Lifecycle management of AI / ML models in wireless communication systems

Abstract

Apparatus, systems, and methods for lifecycle management of AI / ML models in a wireless communication system. Comprising at least one antenna and a processor is configured to: receive a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and sending an AI-based feedback message generated based on the AI model to the network device.

Description

Technical Field

[0001] The present application as a whole relates to wireless communication systems, including apparatus, systems and methods for lifecycle management of AI / ML models in wireless communication systems. Background Art

[0002] Wireless mobile communication technologies use various standards and protocols to send data between base stations and wireless communication devices. Wireless communication system standards and protocols may include, for example, the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G), 3GPP New Radio (NR) (e.g., 5G), and the IEEE 802.11 standard for wireless local area networks (WLANs) (commonly referred to within industry organizations as WLANs). ).

[0003] As envisioned by 3GPP, different wireless communication system standards and protocols may use various radio access networks (RANs) to communicate between base stations of the RAN (which may also sometimes be referred to as RAN nodes, network nodes, or simply nodes) and wireless communication devices referred to as user equipment (UE). 3GPP RANs may include, for example, Global System for Mobile Communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE) RAN (GERAN), Universal Terrestrial Radio Access Network (UTRAN), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), and / or Next Generation Radio Access Network (NG-RAN).

[0004] Each RAN may use one or more radio access technologies (RATs) to perform communications between base stations and UEs. For example, GERAN implements GSM and / or EDGE RAT, UTRAN implements Universal Mobile Telecommunications System (UMTS) RAT or other 3GPP RAT, E-UTRAN implements LTE RAT (sometimes referred to as LTE), and NG-RAN implements NR RAT (sometimes referred to as 5G RAT, 5G NR RAT, or NR in this document). In some deployments, E-UTRAN may also implement NR RAT. In some deployments, NG-RAN may also implement LTE RAT.

[0005] The base stations used by the RAN may correspond to the RAN. An example of an E-UTRAN base station is an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (also commonly denoted as an evolved Node B, enhanced Node B, eNodeB, or eNB). An example of an NG-RAN base station is a Next Generation Node B (also sometimes referred to as a gNodeB or gNB).

[0006] The RAN provides its communication services with external entities through its connection to the Core Network (CN). For example, E-UTRAN may utilize the Evolved Packet Core (EPC) while NG-RAN may utilize the 5G Core Network (5GC).

[0007] Artificial intelligence (AI) is the simulation of human intelligence processes by machines, usually computer systems. Machine learning (ML) is a subset of AI that creates algorithms and statistical models to perform specific tasks without explicit instructions, but instead relies on patterns and reasoning. ML algorithms build mathematical models based on sample data, called training data, to make predictions or decisions without being specifically programmed for the task. Learned signal processing algorithms can support next-generation wireless systems with significantly reduced power consumption and improved density, throughput, and accuracy when compared to today's fragile, manually designed systems. Summary of the invention

[0008] The present disclosure provides apparatus, systems, and methods for lifecycle management of AI / ML models in wireless communication systems.

[0009] Embodiments disclosed herein include a wireless device comprising: at least one antenna; and a processor; wherein the wireless device is configured to: receive a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and send an AI-based feedback message generated based on the AI ​​model to the network device.

[0010] Embodiments disclosed herein include a network device comprising: at least one antenna; and a processor; wherein the network device is configured to: send a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receive an AI-based feedback message generated based on the AI ​​model from the wireless device.

[0011] Embodiments disclosed herein include a method performed by a wireless device, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and sending an AI-based message generated based on the AI ​​model to the network device.

[0012] Embodiments disclosed herein include a method performed by a network device, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based message generated based on the AI ​​model from the wireless device.

[0013] Embodiments disclosed herein include a non-transitory computer-readable storage medium having instructions stored thereon, which instructions, when executed by a processor, cause a wireless device to: receive a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and send an AI-based message generated based on the AI ​​model to the network device.

[0014] Embodiments disclosed herein include a non-transitory computer-readable storage medium having instructions stored thereon, which instructions, when executed by a processor, cause a network device to: send a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receive an AI-based message generated based on the AI ​​model from the wireless device. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To easily identify the discussion of any particular element or action, the most significant digit(s) in a reference number refers to the drawing number that first introduces the element.

[0016] Figure 1 An example architecture of a wireless communication system according to embodiments disclosed herein is illustrated.

[0017] Figure 2 A system for performing signaling between a wireless device and a network device according to embodiments disclosed herein is illustrated.

[0018] Figure 3 An example functional framework of AI / ML in a wireless communication system according to the embodiments disclosed herein is illustrated.

