for the ML model operation (e.g., run) phase, ML data may need to be input to run the ML model. In some cases, input controls may also need to be input to evaluate key performance indicators (KPIs) of the ML model during the operation of the ML model to determine decisions related to switching the ML model, monitoring the ML model, and / or training the ML model.

[0029] The techniques proposed herein provide a signaling design for ML data input configuration and reporting to a network entity and / or UE. In one example, the information / ML data signaled by the network entity or UE can assist in the operation of the ML model at the UE or network entity. In another example, the information / training data signaled by the network entity or UE can assist in labeling the training data at the UE or network entity. The signaling techniques proposed herein can assist in the operation and / or training of the ML model.

[0030] Introduction to wireless communication networks

[0031] The techniques and methods described herein can be used in various wireless communication networks. Although various aspects may be described herein using terms commonly associated with 3G, 4G and / or 5G wireless technologies, various aspects of the present disclosure may also be applicable to other communication systems and standards not explicitly mentioned herein.

[0032] Figure 1 An example of a wireless communication network 100 is depicted in which various aspects described herein may be implemented.

[0033] In general, the wireless communication network 100 includes various network entities (alternatively, network elements or network nodes). A network entity is typically a communication device and / or a communication function performed by a communication device (e.g., user equipment (UE), base station (BS), component of a BS, server, etc.). For example, various functions of a network and various devices associated with and interacting with the network may be considered network entities. In addition, the wireless communication network 100 includes ground aspects, such as ground-based network entities (e.g., BS 102), and non-ground aspects, such as satellites 140 and aircraft 145, which may include airborne network entities (e.g., one or more BSs) that can communicate with other network elements (e.g., ground BSs) and UEs.

[0034] In the depicted example, the wireless communication network 100 includes a BS 102, a UE 104, and one or more core networks (such as an evolved packet core (EPC) 160 and a 5G core (5GC) network 190) that interoperate to provide communication services over various communication links (including wired and wireless links).

[0035] Figure 1 Various example UEs 104 are depicted, which may more generally include: a cellular phone, a smart phone, a Session Initiation Protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player, a camera, a game console, a tablet computer, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor / actuator, a display, an Internet of Things (IoT) device, an Always-On (AON) device, an edge processing device, or other similar devices. UE 104 may also be more generally referred to as a mobile device, a wireless device, a wireless communication device, a station, a mobile station, a subscriber station, a mobile subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a remote device, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, and others.

[0036] BS 102 wirelessly communicates with (e.g., transmits signals to or receives signals from) UE 104 via communication link 120. Communication link 120 between BS 102 and UE 104 may include uplink (UL) (also referred to as a reverse link) transmissions from UE 104 to BS 102 and / or downlink (DL) (also referred to as a forward link) transmissions from BS 102 to UE 104. In various aspects, communication link 120 may employ multiple-input multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity.

[0037] BS 102 may generally include: NodeB, enhanced NodeB (eNB), next generation enhanced NodeB (ng-eNB), next generation NodeB (gNB or gNodeB), access point, transceiver base station, radio BS, radio transceiver, transceiver function, transmission and reception point, and / or other. Each of BS 102 may provide communication coverage for a corresponding geographic coverage area 110, which may sometimes be referred to as a cell, and may overlap in some cases (e.g., a small cell 102' may have a coverage area 110' that overlaps with the coverage area 110 of a macro cell). For example, a BS may provide communication coverage for a macro cell (covering a relatively large geographic area), a pico cell (covering a relatively small geographic area, such as a stadium), a femto cell (relatively small geographic area (e.g., a home)), and / or other types of cells.

[0038] Although BS102 is depicted as a single communication device in various aspects, BS102 can be implemented in various configurations. For example, one or more components of BS102 can be decomposed, including a central unit (CU), one or more distributed units (DU), one or more radio units (RU), a near real-time (near RT) RAN intelligent controller (RIC) or a non-real-time (non-RT) RIC, to name a few examples. In another example, various aspects of BS102 can be virtualized. More generally, a BS (e.g., BS102) may include components located at a single physical location or components located at various physical locations. In an example in which BS102 includes components located at various physical locations, various components may each perform each function so that various components together achieve functionality similar to that of a BS 102 located at a single physical location. In some aspects, a BS102 including components located at various physical locations may be referred to as a decomposed radio access network (RAN) architecture, such as an open RAN (O-RAN) or virtualized RAN (VRAN) architecture. Figure 2 An example decomposed BS architecture is depicted and described.

[0039] Different BSs 102 within the wireless communication network 100 may also be configured to support different radio access technologies (such as 3G, 4G, and / or 5G). For example, a BS 102 configured for 4G LTE (collectively referred to as the Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E-UTRAN)) may interface with the EPC 160 via a first backhaul link 132 (e.g., an S1 interface). A BS 102 configured for 5G (e.g., 5G NR or Next Generation RAN (NG-RAN)) may interface with the 5GC 190 via a second backhaul link 184. The BSs 102 may communicate with each other directly or indirectly (e.g., via the EPC 160 or the 5GC 190) via a third backhaul link 134 (e.g., an X2 interface), which may be wired or wireless.

[0040] The wireless communication network 100 may subdivide the electromagnetic spectrum into various categories, frequency bands, channels, or other characteristics. In some aspects, the subdivision is provided based on wavelength and frequency, where frequency may also be referred to as a carrier, subcarrier, channel, tone, or subband. For example, 3GPP currently defines frequency range 1 (FR1) as including 600 MHz to 6 GHz, which is often (interchangeably) referred to as "below 6 GHz". Similarly, 3GPP currently defines frequency range 2 (FR2) as including 26 GHz-41 GHz, which is sometimes (interchangeably) referred to as "millimeter wave" ("mmW" or "mmWave"). A BS (e.g., a mmWave BS, such as BS 180) configured to communicate using mmWave / near mmWave radio bands may utilize beamforming (e.g., 182) with a UE (e.g., 104) to improve path loss and range.

[0041] The communication link 120 between the BS 102 and, for example, the UE 104 may be through one or more carriers, which may have different bandwidths (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz, 400 MHz, and / or other MHz) and may be aggregated in various aspects. The carriers may be adjacent to each other or may not be adjacent to each other. The allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated for DL ​​compared to UL).

[0042] Communications using higher frequency bands may have higher path loss and shorter range than communications at lower frequencies. Therefore, some BSs (e.g. Figure 1180) may utilize beamforming 182 with UE 104 to improve path loss and range. For example, BS 180 and UE 104 may each include multiple antennas (such as antenna elements, antenna panels and / or antenna arrays) to facilitate beamforming. In some cases, BS 180 may send beamformed signals to UE 104 in one or more transmit directions 182'. UE 104 may receive beamformed signals from BS 180 in one or more receive directions 182". UE 104 may also send beamformed signals to BS 180 in one or more transmit directions 182". BS 180 may also receive beamformed signals from UE 104 in one or more receive directions 182'. BS 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of BS 180 and UE 104. It is noteworthy that the transmit direction and receive direction of BS 180 may be the same or different. Similarly, the transmit direction and receive direction of UE 104 may be the same or different.

[0043] Wireless communication network 100 also includes Wi-Fi AP 150 that communicates with Wi-Fi station (STA) 152 via communication link 154 in, for example, the 2.4 GHz and / or 5 GHz unlicensed spectrum.

[0044] Certain UEs 104 may communicate with each other using a device-to-device (D2D) communication link 158. The D2D communication link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), a physical sidelink control channel (PSCCH), and / or a physical sidelink feedback channel (PSFCH).

[0045] The EPC 160 may include various functional components, including: a mobility management entity (MME) 162, other MMEs 164, a serving gateway 166, a multimedia broadcast multicast service (MBMS) gateway 168, a broadcast multicast service center (BM-SC) 170, and / or a packet data network (PDN) gateway 172, such as in the depicted example. The MME 162 may communicate with a home subscriber server (HSS) 174. The MME 162 is a control node that handles signaling between the UE 104 and the EPC 160. In general, the MME 162 provides bearer and connection management.

[0046] Typically, user Internet Protocol (IP) packets are delivered through the Serving Gateway 166, which itself is connected to the PDN Gateway 172. The PDN Gateway 172 provides UE IP address allocation and other functions. The PDN Gateway 172 and the BM-SC 170 are connected to IP services 176, which may include, for example, the Internet, an intranet, an IP Multimedia Subsystem (IMS), a Packet Switched (PS) streaming media service, and / or other IP services.