[0019] Figure 4 An example communication process between a UE and a gNB for model training according to an embodiment disclosed herein is illustrated.

[0020] Figure 5 An example information structure of the NZP-CSI-RS-ResourceSet information element of the RRC configuration message is illustrated.

[0021] Figure 6 An example communication process between a UE and a gNB for model selection according to an embodiment disclosed herein is illustrated.

[0022] Figure 7 An example communication process between a UE and a gNB for model configuration, activation / deactivation, and switching according to the embodiments disclosed herein is illustrated.

[0023] Figure 8An example information structure of the reportQuantity parameter of the CSI-ReportConfig information element of the RRC configuration message is illustrated.

[0024] Fig. 9 An example method for lifecycle management of a model performed by a wireless device according to embodiments disclosed herein is illustrated.

[0025] Fig.10 An example method for lifecycle management of a model performed by a network device according to embodiments disclosed herein is illustrated. DETAILED DESCRIPTION

[0026] Various embodiments are described with respect to UE. However, reference to UE is provided for illustrative purposes only. The example embodiments may be used with any wireless device that can establish a connection with a network and is configured with hardware, software and / or firmware for exchanging information and data with the network. Therefore, the UE described herein is used to represent any suitable network-wired device.

[0027] Various embodiments are described with respect to gNBs. However, references to gNBs are provided for illustrative purposes only. The example embodiments may be used with any network device in a network and configured with hardware, software, and / or firmware to implement any functionality of the network. Thus, gNB as described herein is used to represent any suitable network device.

[0028] Figure 1 An example architecture of a wireless communication system 100 according to an embodiment disclosed herein is illustrated. The description provided below is for an example wireless communication system 100 operating in conjunction with the LTE system standard and / or the 5G or NR system standard provided in the 3GPP technical specifications.

[0029] like Figure 1 As shown, wireless communication system 100 includes UE 102 and UE 104 (although any number of UEs may be used). In this example, UE 102 and UE 104 are illustrated as smartphones (e.g., handheld touch screen mobile computing devices capable of connecting to one or more cellular networks), but may also include any mobile or non-mobile computing device configured for wireless communication.

[0030] UE 102 and UE 104 may be configured to be communicatively coupled to RAN 106. In an embodiment, RAN 106 may be NG-RAN, E-UTRAN, etc. UE 102 and UE 104 utilize connections (or channels) (shown as connection 108 and connection 110, respectively) with RAN 106, where each connection (or channel) includes a physical communication interface. RAN 106 may include one or more base stations, such as base station 112 and base station 114, to implement connection 108 and connection 110.

[0031] In this example, connection 108 and connection 110 are air interfaces that enable such communicative coupling and may conform to the RAT used by RAN 106 , such as, for example, LTE and / or NR.

[0032] In some embodiments, UE 102 and UE 104 may also directly exchange communication data via side link interface 116. UE 104 is shown as being configured to access an access point (shown as AP 118) via connection 120. By way of example, connection 120 may include a local wireless connection, such as a connection conforming to any IEEE 802.11 protocol, wherein AP 118 may include In this example, AP 118 may be connected to another network (eg, the Internet) without going through CN 124.

[0033] In an embodiment, UE 102 and UE 104 may be configured to communicate with each other or with base station 112 and / or base station 114 over a multi-carrier communication channel using orthogonal frequency division multiplexing (OFDM) communication signals according to various communication techniques, such as but not limited to orthogonal frequency division multiple access (OFDMA) communication techniques (e.g., for downlink communication) or single carrier frequency division multiple access (SC-FDMA) communication techniques (e.g., for uplink and ProSe or sidelink communication), although the scope of the embodiment is not limited in this respect. The OFDM signal may include multiple orthogonal subcarriers.

[0034] In some embodiments, all or part of base station 112 or base station 114 may be implemented as one or more software entities running on a server computer as part of a virtual network. In addition, or in other embodiments, base station 112 or base station 114 may be configured to communicate with each other via interface 122. In an embodiment where wireless communication system 100 is an LTE system (e.g., when CN124 is EPC), interface 122 may be an X2 interface. The X2 interface may be defined between two or more base stations (e.g., two or more eNBs, etc.) connected to EPC and / or between two eNBs connected to EPC. In an embodiment where wireless communication system 100 is an NR system (e.g., when CN 124 is 5GC), interface 122 may be an Xn interface. The Xn interface is defined between two or more base stations (e.g., two or more gNBs, etc.) connected to 5GC, between base station 112 (e.g., gNB) and eNB connected to 5GC, and / or between two eNBs connected to 5GC (e.g., CN 124).

[0035] The RAN 106 is shown as being communicatively coupled to the CN 124. The CN 124 may include one or more network elements 126 configured to provide various data and telecommunication services to customers / subscribers (e.g., UE 102 and users of UE 104) connected to the CN 124 via the RAN 106. The components of the CN 124 may be implemented in one physical device or separate physical devices including components for reading and executing instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium).

[0036] In an embodiment, CN 124 may be an EPC, and RAN 106 may be connected to CN 124 via an S1 interface 128. In an embodiment, S1 interface 128 may be divided into two parts: an S1 user plane (S1-U) interface that carries traffic data between base station 112 or base station 114 and a serving gateway (S-GW); and an S1-MME interface that is a signaling interface between base station 112 or base station 114 and a mobility management entity (MME).

[0037] In an embodiment, CN 124 may be a 5GC, and RAN 106 may be connected to CN 124 via an NG interface 128. In an embodiment, NG interface 128 may be divided into two parts: an NG user plane (NG-U) interface that carries service data between base station 112 or base station 114 and a user plane function (UPF); and an S1 control plane (NG-C) interface that is a signaling interface between base station 112 or base station 114 and an access and mobility management function (AMF).

[0038] Generally speaking, application server 130 may be an element that provides applications (e.g., packet-switched data services) that use Internet Protocol (IP) bearer resources with CN 124. Application server 130 may also be configured to support one or more communication services (e.g., VoIP sessions, group communication sessions, etc.) for UE 102 and UE 104 via CN 124. Application server 130 may communicate with CN 124 via IP communication interface 132.

[0039] Figure 2 A system 200 for performing signaling 234 between a wireless device 202 and a network device 218 according to an embodiment disclosed herein is illustrated. The system 200 may be part of a wireless communication system as described herein. The wireless device 202 may be, for example, a UE of a wireless communication system. The network device 218 may be, for example, a base station (e.g., an eNB or a gNB) of a wireless communication system.

[0040] The wireless device 202 may include one or more processors 204. The processor 204 may execute instructions to perform various operations of the wireless device 202, as described herein. The processor 204 may include one or more baseband processors implemented using, for example, a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0041] The wireless device 202 may include a memory 206. The memory 206 may be a non-transitory computer-readable storage medium that stores instructions 208 (which may include, for example, instructions executed by the processor 204). The instructions 208 may also be referred to as program code or a computer program. The memory 206 may also store data used by the processor 204 and results calculated by the processor.

[0042] The wireless device 202 may include one or more transceivers 210 that may use radio frequency (RF) transmitter and / or receiver circuitry that uses an antenna 212 of the wireless device 202 to facilitate signaling (e.g., signaling 234) to and / or from the wireless device 202 with other devices (e.g., network device 218) according to a corresponding RAT.

[0043] The wireless device 202 may include one or more antennas 212 (e.g., one, two, four, or more). For embodiments with multiple antennas 212, the wireless device 202 may utilize the spatial diversity of such multiple antennas 212 to transmit and / or receive multiple different data streams on the same time-frequency resources. This behavior may be referred to as, for example, multiple-input multiple-output (MIMO) behavior (referring to the multiple antennas used at each of the transmitting device and the receiving device to implement this aspect). MIMO transmission by the wireless device 202 may be implemented based on precoding (or digital beamforming) applied at the wireless device 202, which multiplexes the data streams across the antennas 212 based on known or assumed channel characteristics, so that each data stream is received at an appropriate signal strength relative to the other streams and at a desired location in the spatial domain (e.g., the location of the receiver associated with the data stream). Certain embodiments may use a single-user MIMO (SU-MIMO) approach (where the data streams are all directed to a single receiver) and / or a multi-user MIMO (MU-MIMO) approach (where separate data streams may be directed to separate (different) receivers in different locations in the spatial domain).

[0044] In certain embodiments with multiple antennas, the wireless device 202 may implement analog beamforming techniques whereby the phases of signals transmitted by the antennas 212 are relatively adjusted so that the (joint) transmissions of the antennas 212 can be directed (this is sometimes referred to as beam steering).

[0045] The wireless device 202 may include one or more interfaces 214. The interfaces 214 may be used to provide input to the wireless device 202 or output from the wireless device. For example, a wireless device 202 that is a UE may include an interface 214, such as a microphone, a speaker, a touch screen, buttons, etc., to allow a user of the UE to input and / or output to the UE. Other interfaces of such a UE may be composed of transmitters, receivers, and other circuits that allow communication between the UE and other devices (e.g., in addition to the transceiver 210 / antenna 212 described above), and may be based on known protocols (e.g., etc.) to perform the operation.