[0047] The BM-SC 170 may provide functionality for MBMS user service configuration and delivery. The BM-SC 170 may serve as an entry point for content provider MBMS delivery, may be used to authorize and initiate MBMS bearer services within a public land mobile network (PLMN), and / or may be used to schedule MBMS delivery. The MBMS Gateway 168 may be used to distribute MBMS services to BSs 102 belonging to a multicast broadcast single frequency network (MBSFN) area broadcasting a specific service, and / or may be responsible for session management (start / stop) and for collecting eMBMS related charging information.

[0048] 5GC 190 may include various functional components, including: access and mobility management function (AMF) 192, other AMFs 193, session management function (SMF) 194, and user plane function (UPF) 195. AMF 192 may communicate with unified data management (UDM) 196.

[0049] AMF 192 is a control node that handles signaling between UE 104 and 5GC 190. AMF 192 provides, for example, Quality of Service (QoS) flow and session management.

[0050] Internet Protocol (IP) packets are delivered through UPF 195, which is connected to IP Services 197 and provides UE IP address allocation and other functions for 5GC 190. IP Services 197 may include, for example, the Internet, Intranet, IMS, PS streaming services, and / or other IP services.

[0051] The wireless communication network 100 also includes a machine learning (ML) component 198 that can be configured to perform Figure 8 Operation 800 and / or Fig.11 The wireless communication network 100 also includes an ML component 199, which may be configured to perform Fig. 9 Operation 900 and / or Fig.12 Operation 1200.

[0052] In various aspects, a network entity or network node may be implemented as a converged BS, a decomposed BS, a component of a BS, an integrated access and backhaul (IAB) node, a relay node, a sidelink node, to name a few examples.

[0053] Figure 2 An example decomposed BS 200 architecture is depicted. The decomposed BS 200 architecture may include one or more central units (CUs) 210 that may communicate directly with a core network 220 via a backhaul link, or indirectly with the core network 220 through one or more decomposed BS units, such as a near real-time (near-RT) RAN intelligent controller (RIC) 225 via an E2 link, or a non-real-time (non-RT) RIC 215 associated with a service management and orchestration (SMO) framework 205, or both. The CU 210 may communicate with one or more distributed units (DUs) 230 via respective midhaul links, such as an F1 interface. The DU 230 may communicate with one or more radio units (RUs) 240 via respective fronthaul links. The RU 240 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 may be served simultaneously by multiple RUs 240.

[0054] Each of the units (e.g., CU 210, DU 230, RU 240, and near-RT RIC 225, non-RT RIC 215, and SMO framework 205) may include 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 or 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, each unit may include a wired interface configured to receive or send signals to one or more of the other units via a wired transmission medium. Additionally or alternatively, each unit may include a wireless interface that may include a receiver, a transmitter, or a transceiver (such as a radio frequency (RF) transceiver) configured to receive or send signals or both to one or more of the other units via a wireless transmission medium.

[0055] In some aspects, the CU 210 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 that is configured to communicate signals with other control functions hosted by the CU 210. The CU 210 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, the CU 210 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, the CU 210 may be implemented to communicate with the DU 230 for network control and signaling.

[0056] The DU 230 may correspond to a logical unit that includes one or more BS functions for controlling the operation of one or more RUs 240. In some aspects, the DU 230 may host one or more of 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.) at least in part according to a functional split such as those defined by the 3rd Generation Partnership Project (3GPP). In some aspects, the DU 230 may also 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 the DU 230 or with control functions hosted by the CU 210.

[0057] The lower layer functionality may be implemented by one or more RUs 240. In some deployments, a RU 240 controlled by a DU 230 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 240 may be implemented to handle over-the-air (OTA) communications with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU 240 may be controlled by the corresponding DU 230. In some scenarios, this configuration may enable the implementation of the DU 230 and the CU 210 in a cloud-based RAN architecture (such as a vRAN architecture).

[0058] The SMO framework 205 may be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 205 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 205 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 290) to perform network element lifecycle management (such as to instantiate 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 210, DU 230, RU 240, and near-RT RIC 225. In some specific implementations, the SMO framework 205 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 211) via the O1 interface. Additionally, in some specific implementations, the SMO framework 205 may communicate directly with one or more RUs 240 via the O1 interface. The SMO framework 205 may also include a non-RT RIC 215 configured to support the functionality of the SMO framework 205 .

[0059] The non-RT RIC 215 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 225. The non-RT RIC 215 may be coupled to or in communication with the near-RT RIC 225 (such as via an A1 interface). The near-RT RIC 225 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 210, one or more DUs 230, or both, and the O-eNB with the near-RT RIC 225.

[0060] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 225, the non-RT RIC 215 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 225 and may be received from a non-network data source or from a network function at the SMO framework 205 or the non-RT RIC 215. In some examples, the non-RT RIC 215 or the near-RT RIC 225 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 215 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 205 (such as via reconfiguration of O1) or via the creation of RAN management policies (such as A1 policies).

[0061] Figure 3 Aspects of an example BS 102 and UE 104 are depicted.

[0062] In general, BS 102 includes various processors (e.g., 320, 330, 338, and 340), antennas 334a-334t (collectively 334), transceivers 332a-332t (collectively 332) including modulators and demodulators, and other aspects that enable wireless transmission of data (e.g., data source 312) and wireless reception of data (e.g., data sink 339). For example, BS 102 can transmit and receive data between BS 102 and UE 104. BS 102 includes a controller / processor 340 that can be configured to implement various functions described herein related to wireless communication.

[0063] BS 102 includes a controller / processor 340 that can be configured to implement various functions related to wireless communication. In the depicted example, controller / processor 340 includes an ML component 341, which can represent Figure 1 ML component 199 of BS 102. It is worth noting that, although depicted as an aspect of controller / processor 340, in other specific implementations, ML component 341 may additionally or alternatively be implemented in various other aspects of BS 102.

[0064] In general, the UE 104 includes various processors (e.g., 358, 364, 366, and 380), antennas 352a-352r (collectively 352), transceivers 354a-354r (collectively 354) including modulators and demodulators, and other aspects that enable wireless transmission of data (e.g., retrieved from a data source 362) and wireless reception of data (e.g., provided to a data sink 360). The UE 104 includes a controller / processor 380 that can be configured to implement various functions described herein related to wireless communications.

[0065] UE 104 includes a controller / processor 380 that can be configured to perform various functions related to wireless communication. In the depicted example, the controller / processor 380 includes an ML component 381, which can represent Figure 1 ML component 198 of the UE 104. It is worth noting that, although depicted as an aspect of the controller / processor 380, in other specific implementations, the ML component 381 can additionally or alternatively be implemented in various other aspects of the UE 104.

[0066] Regarding example downlink transmissions, BS 102 includes a transmit processor 320 that can receive data from a data source 312 and control information from a controller / processor 340. The control information can be for a physical broadcast channel (PBCH), a physical control format indicator channel (PCFICH), a physical HARQ indicator channel (PHICH), a physical downlink control channel (PDCCH), a group common PDCCH (GC PDCCH), and / or others. In some examples, the data can be for a physical downlink shared channel (PDSCH).

[0067] The transmit processor 320 may process (e.g., encode and symbol map) data and control information to obtain data symbols and control symbols, respectively. The transmit processor 320 may also generate reference symbols, such as for a primary synchronization signal (PSS), a secondary synchronization signal (SSS), a PBCH demodulation reference signal (DMRS), and a channel state information reference signal (CSI-RS).

[0068] The transmit (TX) multiple-input multiple-output (MIMO) processor 330 may perform spatial processing (e.g., pre-coding) on ​​data symbols, control symbols, and / or reference symbols, where applicable, and may provide an output symbol stream to a modulator (MOD) in a transceiver 332a-332t. Each modulator in the transceiver 332a-332t may process a corresponding output symbol stream to obtain an output sample stream. Each modulator may further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. The downlink signals from the modulators in the transceivers 332a-332t may be transmitted via antennas 334a-334t, respectively.

[0069] To receive downlink transmissions, UE 104 includes antennas 352a-352r that can receive downlink signals from BS 102 and can provide received signals to demodulators (DEMODs) in transceivers 354a-354r, respectively. Each demodulator in transceivers 354a-354r can condition (e.g., filter, amplify, downconvert, and digitize) a corresponding received signal to obtain input samples. Each demodulator can further process the input samples to obtain received symbols.

[0070] A MIMO detector 356 may obtain received symbols from all demodulators in transceivers 354a-354r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A receive processor 358 may process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for the UE 104 to a data sink 360, and provide decoded control information to a controller / processor 380.

[0071] With respect to example uplink transmissions, the UE 104 also includes a transmit processor 364 that may receive and process data from a data source 362 (e.g., for a PUSCH) and control information from a controller / processor 380 (e.g., for a physical uplink control channel (PUCCH)). The transmit processor 364 may also generate reference symbols for reference signals (e.g., a sounding reference signal (SRS)). The symbols from the transmit processor 364 may be pre-decoded by a TX MIMO processor 366, if applicable, further processed by a modulator in the transceivers 354a-354r (e.g., for SC-FDM), and transmitted to the BS 102.

[0072] At BS 102, uplink signals from UE 104 may be received by antennas 334 a-334 t, processed by demodulators in transceivers 332 a-332 t, detected by MIMO detector 336 if applicable, and further processed by receive processor 338 to obtain decoded data and control information transmitted by UE 104. Receive processor 338 may provide decoded data to data sink 339 and decoded control information to controller / processor 340.

[0073] Memories 342 and 382 may store data and program codes for BS 102 and UE 104, respectively.

[0074] Scheduler 344 may schedule UEs for data transmission on the downlink and / or uplink.

[0075] In various aspects, BS 102 may be described as sending and receiving various types of data associated with the methods described herein. In these contexts, "sending" may refer to various mechanisms for outputting data, such as outputting data from a data source 312, a scheduler 344, a memory 342, a transmit processor 320, a controller / processor 340, a TX MIMO processor 330, a transceiver 332a-332t, an antenna 334a-334t, and / or other aspects described herein. Similarly, "receiving" may refer to various mechanisms for obtaining data, such as obtaining data from an antenna 334a-334t, a transceiver 332a-332t, a RX MIMO detector 336, a controller / processor 340, a receive processor 338, a scheduler 344, a memory 342, and / or other aspects described herein.

[0076] In various aspects, the UE 104 may also be described as sending and receiving various types of data associated with the methods described herein. In these contexts, "sending" may refer to various mechanisms for outputting data, such as outputting data from a data source 362, a memory 382, ​​a transmit processor 364, a controller / processor 380, a TX MIMO processor 366, a transceiver 354a-354t, an antenna 352a-352t, and / or other aspects described herein. Similarly, "receiving" may refer to various mechanisms for obtaining data, such as obtaining data from an antenna 352a-352t, a transceiver 354a-354t, a RX MIMO detector 356, a controller / processor 380, a receive processor 358, a memory 382, ​​and / or other aspects described herein.

[0077] In some aspects, the processor may be configured to perform various operations (such as those associated with the methods described herein) and send (output) data to or receive (obtain) data from another interface configured to send or receive data, respectively.

[0078] Figure 4A , Figure 4B , Figure 4C and Figure 4D Describes a method for use in a wireless communication network such as Figure 1 Various aspects of the data structure of the wireless communication network 100).

[0079] Specifically, Figure 4A is a diagram 400 illustrating an example of a first subframe within a 5G (e.g., 5G NR) frame structure, Figure 4B is a diagram 430 illustrating an example of a DL channel within a 5G subframe, Figure 4C is a diagram 450 illustrating an example of a second subframe within a 5G frame structure, and Figure 4DFIG480 is a diagram illustrating an example of UL channels within a 5G subframe.

[0080] Wireless communication systems may utilize orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) on both the uplink and downlink. Such systems may also support half-duplex operation using time division duplex (TDD). OFDM and single carrier frequency division multiplexing (SC-FDM) can be used to maximize the system bandwidth (e.g., Figure 4B and Figure 4D ) is divided into multiple orthogonal subcarriers. Each subcarrier can be modulated with data. Modulation symbols can be transmitted in the frequency domain using OFDM and / or in the time domain using SC-FDM.

[0081] The wireless communication frame structure may be frequency division duplex (FDD), where for a particular set of subcarriers, subframes within the set of subcarriers are dedicated to either DL or UL. The wireless communication frame structure may also be time division duplex (TDD), where for a particular set of subcarriers, subframes within the set of subcarriers are dedicated to both DL and UL.

[0082] exist Figure 4A and Figure 4C In the wireless communication frame structure, TDD is used, where D is DL, U is UL, and X can be used flexibly between DL / UL. The UE can be configured with a time slot format (dynamically configured by DL control information (DCI) or semi-statically / statically configured by radio resource control (RRC) signaling) through the received time slot format indicator (SFI). In the depicted example, the 10ms frame is divided into 10 equally sized 1ms subframes. Each subframe may include one or more time slots. In some examples, each time slot may include 7 or 14 symbols, depending on the time slot format. The subframe may also include a micro-time slot, which typically has fewer symbols than a whole time slot. Other wireless communication technologies may have different frame structures and / or different channels.

[0083] In certain aspects, the number of slots within a subframe is based on the slot configuration and parameter set. For example, for slot configuration 0, different parameter sets (μ) 0 to 5 allow for 1, 2, 4, 8, 16, and 32 slots per subframe, respectively. For slot configuration 1, different parameter sets 0 to 2 allow for 2, 4, and 8 slots per subframe, respectively. Thus, for slot configuration 0 and parameter set μ, there are 14 symbols / slot and 2μ slots / subframe. The subcarrier spacing and symbol length / duration are a function of the parameter set. The subcarrier spacing may be equal to 2 μ × 15kHz, where μ is parameter set 0 to 5. Thus, parameter set μ=0 has a subcarrier spacing of 15kHz, and parameter set μ=5 has a subcarrier spacing of 480kHz. The symbol length / duration is inversely related to the subcarrier spacing. Figure 4A , Figure 4B , Figure 4C and Figure 4D An example is provided for slot configuration 0 with 14 symbols per slot and parameter set μ=2 with 4 slots per subframe. The slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 μs.

[0084] like Figure 4A , Figure 4B , Figure 4C and Figure 4D As depicted in , a resource grid can be used to represent a frame structure. Each time slot includes a resource block (RB) (also called a physical RB (PRB)) extending, for example, 12 consecutive subcarriers. The resource grid is divided into a plurality of resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.

[0085] like Figure 4A As illustrated, some of the REs carry data for UEs (eg, Figure 1 and Figure 3 The RS may include a demodulation RS (DMRS) and / or a channel state information reference signal (CSI-RS) for channel estimation at the UE. The RS may also include a beam measurement RS (BRS), a beam refinement RS (BRRS), and / or a phase tracking RS (PT-RS).

[0086] Figure 4B Examples of various DL channels located within a subframe of a frame are illustrated. A physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs), each CCE comprising, for example, nine RE groups (REGs), each REG comprising, for example, four consecutive REs in an OFDM symbol.

[0087] The primary synchronization signal (PSS) may be located in symbol 2 of a particular subframe of a frame. The PSS is transmitted by a UE (e.g., Figure 1 and Figure 3 104) is used to determine subframe / symbol timing and physical layer identification.

[0088] A Secondary Synchronization Signal (SSS) may be within symbol 4 of a particular subframe of a frame. The SSS is used by the UE to determine the physical layer cell identity group number and radio frame timing.

[0089] Based on the physical layer identifier and the physical layer cell identifier group number, the UE can determine the physical cell identifier (PCI). Based on the PCI, the UE can determine the location of the aforementioned DMRS. The physical broadcast channel (PBCH) carrying the master information block (MIB) can be logically grouped with the PSS and SSS to form a synchronization signal (SS) / PBCH block. The MIB provides the system frame number (SFN) and the number of RBs in the system bandwidth. The physical downlink shared channel (PDSCH) carries user data, broadcast system information (such as system information blocks (SIBs)) that is not sent via the PBCH, and / or paging messages.

[0090] like Figure 4C As illustrated, some of the REs carry DMRS for channel estimation at the BS (indicated as R for one particular configuration, but other DMRS configurations are possible). The UE may send a DMRS for the PUCCH and a DMRS for the PUSCH. The PUSCH DMRS may be sent, for example, in the first one or two symbols of the PUSCH. The PUCCH DMRS may be sent in different configurations depending on whether a short PUCCH or a long PUCCH is sent and depending on the specific PUCCH format used. The UE 104 may send a sounding reference signal (SRS). The SRS may be sent, for example, in the last symbol of a subframe. The SRS may have a comb structure, and the UE may send the SRS on one of the combs. The SRS may be used by the BS for channel quality estimation to enable frequency-dependent scheduling on the UL.