[0046] The network device 218 may include one or more processors 220. The processor 220 may execute instructions to perform various operations of the network device 218, as described herein. The processor 204 may include one or more baseband processors implemented using, for example, a CPU, a DSP, an ASIC, a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[0047] The network device 218 may include a memory 222. The memory 222 may be a non-transitory computer-readable storage medium that stores instructions 224 (which may include, for example, instructions executed by the processor 220). The instructions 224 may also be referred to as program code or a computer program. The memory 222 may also store data used by the processor 220 and results calculated by the processor.

[0048] The network device 218 may include one or more transceivers 226, which may include RF transmitter and / or receiver circuits that use an antenna 228 of the network device 218 to facilitate signaling (e.g., signaling 234) to and / or from the network device 218 with other devices (e.g., wireless device 202) according to a corresponding RAT.

[0049] The network device 218 may include one or more antennas 228 (eg, one, two, four, or more). In embodiments with multiple antennas 228, the network device 218 may perform MIMO, digital beamforming, analog beamforming, beamsteering, etc. as described.

[0050] The network device 218 may include one or more interfaces 230. The interface 230 may be used to provide input to or output from the network device 218. For example, the network device 218 as a base station may include an interface 230 composed of a transmitter, a receiver, and other circuits (e.g., in addition to the transceiver 226 / antenna 228 described above), which enables the base station to communicate with other equipment in the core network and / or enables the base station to communicate with external networks, computers, databases, etc., in order to achieve the purpose of performing operations, management, and maintenance of the base station or other equipment operably connected to the base station.

[0051] AI / ML can be applied to wireless communication systems. Use cases include channel state information (CSI) feedback enhancement (e.g., overhead reduction, improved accuracy and prediction), beam management (BM) (e.g., beam prediction in time, spatial domain for overhead and latency reduction, and beam selection accuracy improvement), and positioning accuracy enhancement for different scenarios (including, for example, those with severe NLOS conditions).

[0052] The embodiments contemplated herein provide lifecycle management of AI / ML models in wireless communication systems. The lifecycle management of the AI / ML models includes, for example, model training, model deployment, model reasoning (activation / deactivation / switching of AI / ML models), model monitoring, and model updates.

[0053] Figure 3An example functional framework of AI / ML in a wireless communication system according to the embodiments disclosed herein is illustrated.

[0054] Data collection 302 is a function that provides input data to the model training and model reasoning functions. No AI / ML algorithm specific data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) is performed in the data collection function. Examples of input data may include measurements from UEs or different network entities, feedback from actors, outputs from AI / ML models. Training data is data required as input to the AI / ML model training function. Reasoning data is data required as input to the AI / ML model reasoning function.

[0055] Model training 304 is a function that performs AI / ML model training, validation, and testing, which can generate model performance metrics as part of the model testing process. The model training function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the training data delivered by the data collection function, if necessary. Model deployment / update is the initial deployment of the trained, validated, and tested AI / ML model to the model reasoning function or the delivery of an updated model to the model reasoning function.

[0056] Model reasoning 306 is a function that provides AI / ML model reasoning output (e.g., prediction or decision). When applicable, the model reasoning function can provide model performance feedback to the model training function. If necessary, the model reasoning function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the reasoning data delivered by the data collection function. The output is the reasoning output of the AI / ML model produced by the model reasoning function. The details of the reasoning output are use case specific. When available, model performance feedback can be used to monitor the performance of the AI / ML model.

[0057] The actor 308 is a function that receives output from the model reasoning function and triggers or executes the corresponding action. The actor can trigger actions for other entities or for itself. Feedback is the information needed to derive training data, reasoning data, or monitor the performance of the AI / ML model and its impact on the network by updating KPIs and performance counters.

[0058] Performance monitoring 310 is a function for monitoring the performance of the AI / ML model. The performance monitoring function may receive an activation signal indicating activation of the AI / ML model from the model interference function. The performance monitoring function may also provide a switching / deactivation signal to the model interference function to switch / deactivate the AI / ML model.

[0059] An example communication process will be described below to illustrate various aspects of the present disclosure. The example communication process is described with respect to AI. However, reference to AI is provided for illustrative purposes only and may be replaced with ML.