[0091] Figure 4D Examples of various UL channels within a subframe of a frame are illustrated. The PUCCH may be located at a position as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, channel quality indicator (CQI), precoding matrix indicator (PMI), rank indicator (RI), and HARQ ACK / NACK feedback. The PUSCH carries data and may additionally be used to carry a buffer status report (BSR), a power headroom report (PHR), and / or UCI.

[0092] Example AI / ML-based procedures

[0093] User equipment (UE) and network entity vendors may develop one or more machine learning (ML) models, model structures (MS) and / or parameter sets (PS) in accordance with machine learning function names (MLFNs). This is done because the modems and / or hardware accelerators of the UE and / or network entity have limited memory.

[0094] UE and network entity vendors may develop ML models for different UE and network entity settings / configurations (e.g., number of antennas). In some cases, for the ML model development and training phases, the UE may need to signal the network entity of the UE's settings / configurations so that the network entity can mark ML data for a given UE and network entity settings / configurations. The network entity may also need to signal the UE of the network entity's settings / configurations so that the UE can mark ML data for a given network entity and UE settings / configurations. In some cases, for the ML model operation (e.g., run) phase, ML data may need to be input to run the ML model. In some cases, input controls may also need to be input to evaluate key performance indicators (KPIs) of the ML model during the operation of the ML model to determine decisions related to switching the ML model, monitoring the ML model, and / or training the ML model.

[0095] Aspects related to web-based machine learning (ML) data input

[0096] Aspects of the present disclosure provide apparatus, methods, processing systems, and computer-readable media for web-based machine learning (ML) data input configuration and reporting.

[0097] For example, the techniques proposed herein provide a signaling design for network-based ML data input configuration and reporting. In one example, network-based ML data signaled to a UE by a network entity may assist in the operation of an ML model at the UE. In another example, network-based ML training data signaled to a UE by a network entity may assist in labeling network-based ML training data at the UE. The signaling techniques proposed herein can assist in the operation and / or training of an ML model at a UE.

[0098] The technology proposed in this article can be referenced Figures 5 to 9 To understand.

[0099] Figure 5 A process flow of communication between a network entity, a first UE, and a second UE is depicted. In some aspects, the network entity may be related to Figure 1 and Figure 3 BS102 as depicted and described or relating to Figure 2 An example of a decomposed BS is depicted and described. Similarly, the first UE and / or the second UE may be related to Figure 1 and Figure 3 An example of a UE 104 is depicted and described. However, in other aspects, the UE 104 may be another type of wireless communication device and the BS 102 may be another type of network entity or network node, such as those described herein.

[0100] The first UE and the second UE are located in a coverage area of ​​a network entity. The network entity configures the first UE and the second UE with the same machine learning function name (MLFN), ML model identification (ID), and / or model structure (MS) ID. The network entity may broadcast common parameters (e.g., non-UE-specific artificial intelligence (AI) / ML data) to the first UE and the second UE.

[0101] At 502, the network entity sends AI / ML data input to the first UE (e.g., per MLFN, per ML model ID, or per MS ID). In one example, the network entity may send the AI / ML data input to the first UE via a system information block (SIB). In another example, the network entity may send the AI / ML data input to the first UE via a multicast broadcast service (MBS) on an MBS channel.

[0102] In certain aspects, information provided by a network entity may be used to assist in the operation of at least one ML model at a first UE and / or network entity. For example, AI / ML data may be used as input to run an ML model (e.g., input parameters for inference), switch an ML model (e.g., auxiliary information or metadata to enable MS or parameter set (PS) switching during inference), monitor an ML model (e.g., ground truth to determine whether an MS or PS is operating correctly), and / or train an ML model (e.g., ground truth to update a PS). An example of AI / ML data (e.g., model management data) may include network or UE configuration information, such as the number of antennas and the maximum number of retransmissions. Model management data may be used to operate an ML model. Another example of AI / ML data (e.g., training data) may include actual data such as network load and / or event thresholds. The training data may be used to train an ML model.

[0103] In certain aspects, the information provided by the network entity may be used to assist in labeling training data at the first UE and / or the network entity. In some cases, the first UE may include such information as part of a data collection procedure to assist in offline training of the ML model.

[0104] At 504, the network entity sends the AI / ML data input to the second UE. In one example, the network entity may send the AI / ML data input to the second UE via the SIB. In another example, the network entity may send the AI / ML data input to the second UE via MBS on an MBS channel.

[0105] At 506 , the first UE uses the received AI / ML data as input for operation and / or training of the ML model.

[0106] At 508, the second UE uses the received AI / ML data as input for operation and / or training of the ML model.

[0107] like Figure 6 As shown, the network entity determines an AI / ML data input at 602. For example, the network entity may determine a UE-specific AI / ML data input that can be sent to the UE in a unicast message.

[0108] At 604, the network entity sends the determined AI / ML data input to the UE (e.g., per MLFN, per ML model ID, or per MS ID). In one example, when the UE is connected to the network entity, the network entity may send the determined AI / ML data input to the UE (e.g., for labeling AI / ML training data). In another example, when the UE is configured with MLFN, ML model ID, or MS ID, the network entity may send the determined AI / ML data input to the UE (e.g., for operation of the ML model). The UE may use the received AI / ML data as input for operation and / or training of the ML model.

[0109] At 606, the UE sends an AI / ML data input activation or deactivation indication to the network entity. The UE may send the AI / ML data input activation or deactivation indication using a medium access control (MAC) control element (CE), UE assistance information (UAI), and / or uplink control information (UCI). In one example, the UE may transmit an indication to activate sending the AI / ML data input from the network entity based on one or more first requirements of the UE. In another example, the UE may transmit an indication to deactivate sending the AI / ML data input from the network entity based on one or more second requirements of the UE.

[0110] like Figure 7 As shown in , at 702, the UE sends an AI / ML data input request to a network entity (e.g., per MLFN, per ML model ID, or per MS ID). In one example, the UE may send the AI / ML data input request to the network entity via UE assistance information (UAI). In another example, the UE may send the AI / ML data input request to the network entity via a subscription request.

[0111] In one example, the AI / ML data input request may include the MLFN for which the AI / ML data input is requested. In another example, the AI / ML data input request may include the ML model ID for which the AI / ML data input is requested. In another example, the AI / ML data input request may include the MS ID for which the AI / ML data input is requested. In another example, the AI / ML data input request may include geographic information, such as the current geographic area of ​​the UE, a public land mobile network (PLMN), cell information, and / or a frequency list. In another example, the AI / ML data input request may include a valid time, such as a duration and / or an interval time, during which the AI / ML data input must be provided to the UE by the network entity. In another example, the AI / ML data input request may include one or more network configurations. In another example, the AI / ML data input request may include one or more network settings. In another example, the AI / ML data input request may include the type of AI / ML data input, such as metadata, training data, and / or reasoning data.

[0112] At 704, the network entity sends the AI / ML data input to the UE. In one example, the network entity may send the AI / ML data input to the UE via the SIB. In another example, the network entity may send the AI / ML data input to the UE via the MBS on the MBS channel.

[0113] At 706, the UE sends an AI / ML data input activation or deactivation indication to the network entity. The UE may use MAC CE, UAI and / or UCI to send the AI / ML data input activation or deactivation indication. In one example, the UE may transmit an indication to the UE to activate sending the AI / ML data input from the network entity based on one or more first requirements of the UE. In another example, the UE may transmit an indication to the UE to deactivate sending the AI / ML data input from the network entity based on one or more second requirements of the UE.

[0114] In certain aspects, the network entity may store the AI / ML data input request received from the UE (e.g., at least until the geographic information and validity time remain valid), and subsequently pass the AI / ML data input request to another network entity (e.g., during a UE context establishment, modification, and / or retrieval procedure). For example, the network entity may pass the AI / ML data input request to another network entity when one or more conditions are met. In one example, the one or more conditions may be met when the UE moves from a connected state to an idle state or an inactive state. In another example, the one or more conditions may be met when the UE moves from an idle state or an inactive state to a connected state.

[0115] Figure 8A method for use by a UE such as, Figure 1 and Figure 3 An example of method 800 for performing wireless communications with a UE 104).

[0116] Method 800 begins at step 805, i.e., receiving a configuration for at least one MLFN. In some cases, the operation of this step refers to the operation described in reference to Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0117] Then, the method 800 proceeds to step 810, that is, receiving ML data associated with the at least one MLFN. In some cases, the operation of this step refers to the operation of Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0118] Then, method 800 proceeds to step 815, i.e., using the ML data as input to at least one of: operating or training an ML model associated with the at least one MLFN. In some cases, the operating of this step refers to the operation of Fig.13 The described circuitry for use and / or code for use, or may be executed by the circuitry for use and / or code for use.