[0060] Figure 4 An example communication process between a UE and a gNB for model training according to an embodiment disclosed herein is illustrated. In step S402, the gNB sends training assistance information to the UE to assist in model training. In step S404, the UE performs data collection according to the training assistance information received from the gNB. The UE may also send the collected data to the UE provider for model training. Alternatively, the UE may perform model training by itself.

[0061] Many AI models can be applied in wireless communication networks. The specific AI model to be used may depend on, for example, network deployment, use case, etc. In addition, even if the same AI model is used, different weights may be required. Therefore, it is necessary to classify the collected data into different AI training sets so that different weights can be derived from model training.

[0062] The training assistance information sent by the gNB may include, for example, an AI training set ID, which indicates the AI ​​training set to which the data collected by the UE should be added. The UE adds the collected data to the AI ​​training set indicated by the AI ​​training set ID in the training assistance information.

[0063] The AI ​​training set ID may be included in an RRC configuration message sent from the gNB to the UE. The AI ​​training set ID may be associated with a resource set ID indicating a resource set on which measurements are performed to collect data.

[0064] For a two-sided model with CSI compression, an AI training set ID may be added to the CSI-RS configuration from the network to assist UE data collection. For example, CSI-RS set configurations 1, 2, 3 may correspond to AI training set 1. This corresponds to the type of antenna to port mapping. The UE may include channel measurements based on the CSI-RS used for training model weight 1. For another example, CSI-RS set configurations 4, 5, 6 may correspond to AI training set 2. This corresponds to CoMP deployment. The UE may include channel measurements based on the CSI-RS used for training model weight 2. When the UE moves between different cells, the CSI-RS configuration associated with the same AI set ID will assist the UE in generating data sets for different network configurations / deployments.

[0065] Figure 5An example information structure of the NZP-CSI-RS-ResourceSet information element of the RRC configuration message is illustrated. The NZP-CSI-RS-ResourceSet information element includes a parameter indicating cmrGroupingForAItraining of an AI training set.

[0066] Figure 6 An example communication process between a UE and a gNB for model determination according to an embodiment disclosed herein is illustrated. In step S602, the UE sends capability information to the gNB. The capability information may indicate use cases supported by the UE. In step S604, the gNB determines an AI model that can be used by the UE under the supported use cases. In some instances, the gNB may retrieve a model ID corresponding to the supported use case from a database in the network. In some instances, the capability information may indicate a model ID corresponding to the supported use case. Therefore, the gNB may retrieve the model ID directly from the capability information.

[0067] Figure 7 An example communication process between a UE and a gNB for model configuration, activation / deactivation, and switching according to an embodiment disclosed herein is illustrated. In step S702, the gNB sends an RRC configuration message for model configuration to the UE. The RRC configuration message indicates the AI ​​model to be used by the UE. The RRC configuration message may be a use case configuration message or an AI specific configuration message. The use case configuration message is a configuration message for each use case. The AI ​​specific configuration message is a unified configuration message for different use cases.

[0068] In some instances, the AI ​​model may be implicitly indicated in the RRC configuration message. For example, the AI ​​model may be indicated by an AI-based codebook (e.g., a CSI codebook) in the RRC configuration message. This may be done by adding a new field in the CodebookConfig structure. The current codebookConfig configures type 1 and type 2 codebooks. An additional type-AI codebook may be added to the CodebookConfig structure. In some instances, the AI ​​model may be explicitly indicated in the RRC configuration message. For example, the RRC configuration message may include a model ID to explicitly indicate the AI ​​model. A list of model IDs may be included in the RRC configuration message. Different model IDs may correspond to different use cases.

[0069] If the metadata is used for the model description of the AI ​​model, the UE may derive model-related parameters from the metadata associated with the model ID. For example, the model-related parameters in the metadata may include training status, functionality / objects, model input / output, latency benchmark, memory requirements, accuracy, compression status, reasoning / operation conditions (e.g., urban, indoor, dense macro, etc.), pre-processing and post-processing, etc.

[0070] Alternatively, the RRC configuration may include a complete list of information, including model ID, use cases (e.g., CSI compression, BM, positioning, etc.), input data for the model (e.g., CSI-RS configuration for CSI, BM, positioning reference signal (PRS) / sounding reference signal (SRS) configuration for positioning), pre-processing and post-processing for the model (e.g., domain transfer information for CSI compression), model output content, size and feedback format, etc.