[0119] In some aspects, the operation of the ML model corresponds to at least one of: running the ML model, switching the ML model, or monitoring the ML model; and the training of the ML model includes labeling the ML data for specific UE and network entity settings or configurations, and performing the training of the ML model using the labeled ML data.

[0120] In some aspects, the ML data includes model management data and training data; the model management data indicates at least one of: a number of antennas or a number of retransmissions; and the training data indicates at least one of: network load, event thresholds, or L1 and L2 measurements.

[0121] In some aspects, the ML data is applicable to one or more other UEs in the network entity coverage or target area, and the ML data indicates at least one of: an ML model ID or an MS ID along with the ML data itself.

[0122] In some aspects, the ML data is received via at least one of: a SIB or an MBS on an MBS channel.

[0123] In some aspects, the ML data is received via a unicast message when at least one of the following conditions is met: the UE is connected to a network entity; or the UE is configured with at least one of: the at least one MLFN, ML model ID, or MS ID.

[0124] In some aspects, the method 800 further includes sending an indication to the network entity using MAC-CE, UAI, or UCI to activate or deactivate sending the ML data to the UE. In some cases, the operation of this step refers to the following steps: Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0125] In some aspects, the method 800 further includes sending a request for the ML data to a network entity; and in response to the request, receiving the ML data. In some cases, the operation of this step refers to the operation of Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or the code for sending.

[0126] In some aspects, the sending comprises sending the request via at least one of: a UAI or a subscription request.

[0127] In some aspects, the request indicates at least one of: the at least one MLFN; the ML model ID; the MS ID; geographic information, the geographic information comprising at least one of: the current geographic area, PLMN, cell information, or frequency list of the UE; a validity time, the validity time comprising at least one of: a duration or interval during which the ML data must be provided to the UE; one or more network configurations or settings; or a type of the ML data, the type of the ML data comprising at least one of: metadata, training data, or inference data.

[0128] In some aspects, the method 800 further includes sending an indication to the network entity using MAC-CE, UAI, or UCI to activate or deactivate sending the ML data to the UE. In some cases, the operation of this step refers to the following steps: Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0129] In one aspect, method 800 or any aspect related thereto may be performed by an apparatus such as Fig.13 The method 800 is performed by a communication device 1300 that includes various components that are operable, configured, or adapted to perform the method 800. The communication device 1300 is described in more detail below.

[0130] It should be noted that Figure 8 This is merely one example of one method, and other methods are possible consistent with the present disclosure and including fewer, additional, or alternative steps.

[0131] Fig. 9 shows a method for use by a network entity such as Figure 1 and Figure 3 BS102 or such Figure 2 An example of a method 900 for performing wireless communications in a decomposed BS) as discussed.

[0132] Method 900 begins at step 905, that is, sending a configuration of at least one MLFN to at least one UE. In some cases, the operation of this step refers to the following. Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0133] Then, the method 900 proceeds to step 910, i.e., determining ML data associated with the at least one MLFN, the ML data to be used as input to at least one of: operating or training an ML model associated with the at least one MLFN. In some cases, the operation of this step refers to referring to Fig.13 The described circuitry for determining and / or code for determining may be performed by, or may be executed by, the circuitry for determining and / or the code for determining.

[0134] Then, the method 900 proceeds to step 915, that is, sending the ML data to the at least one UE. In some cases, the operation of this step refers to the operation of Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0135] In some aspects, the operation of the ML model includes at least one of: running the ML model, switching the ML model, or monitoring the ML model; the training of the ML model includes marking the ML data for specific UE and network entity settings or configurations, and performing the training of the ML model using the marked ML data; the ML data represents model management data and training data; the model management data indicates at least one of the following: the number of antennas or the number of retransmissions; the training data indicates at least one of the following: network UE load, event thresholds, or L1 and L2 measurements.

[0136] In some aspects, the at least one UE corresponds to a first UE and a second UE; and the first UE and the second UE require the same ML data input or control associated with at least one of: at least one MLFN, ML Model ID, or MS ID.

[0137] In some aspects, the ML data is sent to the first UE and the second UE via at least one of: a SIB or an MBS.

[0138] In some aspects, the at least one UE corresponds to a first UE; and the ML data is sent to the first UE via a unicast message.

[0139] In some aspects, the method 900 further includes receiving an indication from the first UE using MAC-CE, UAI, or UCI to activate or deactivate sending the ML data to the first UE. In some cases, the operation of this step refers to as described in reference Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0140] In some aspects, the at least one UE corresponds to a first UE. In some aspects, the method 900 also includes receiving a request for the ML data from the first UE. In some cases, the operation of this step refers to as described in reference Fig.13 The described circuitry for receiving and / or code for receiving may be, or may be, performed by the circuitry for receiving and / or code for receiving. In some aspects, the ML data is sent to the first UE in response to the request.

[0141] In some aspects, the request is received via at least one of: a UAI or a subscription request.

[0142] In some aspects, the request indicates at least one of: the at least one MLFN; the ML model ID; the MS ID; geographic information, the geographic information comprising at least one of: the current geographic area, PLMN, cell information or frequency list of the first UE; a validity time, the validity time comprising at least one of: a duration or interval time during which the ML data must be provided to the first UE; one or more network configurations; or a type of the ML data, the type of the ML data comprising at least one of: metadata, training data or inference data.

[0143] In some aspects, the method 900 further includes receiving an indication from the first UE to activate or deactivate sending the ML data to the first UE. In some cases, the operation of this step refers to as described in reference Fig.13The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0144] In some aspects, the method 900 further includes sending the request to another network entity when one or more conditions are met. In some cases, the operation of this step refers to the Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0145] In some aspects, the request is sent during a handover from one network entity to another network entity; and one or more conditions are satisfied when the first UE moves from a connected state to an idle state or an inactive state or vice versa.

[0146] In one aspect, method 900 or any aspect related thereto may be performed by an apparatus such as Fig.13 The method 900 is performed by a communication device 1300 that includes various components that are operable, configured, or adapted to perform the method 900. The communication device 1300 is described in more detail below.

[0147] It should be noted that Fig. 9 This is merely one example of one method, and other methods are possible consistent with the present disclosure and including fewer, additional, or alternative steps.

[0148] Aspects related to UE-based machine learning (ML) data input

[0149] Aspects of the present disclosure provide apparatus, methods, processing systems, and computer-readable media for user equipment (UE) based machine learning (ML) data input configuration and reporting.

[0150] For example, the techniques proposed herein provide a signaling design for UE-based ML data input configuration and reporting. In one example, UE-based ML data signaled by the UE to a network entity can assist in the operation of an ML model at the network entity. In another example, UE-based ML training data signaled by the UE to a network entity can assist in labeling UE-based ML training data at the network entity. The signaling techniques proposed herein can assist in the operation and / or training of an ML model at a network entity.

[0151] The technology proposed in this article can be referenced Figures 10 to 12 To understand.

[0152] Fig.10 Describes a process flow for communication between a network entity and a UE. In some aspects, the network entity may be related to Figure 1 and Figure 3 BS102 as depicted and described or relating to Figure 2 An example of a decomposed BS is depicted and described. Similarly, a UE may be related to Figure 1 and Figure 3 An example of a UE 104 is depicted and described. However, in other aspects, the UE 104 may be another type of wireless communication device and the BS 102 may be another type of network entity or network node, such as those described herein.

[0153] At 1002, the UE sends UE capability information to a network entity.

[0154] At 1004, the network entity determines AI / ML data input. For example, the network entity may determine AI / ML data input based on UE capability information (eg, per MLFN, per ML model ID, or per MS ID).

[0155] At 1006, the network entity sends the AI / ML data input configuration to the UE. For example, the network entity may send the AI / ML data input report configuration to the UE per MLFN, per ML model ID, or per MS ID.

[0156] At 1008, the network entity sends an AI / ML data input activation or deactivation indication to the UE. The network entity may send the AI / ML data input activation or deactivation indication via downlink control information (DCI). In one example, the network entity may transmit an indication to activate sending AI / ML data input from the UE to the network entity based on one or more first requirements of the network entity. In another example, the network entity may transmit an indication to deactivate sending AI / ML data input from the UE to the network entity based on one or more second requirements of the network entity.