[0071] In some instances, the RRC configuration message for model configuration may include a report quantity field that indicates the number of AI-based reports to be fed back (e.g., the format of the number of reports corresponding to the AI ​​model). The number of reports varies depending on the use case. For example, the number of reports may be CSI, positioning information, etc. Figure 8 An example information structure of the reportQuantity parameter of the CSI-ReportConfig information element of the RRC configuration message is illustrated. The reportQuantity parameter can be RI-PMI-CQI-AI indicating the AI-generated RI (rank indicator), PMI (pre-decoder matrix indicator), and CQI (channel quality indicator) to be fed back as CSI. All configurations of the structure of the reportQuantity parameter can be reused for MIMO feedback.

[0072] Return to Figure 7 . Upon receiving the RRC configuration message for model configuration, the UE may activate the AI ​​model. Alternatively, the gNB transmits a MAC (Medium Access Control) CE (Control Element) / DCI (Downlink Control Information) message for model activation in step 704, and the UE activates the AI ​​model upon receiving the MAC CE / DCI. The MAC CE / DCI for model activation may include a model ID corresponding to the AI ​​model to be activated.

[0073] Once the AI ​​model is activated, the UE may generate an AI-based feedback message and send it to the gNB in ​​step S706. For example, the UE may perform measurements based on the reasoning of the AI ​​model and generate an AI-based report quantity, and include the AI-based report quantity in the AI-based feedback message.

[0074] In addition, the performance of the AI ​​model can be monitored at the gNB or UE. In some instances, performance monitoring can be based on ACK / NACK feedback. For example, if the DL (downlink) throughput decreases, the gNB can determine that the performance of the AI ​​model has degraded. For another example, if the physical downlink shared channel (PDSCH) block error rate (BLER) increases, the UE can determine that the performance of the AI ​​model has degraded. The metrics used for performance monitoring can be configured by the network.

[0075]

[00136] In some instances, performance monitoring may be based on a legacy reporting number. For example, an AI-based feedback message may include an AI-based reporting number and a legacy reporting number for monitoring the performance of an AI model. The gNB may compare the AI-based reporting number with the legacy reporting number for performance monitoring. If the difference between the AI-based reporting number and the legacy reporting number exceeds a threshold, the gNB may determine that the performance of the AI ​​model has degraded. The legacy reporting number may be sent periodically or aperiodically. If the legacy reporting number is sent aperiodically, performance monitoring may be triggered when performance degradation is observed based on ACK / NACK feedback.

[0076] If it is decided to switch / deactivate the AI ​​model (e.g., in response to performance degradation), the gNB may send an RRC reconfiguration message / MAC CE / DCI for model switching / deactivation at step S708. If multiple model IDs are configured in the RRC configuration message at step S702, the model switching may be performed via a MAC CE / DCI including a new model ID among the multiple model IDs.

[0077] In addition, when the UE fine-tunes the AI ​​model, the UE can send the updated AI model to the gNB via UAI (UE Assistance Information) or MAC CE. The network can assign a new model ID to the updated AI model or update the model description of the AI ​​model.

[0078] Various embodiments are described above with respect to UE and gNB. However, as previously indicated, UE as described herein is used to represent any suitable wireless device, and gNB as described herein is used to represent any suitable network device.

[0079] Fig. 9An example method for lifecycle management of a model performed by a wireless device according to an embodiment disclosed herein is illustrated. In step S902, the wireless device receives a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device. In step S904, the wireless device sends an AI-based feedback message generated based on the AI ​​model to the network device. Any of the steps described above with reference to the UE may be performed by the wireless device, and for the sake of brevity, the description is not repeated here.

[0080] Fig.10 An example method for lifecycle management of a model performed by a network device according to an embodiment disclosed herein is illustrated. In step S1002, the network device sends a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device. In step S1004, the network device receives an AI-based feedback message generated based on the AI ​​model from the wireless device. Any of the steps described above with reference to the gNB may be performed by the network device, and are not repeated here for the sake of brevity.

[0081] Embodiments contemplated herein include a wireless device comprising: at least one antenna; and a processor; wherein the wireless device is configured to: receive a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and send an AI-based feedback message generated based on the AI ​​model to the network device.

[0082] In some embodiments of the present disclosure, the RRC configuration message includes a model ID of the AI ​​model.

[0083] In some embodiments of the present disclosure, the RRC configuration message includes a report quantity field, which indicates the number of AI-based reports to be fed back.

[0084] In some embodiments of the present disclosure, the reported quantity is channel state information or positioning information.

[0085] In some embodiments of the present disclosure, the wireless device is further configured to send capability information to the network device, where the capability information indicates use cases supported by the wireless device and a model ID corresponding to the use case.

[0086] In some embodiments of the present disclosure, the wireless device is further configured to activate the AI ​​model based on the RRC configuration message.