[0157] At 1010, the UE sends the AI / ML data input to the network entity. For example, the UE may send the AI / ML data input to the network entity according to the AI / ML data input reporting configuration. The network entity may use the received AI / ML data as input for operation and / or training of the ML model.

[0158] Fig.11 A method for use by a UE such as, Figure 1 and Figure 3 An example of a method 1100 for performing wireless communications with a UE 104).

[0159] Method 1100 begins at step 1105, i.e., sending UE capability information to a network entity. In some cases, the operation of this step refers to the following. Fig.13The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0160] Then, the method 1100 proceeds to step 1110, i.e., receiving from a network entity a configuration of at least one MLFN and a request for ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information. In some cases, the operation of this step refers to as described in reference Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0161] Then, the method 1100 proceeds to step 1115, that is, in response to the request, the ML data is sent to the network entity. In some cases, the operation of this step refers to the operation of Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0162] In some aspects, the configuration also indicates at least one of: an ML model ID or an MS ID.

[0163] In some aspects, the method 1100 further includes receiving an indication from the network entity via a MAC-CE or a DCI to activate or deactivate sending the ML data to the network entity. In some cases, the operation of this step refers to as described in reference Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0164] In one aspect, method 1100 or any aspect related thereto may be performed by an apparatus such as Fig.13 The method 1100 is performed by a communication device 1300 that includes various components operable to, configured to, or adapted to perform the method 1100. The communication device 1300 is described in more detail below.

[0165] It should be noted that Fig.11 This is merely one example of one method, and other methods are possible consistent with the present disclosure and including fewer, additional, or alternative steps.

[0166] Fig.12 A method for transmitting data to a network entity such as Figure 1 and Figure 3 BS102 or such Figure 2An example of a method 1200 for performing wireless communications in a decomposed BS) as discussed.

[0167] Method 1200 begins at step 1205, i.e., receiving UE capability information from the UE. In some cases, the operation of this step refers to the operation described in reference to Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0168] Then, the method 1200 proceeds to step 1210, i.e., sending to the UE a configuration of at least one MLFN and a request for ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information. In some cases, the operation of this step refers to as described in reference Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0169] Then, the method 1200 proceeds to step 1215, that is, receiving the ML data from the UE in response to the request. In some cases, the operation of this step refers to the operation of Fig.13 The described circuitry for receiving and / or code for receiving may be or may be performed by the circuitry for receiving and / or code for receiving.

[0170] In some aspects, the configuration also indicates at least one of: an ML model ID or an MS ID.

[0171] In some aspects, the method 1200 further includes sending an indication to the UE via a MAC-CE or a DCI to activate or deactivate sending the ML data to the network entity. In some cases, the operation of this step refers to the following steps. Fig.13 The described circuit system for sending and / or code for sending, or may be performed by the circuit system for sending and / or code for sending.

[0172] In some aspects, the method 1200 further includes determining the ML data to be reported based on the UE capability information. In some cases, the operation of this step refers to referring to Fig.13 The described circuitry for determining and / or code for determining may be performed by, or may be executed by, the circuitry for determining and / or the code for determining.

[0173] In one aspect, method 1200 or any aspect related thereto may be performed by an apparatus such as Fig.13The method 1200 is performed by a communication device 1300 that includes various components operable to, configured to, or adapted to perform the method 1200. The communication device 1300 is described in more detail below.

[0174] It should be noted that Fig.12 This is merely one example of one method, and other methods are possible consistent with the present disclosure and including fewer, additional, or alternative steps.

[0175] Example Communication Device

[0176] Fig.13 Aspects of an example communications device 1300 are depicted. In some aspects, communications device 1300 is a user equipment (UE), such as described above with respect to Figure 1 and Figure 3 UE 104 is described. In some aspects, communication device 1300 is a network entity such as Figure 1 and Figure 3 BS102 or such Figure 2 The decomposed BS in question.

[0177] The communication device 1300 includes a processing system 1305 coupled to a transceiver 1365 (e.g., a transmitter and / or a receiver). In some aspects (e.g., when the communication device 1300 is a network entity), the processing system 1305 can be coupled to a network interface 1375 that is configured to communicate with the communication device 1300 via a communication link (e.g., as described herein, such as with respect to Figure 2 The processing system 1305 may be configured to perform processing functions of the communication device 1300, including processing signals received by the communication device 1300 and / or signals to be transmitted by the communication device 1300, using the backhaul link, midhaul link, and / or fronthaul link described herein. The transceiver 1365 is configured to send and receive signals of the communication device 1300, such as the various signals described herein, via the antenna 1370. The processing system 1305 may be configured to perform processing functions of the communication device 1300, including processing signals received by the communication device 1300 and / or signals to be transmitted by the communication device.

[0178] The processing system 1305 includes one or more processors 1310. In various aspects, the one or more processors 1310 may represent one or more of the receive processor 358, the transmit processor 364, the TX MIMO processor 366, and / or the controller / processor 380, as described with respect to FIG. Figure 3 In various aspects, the one or more processors 1310 may represent one or more of the receive processor 338, the transmit processor 320, the TX MIMO processor 330, and / or the controller / processor 340, as described with respect to Figure 3The one or more processors 1310 are coupled to the computer readable medium / memory 1335 via the bus 1360. In some aspects, the computer readable medium / memory 1335 is configured to store instructions (e.g., computer executable code) that, when executed by the one or more processors 1310, cause the one or more processors 1310 to perform the following: Figure 8 The method 800 described herein or any aspect thereof; Fig. 9 Method 900 as described or any aspect thereof; Fig.11 The method 1100 described herein or any aspect thereof; and / or Fig.12 The described method 1200 or any aspect related thereto. It should be noted that references to a processor performing a function of the communication device 1300 may include one or more processors 1310 performing that function of the communication device 1300 .

[0179] In the depicted example, computer readable medium / memory 1335 stores code (e.g., executable instructions), such as code for receiving 1340, code for using 1345, code for sending 1350, and code for determining 1355. Processing of code for receiving 1340, code for using 1345, code for sending 1350, and code for determining 1355 may cause communication device 1300 to perform the following: Figure 8 The method 800 described herein or any aspect thereof; Fig. 9 Method 900 as described or any aspect thereof; Fig.11 The method 1100 described herein or any aspect thereof; and / or Fig.12 The described method 1200 or any aspect related thereto.

[0180] The one or more processors 1310 include circuit systems configured to implement (e.g., execute) code stored in a computer-readable medium / memory 1335, including circuit systems such as circuit systems for receiving 1315, circuit systems for using 1320, circuit systems for sending 1325, and circuit systems for determining 1330. The processing of the circuit systems for receiving 1315, the circuit systems for using 1320, the circuit systems for sending 1325, and the circuit systems for determining 1330 may cause the communication device 1300 to perform the following: Figure 8 The method 800 described herein or any aspect thereof; Fig. 9 Method 900 as described or any aspect thereof; Fig.11 The method 1100 described herein or any aspect thereof; and / or Fig.12 The described method 1200 or any aspect related thereto.

[0181] The various components of the communication device 1300 may provide means for performing the following: Figure 8 The method 800 described herein or any aspect thereof; Fig. 9 Method 900 as described or any aspect thereof; Fig.11 The method 1100 described herein or any aspect thereof; and / or Fig.12 The method 1200 described herein or any aspect related thereto. For example, a component for sending, transmitting, or outputting for sending may include Figure 3 The illustrated transceiver 354 and / or antenna 352 of the UE 104, Figure 3 The illustrated transceiver 332 and / or antenna 334 and / or Fig.13 The transceiver 1265 and antenna 1270 of the communication device 1300 in FIG. 1 may include Figure 3 The illustrated transceiver 354 and / or antenna 352 of the UE 104, Figure 3 The illustrated transceiver 332 and / or antenna 334 and / or Fig.13 The transceiver 1365 and antenna 1370 of the communication device 1300 in FIG.

[0182] Sample Clauses

[0183] Specific implementation examples are described in the following numbered clauses:

[0184] Clause 1: A method for wireless communications by a UE, the method comprising: receiving a configuration for at least one MLFN; receiving ML data associated with the at least one MLFN; and using the ML data as input to at least one of: operation or training of an ML model associated with the at least one MLFN.

[0185] Clause 2: A method according to clause 1, wherein the operation of the ML model includes at least one of: running the ML model, switching the ML model, or monitoring the ML model; and the training of the ML model includes: marking ML data for specific UE and network entity settings or configurations, and performing the training of the ML model using the marked ML data.

[0186] Clause 3: A method according to any one of clauses 1 and 2, wherein the ML data comprises model management data and training data; the model management data indicates at least one of: the number of antennas or the number of retransmissions; and the training data indicates at least one of: network load, event threshold or layer 1 (L1) and layer 2 (L2) measurements.

[0187] Clause 4: A method according to any of clauses 1 to 3, wherein the ML data is applicable to one or more other UEs in the network entity coverage or target area, and the ML data indicates at least one of: an ML model ID or an MS ID together with the ML data itself.

[0188] Clause 5: The method of clause 4, wherein the ML data is received via at least one of: a SIB or an MBS on an MBS channel.

[0189] Clause 6: A method according to any of clauses 1 to 5, wherein the ML data is received via a unicast message when at least one of the following conditions is met: the UE is connected to a network entity; or the UE is configured with at least one of the following: the at least one MLFN, ML model ID or MS ID.

[0190] Clause 7: The method of clause 6, further comprising: sending an indication to the network entity using MAC-CE, UAI or UCI to activate or deactivate sending the ML data to the UE.

[0191] Clause 8: A method as set forth in any of clauses 1 to 7, further comprising sending a request for the ML data to a network entity, wherein the ML data is received by the UE in response to the request.

[0192] Clause 9: The method of clause 8, wherein the sending comprises sending the request via at least one of: a UAI or a subscription request.

[0193] Clause 10: A method according to clause 8, wherein the request indicates at least one of: the at least one MLFN; the ML model ID; the MS ID; geographic information, the geographic information comprising at least one of: the current geographic area, PLMN, cell information or frequency list of the UE; a validity time, the validity time comprising at least one of: a duration or interval time during which the ML data must be provided to the UE; one or more network configurations or settings; or a type of the ML data, the type of the ML data comprising at least one of: metadata, training data or inference data.

[0194] Clause 11: The method of clause 8, further comprising: sending an indication to the network entity using MAC-CE, UAI or UCI to activate or deactivate sending the ML data to the UE.

[0195] Clause 12: A method for wireless communications by a network entity, the method comprising: sending a configuration for at least one MLFN to at least one UE; determining ML data associated with the at least one MLFN, the ML data to be used as input to at least one of: operation or training of an ML model associated with the at least one MLFN; and sending the ML data to the at least one UE.

[0196] Clause 13: A method according to clause 12, wherein the operation of the ML model includes at least one of: running the ML model, switching the ML model or monitoring the ML model; the training of the ML model includes marking the ML data for specific UE and network entity settings or configurations, and performing the training of the ML model using the marked ML data; the ML data indicates model management data and training data; the model management data indicates at least one of the following: the number of antennas or the number of retransmissions; and the training data indicates at least one of the following: network UE load, event thresholds or layer 1 (L1) and layer 2 (L2) measurements.

[0197] Clause 14: A method according to any of clauses 12 and 13, wherein the at least one UE corresponds to a first UE and a second UE; and the first UE and the second UE require the same ML data input or control associated with at least one of the following: at least one MLFN, ML Model ID or MS ID.

[0198] Clause 15: The method of clause 14, wherein the ML data is sent to the first UE and the second UE via at least one of: a SIB or an MBS.

[0199] Clause 16: A method as set forth in any one of clauses 12 to 15, wherein the at least one UE corresponds to a first UE; and the ML data is sent to the first UE via a unicast message.

[0200] Clause 17: The method of clause 16, further comprising: receiving an indication from the first UE using MAC-CE, UAI or UCI to activate or deactivate sending the ML data to the UE.

[0201] Clause 18: A method as described in any of clauses 12 to 17, wherein the at least one UE corresponds to a first UE; a request for the ML data is received from the first UE, wherein the ML data is sent to the first UE in response to the request.

[0202] Clause 19: The method of clause 18, wherein the request is received via at least one of: a UAI or a subscription request.

[0203] Clause 20: A method according to clause 18, wherein the request indicates at least one of: the at least one MLFN; the ML model ID; the MS ID; geographic information, the geographic information comprising at least one of: the current geographic area, PLMN, cell information or frequency list of the first UE; a validity time, the validity time comprising at least one of: a duration or interval time during which the ML data must be provided to the first UE; one or more network configurations; or a type of the ML data, the type of the ML data comprising at least one of: metadata, training data or inference data.

[0204] Clause 21: The method of clause 18, further comprising: receiving an indication from the first UE to activate or deactivate sending the ML data to the first UE.

[0205] Clause 22: The method of clause 18, further comprising: sending the request to another network entity when one or more conditions are met.

[0206] Clause 23: A method according to clause 22, wherein the request is sent during a handover from one network entity to another network entity; and the one or more conditions are met when the first UE moves from a connected state to an idle state or an inactive state or vice versa.

[0207] Clause 24: A method for wireless communication by a UE, the method comprising: sending UE capability information to a network entity; receiving from the network entity a configuration of at least one MLFN and a request for ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; and sending the ML data to the network entity in response to the request.

[0208] Clause 25: The method of clause 24, wherein the configuration further indicates at least one of: an ML model ID or an MS ID.

[0209] Clause 26: A method as described in any of clauses 24 to 25, further comprising: receiving an indication from the network entity via MAC-CE or DCI to activate or deactivate sending the ML data to the network entity.

[0210] Clause 27: A method for wireless communications by a network entity, the method comprising: receiving UE capability information from a UE; sending to the UE a configuration of at least one MLFN and a request for ML data associated with the at least one MLFN, the ML data to be used as input for at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; and receiving the ML data from the UE in response to the request.

[0211] Clause 28: The method of clause 27, wherein the configuration further indicates at least one of: an ML model ID or an MS ID.

[0212] Clause 29: A method as described in any of clauses 27 to 28, the method further comprising: sending an indication to the UE via MAC-CE or DCI to activate or deactivate sending the ML data to the network entity.

[0213] Clause 30: A method as described in any of clauses 27 to 29, the method further comprising: determining the ML data to be reported based on the UE capability information.

[0214] Clause 31: An apparatus, the apparatus comprising: a memory including executable instructions; and a processor configured to execute the executable instructions and cause the apparatus to perform the method according to any one of clauses 1 to 30.

[0215] Clause 32: An apparatus comprising means for performing the method of any of clauses 1 to 30.

[0216] Clause 33: A non-transitory computer readable medium comprising executable instructions which, when executed by a processor of an apparatus, cause the apparatus to perform the method of any of clauses 1 to 30.

[0217] Clause 34: A computer program product embodied on a computer readable storage medium, the computer readable storage medium comprising code for performing the method according to any of clauses 1 to 30.

[0218] Additional considerations

[0219] The foregoing description is provided to enable any person skilled in the art to practice the various aspects described herein. The examples discussed herein do not limit the scope, applicability, or aspects set forth in the claims. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. For example, without departing from the scope of the present disclosure, the functions and arrangements of the elements discussed may be changed. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different from that described, and various actions may be added, omitted, or combined. In addition, the features described with respect to some examples may be combined in some other examples. 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 practiced using other structures, functionality, or structures and functionality other than the various aspects of the present disclosure set forth herein or different from the various aspects of the present disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of the claims.

[0220] The various illustrative logical blocks, modules, and circuits described in conjunction with the present disclosure may be implemented or executed using a general purpose processor, a digital signal processor (DSP), an ASIC, a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, a system on a chip (SoC), or any other such configuration.

[0221] As used herein, a phrase referring to "at least one of" a list of items refers to any combination of those items (including single members). As an example, "at least one of a, b, or c" is intended to cover: a, b, c, ab, ac, bc, and abc, as well as any combination with multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other ordering of a, b, and c).

[0222] As used herein, the term "determining" encompasses a wide variety of actions. For example, "determining" may include calculating, computing, processing, deriving, investigating, searching (e.g., searching in a table, a database, or another data structure), ascertaining, and the like. Also, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), and the like. Additionally, "determining" may include resolving, selecting, choosing, establishing, and the like.

[0223] The method disclosed herein includes one or more actions for implementing the method. The method actions may be interchangeable with each other without departing from the scope of the claims. In other words, unless a specific order of actions is specified, the order and / or use of specific actions may be modified without departing from the scope of the claims. In addition, the various operations of the method described above may be performed by any suitable component capable of performing the corresponding function. The component may include various hardware and / or software components and / or modules, including but not limited to circuits, application specific integrated circuits (ASICs) or processors.