[0087] In some embodiments of the present disclosure, the wireless device is further configured to activate the AI ​​model based on a medium access control (MAC) control element (CE) or downlink control information (DCI) received from the network device.

[0088] In some embodiments of the present disclosure, the wireless device is further configured to monitor the performance of the AI ​​model based on a physical downlink shared channel (PDSCH) block error rate (BLER).

[0089] In some embodiments of the present disclosure, the AI-based feedback message includes an AI-based report number and a traditional report number for monitoring the performance of the AI ​​model.

[0090] In some embodiments of the present disclosure, the wireless device is further configured to switch to another AI model based on one of an RRC reconfiguration message, a MAC CE, or a DCI.

[0091] In some embodiments of the present disclosure, the wireless device is further configured to report the updated AI model to the network device via User Assisted Information (UAI) or MACCE.

[0092] In some embodiments of the present disclosure, the wireless device is also configured to collect measurement data for training the AI ​​model based on the AI ​​training set ID received from the network device.

[0093] Embodiments contemplated herein include an apparatus including means for performing one or more elements of a method, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by a wireless device; and sending an AI-based message generated based on the AI ​​model to the network device. The apparatus may be, for example, an apparatus of a UE (such as wireless device 202 as a UE, as described herein).

[0094] Embodiments contemplated herein include one or more non-transitory computer-readable media including instructions to cause an electronic device to perform one or more elements of a method when the instructions are executed by one or more processors of the electronic device, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by a wireless device; and sending an AI-based message generated based on the AI ​​model to the network device. The non-transitory computer-readable medium can be, for example, a memory of a UE (such as the memory 206 of the wireless device 202 as a UE, as described herein).

[0095] Embodiments contemplated herein include an apparatus including logical components, modules, or circuits for performing one or more elements of a method, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by a wireless device; and sending an AI-based message generated based on the AI ​​model to the network device. The apparatus may be, for example, an apparatus of a UE (such as a wireless device 202 as a UE, as described herein).

[0096] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media, the one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of a method, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by a wireless device; and sending an AI-based message generated based on the AI ​​model to the network device. The apparatus may be, for example, an apparatus of a UE (such as a wireless device 202 as a UE, as described herein).

[0097] Embodiments contemplated herein include signals as described in or relating to one or more elements of a method, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by a wireless device; and sending an AI-based message generated based on the AI ​​model to the network device.

[0098] The embodiments contemplated herein include a computer program or computer program product containing instructions, wherein the program is executed by a processor so that the processor implements one or more elements of a method, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by a wireless device; and sending an AI-based message generated based on the AI ​​model to the network device. The processor can be a processor of a UE (such as processor 204 of wireless device 202 as a UE, as described herein). These instructions can be located, for example, in a processor and / or on a memory of a UE (such as memory 206 of wireless device 202 as a UE, as described herein).

[0099] Embodiments contemplated herein include a network device comprising: at least one antenna; and a processor; wherein the network device is configured to: send a radio resource control (RRC) configuration message to the wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receive an AI-based feedback message generated based on the AI ​​model from the wireless device.

[0100] In some embodiments of the present disclosure, the RRC configuration message includes a model ID of the AI ​​model.

[0101] In some embodiments of the present disclosure, the RRC configuration message includes a report quantity field, which indicates the number of AI-based reports to be fed back.

[0102] In some embodiments of the present disclosure, the reported quantity is channel state information or positioning information.

[0103] In some embodiments of the present disclosure, the network device is also configured to determine the AI ​​model based on capability information received from the network device.

[0104] In some embodiments of the present disclosure, the capability information indicates use cases supported by the wireless device and a model ID corresponding to the use case.

[0105] In some embodiments of the present disclosure, the network device is also configured to send a medium access control (MAC) control element (CE) or downlink control information (DCI) to the wireless device for activating the AI ​​model.

[0106] In some embodiments of the present disclosure, the network device is also configured to monitor the performance of the AI ​​model based on an acknowledgement (ACK) message or a negative acknowledgement (NACK) message.

[0107] In some embodiments of the present disclosure, the AI-based feedback message includes an AI-based report number and a traditional report number for monitoring the performance of the AI ​​model.

[0108] In some embodiments of the present disclosure, the network device is further configured to send one of an RRC reconfiguration message, a MAC CE, or a DCI to the wireless device for switching to another AI model.

[0109] In some embodiments of the present disclosure, the network device is further configured to receive an updated AI model from the wireless device via User Assistance Information (UAI) or MAC CE.

[0110] In some embodiments of the present disclosure, the network device is also configured to assign a new model ID to the updated AI model or update the model description of the AI ​​model.