[0224] The following claims are not intended to be limited to the various aspects shown herein, but should be given the full scope consistent with the language of the claims. Within the claims, unless otherwise specified, reference to an element in the singular form is not intended to mean "one and only one", but "one or more". Unless otherwise specified, the term "some" refers to one or more. Any claim element is not interpreted according to the provisions of 35 U.S.C. § 112 (f) unless the element is explicitly stated using the phrase "parts for...". All structural and functional equivalents of the elements of the various aspects described throughout this disclosure that are known or will be known later to a person of ordinary skill in the art are expressly incorporated herein by reference and are intended to be covered by the claims. In addition, regardless of whether such disclosure is explicitly stated in the claims, anything disclosed herein is not intended to be disclosed to the public.

Claims

1. A user equipment (UE) configured for wireless communication, the user equipment (UE) comprising: a memory including computer executable instructions; and a processor configured to execute the computer executable instructions and cause the UE to: receiving a configuration for at least one machine learning function name (MLFN); receiving machine learning (ML) data associated with the at least one MLFN; and The ML data is used as input to at least one of: operation or training of an ML model associated with the at least one MLFN.

2. The UE according to claim 1, wherein: The operation of the ML model includes at least one of: running the ML model, switching the ML model, or monitoring the ML model; and The training of the ML model includes labeling the ML data for specific UE and network entity settings or configurations, and performing the training of the ML model using the labeled ML data.

3. The UE according to claim 1, wherein: The ML data includes model management data and training data; The model management data indicates at least one of: the number of antennas or the number of retransmissions; and The training data indicates at least one of: network load, event thresholds, or layer 1 (L1) and layer 2 (L2) measurements.

4. The UE according to claim 1, wherein: The ML data is applicable to one or more other UEs in the coverage or target area of ​​the network entity; and The ML data indicates at least one of: an ML model identification (ID) or a model structure (MS) ID together with the ML data itself.

5. The UE of claim 4, wherein the ML data is received via at least one of: a system information block (SIB) or a multicast broadcast service (MBS) on an MBS channel.

6. The UE of claim 1, wherein the ML data is received via a unicast message when at least one of the following conditions is met: The UE is connected to a network entity; or The UE is configured with at least one of: the at least one MLFN, an ML model identification (ID), or a model structure (MS) ID.

7. The UE of claim 6, wherein the processor is further configured to execute the computer-executable instructions and cause the UE to: send an indication to the network entity to activate or deactivate deactivation of sending the ML data to the UE using a medium access control (MAC) control element (CE), UE assistance information (UAI), or uplink control information (UCI).

8. The UE of claim 1, wherein the processor is further configured to execute the computer executable instructions and cause the UE to: A request for the ML data is sent to a network entity, wherein the ML data is received by the UE in response to the request.

9. The UE of claim 8, wherein the sending comprises sending the request via at least one of: UE Assistance Information (UAI) or a subscription request.

10. The UE of claim 8, wherein the request indicates at least one of: the at least one MLFN; ML model identification (ID); Model structure (MS) ID; Geographic information, the geographic information comprising at least one of: a current geographic area of ​​the UE, a public land mobile network (PLMN), cell information, or a frequency list; A validity time, the validity time comprising at least one of the following: a duration or an interval time during which the ML data must be provided to the UE; one or more network configurations or settings; or A type of the ML data, the type of the ML data comprising at least one of: metadata, training data, or inference data.

11. The UE of claim 8, wherein the processor is further configured to execute the computer-executable instructions and cause the UE to: send an indication to the network entity to activate or deactivate sending the ML data to the UE using a medium access control (MAC) control element (CE), UE assistance information (UAI), or uplink control information (UCI).

12. A network entity configured for wireless communication, the network entity comprising: a memory including computer executable instructions; and a processor configured to execute the computer executable instructions and cause the network entity to: sending configuration of at least one machine learning function name (MLFN) to at least one user equipment (UE); determining machine learning (ML) data associated with the at least one MLFN, the machine learning (ML) data to be used as input to at least one of: operation or training of an ML model associated with the at least one MLFN; and The ML data is sent to the at least one UE.

13. The network entity according to claim 12, wherein: The operation of the ML model includes at least one of: running the ML model, switching the ML model, or monitoring the ML model; the training of the ML model includes labeling the ML data for specific UE and network entity settings or configurations, and performing the training of the ML model using the labeled ML data; The ML data indicates model management data and training data; The model management data indicates at least one of: the number of antennas or the number of retransmissions; and The training data indicates at least one of: network UE load, event thresholds, or layer 1 (L1) and layer 2 (L2) measurements.

14. The network entity according to claim 12, wherein: The at least one UE corresponds to a first UE and a second UE; and The first UE and the second UE require the same ML data input or control associated with at least one of: the at least one MLFN, ML model identification (ID), or model structure (MS) ID.

15. The network entity of claim 14, wherein the ML data is sent to the first UE and the second UE via at least one of: a system information block (SIB) or a multicast broadcast service (MBS).

16. The network entity according to claim 12, wherein: The at least one UE corresponds to a first UE; and The ML data is sent to the first UE via a unicast message.

17. The network entity of claim 16, wherein the processor is further configured to execute the computer-executable instructions and cause the network entity to: receive an indication from the first UE to activate or deactivate sending the ML data to the first UE using a medium access control (MAC) control element (CE), UE assistance information (UAI), or uplink control information (UCI).

18. The network entity according to claim 12, wherein: The at least one UE corresponds to a first UE; The processor is further configured to execute the computer-executable instructions and cause the network entity to: receive a request for the ML data from the first UE, wherein the ML data is sent to the first UE in response to the request.

19. The network entity of claim 18, wherein the request is received via at least one of: UE Assistance Information (UAI) or a subscription request.

20. The network entity of claim 18, wherein the request indicates at least one of: the at least one MLFN; ML model identification (ID); Model structure (MS) ID; Geographic information, the geographic information comprising at least one of the following: a current geographic area of ​​the first UE, a public land mobile network (PLMN), cell information, or a frequency list; A validity time, the validity time comprising at least one of the following: a duration or an interval time during which the ML data must be provided to the first UE; one or more network configurations; or A type of the ML data, the type of the ML data comprising at least one of: metadata, training data, or inference data.

21. The network entity of claim 18, wherein the processor is further configured to execute the computer-executable instructions and cause the network entity to: receive an indication from the first UE to activate or deactivate sending the ML data to the first UE.

22. The network entity of claim 18, wherein the processor is further configured to execute the computer-executable instructions and cause the network entity to: send the request to another network entity when one or more conditions are met.

23. The network entity according to claim 22, wherein: sending the request during a handover from one network entity to another network entity; and The one or more conditions are met when the first UE moves from a connected state to an idle state or an inactive state or vice versa.

24. A user equipment (UE) configured for wireless communication, the user equipment (UE) comprising: a memory including computer executable instructions; and a processor configured to execute the computer executable instructions and cause the UE to: Sending UE capability information to a network entity; receiving, from the network entity, a configuration of at least one machine learning function name (MLFN) and a request for machine learning (ML) data associated with the at least one MLFN, the machine learning (ML) data to be used as input to at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; as well as In response to the request, the ML data is sent to the network entity.

25. The UE of claim 24, wherein the configuration further indicates at least one of: an ML model identification (ID) or a model structure (MS) ID.

26. The UE of claim 24, wherein the processor is further configured to execute the computer-executable instructions and cause the UE to: receive an indication from the network entity via a medium access control (MAC) control element (CE) or downlink control information (DCI) to activate or deactivate sending the ML data to the network entity.

27. A network entity configured for wireless communication, the network entity comprising: a memory including computer executable instructions; and a processor configured to execute the computer executable instructions and cause the network entity to: receiving user equipment (UE) capability information from the UE; sending, to the UE, a configuration of at least one machine learning function name (MLFN) and a request for machine learning (ML) data associated with the at least one MLFN, the machine learning (ML) data to be used as input to at least one of: operation or training of an ML model associated with the at least one MLFN, wherein the configuration and the request are based on the UE capability information; as well as In response to the request, the ML data is received from the UE.

28. The network entity of claim 27, wherein the configuration further indicates at least one of: a ML model identification (ID) or a model structure (MS) ID.

29. The network entity of claim 27, wherein the processor is further configured to execute the computer-executable instructions and cause the network entity to: send an indication to the UE via a medium access control (MAC) control element (CE) or downlink control information (DCI) to activate or deactivate sending the ML data to the network entity.

30. The network entity of claim 27, wherein the processor is further configured to execute the computer-executable instructions and cause the network entity to: determine the ML data to be reported based on the UE capability information.

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