[0111] In some embodiments of the present disclosure, the network device is also configured to send an AI training set ID to the wireless device for collecting measurement data for training the AI ​​model.

[0112] Embodiments contemplated herein include an apparatus comprising means for performing one or more elements of a method, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based feedback message generated based on the AI ​​model from the wireless device. The apparatus may be, for example, an apparatus of a base station (such as network device 218 as a base station, as described herein).

[0113] Embodiments contemplated herein include one or more non-transitory computer-readable media including instructions to cause an electronic device to perform one or more elements of a method when the instructions are executed by one or more processors of the electronic device, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based feedback message generated based on the AI ​​model from the wireless device. The non-transitory computer-readable medium may be, for example, a memory of a base station (such as the memory 222 of the network device 218 as a base station, as described herein).

[0114] Embodiments contemplated herein include an apparatus including logic components, modules, or circuits for performing one or more elements of a method, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based feedback message generated based on the AI ​​model from the wireless device. The apparatus may be, for example, an apparatus of a base station (such as a network device 218 as a base station, as described herein).

[0115] Embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media, the one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of a method, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based feedback message generated based on the AI ​​model from the wireless device. The apparatus may be, for example, an apparatus of a base station (such as a network device 218 that is a base station, as described herein).

[0116] Embodiments contemplated herein include signals as described in or relating to one or more elements of a method, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based feedback message generated based on the AI ​​model from the wireless device.

[0117] Embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processing element causes the processing element to perform one or more elements of a method, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and receiving an AI-based feedback message generated based on the AI ​​model from the wireless device. The processor may be a processor of a base station (such as processor 220 of network device 218 as a base station, as described herein). For example, the instructions may be located in a processor and / or on a memory of a base station (such as memory 222 of network device 218 as a base station, as described herein).

[0118] For one or more embodiments, at least one of the components set forth in one or more of the foregoing figures may be configured to perform one or more operations, techniques, processes, and / or methods as described herein. For example, a baseband processor as described herein in conjunction with one or more of the foregoing figures may be configured to operate according to one or more of the examples set forth herein. For another example, circuits associated with a UE, a base station, a network element, etc. as described above in conjunction with one or more of the foregoing figures may be configured to operate according to one or more of the examples set forth herein.

[0119] Unless otherwise expressly stated, any of the above embodiments may be combined with any other embodiment (or combination of embodiments). The foregoing description of one or more specific implementations provides illustration and description, but is not intended to be exhaustive or to limit the scope of the embodiments to the precise form disclosed. In view of the above teachings, modifications and variations are possible or can be obtained from the practice of various embodiments.

[0120] Embodiments and implementations of the systems and methods described herein may include various operations that may be embodied in machine executable instructions to be executed by a computer system. A computer system may include one or more general or special purpose computers (or other electronic devices). A computer system may include hardware components that include specific logic components for performing operations; or may include a combination of hardware, software, and / or firmware.

[0121] It should be appreciated that the systems described herein include descriptions of specific embodiments. These embodiments may be combined into a single system, partially combined into other systems, separated into multiple systems, or otherwise divided or combined. In addition, it is contemplated that parameters, attributes, aspects, etc. of another embodiment may be used in one embodiment. For clarity, these parameters, attributes, aspects, etc. are described only in one or more embodiments, and it should be appreciated that these parameters, attributes, aspects, etc. may be combined with or substituted for parameters, attributes, aspects, etc. of another embodiment unless expressly stated herein.

[0122] It is understood that the use of personally identifiable information should be subject to privacy policies and practices that are generally recognized to meet or exceed industry or government requirements for maintaining user privacy. Specifically, personally identifiable information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use, and the nature of the authorized use should be clearly stated to users.

[0123] Although the foregoing has been described in considerable detail for the sake of clarity, it will be apparent that certain changes and modifications may be made without departing from the principles of the invention. It should be noted that there are many alternative ways to implement both the processes and the apparatus described herein. Therefore, the embodiments of the present invention should be regarded as illustrative rather than restrictive, and the specification is not limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims

1. A wireless device, comprising: at least one antenna; and processor; The wireless device is configured to: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and Sending an AI-based feedback message generated based on the AI ​​model to the network device.

2. The wireless device of claim 1, wherein the RRC configuration message comprises a model ID of the AI ​​model.

3. The wireless device of claim 1 , wherein the RRC configuration message comprises a report quantity field indicating a number of AI-based reports to be fed back. The wireless device according to claim 3 , wherein the reported quantity is channel state information or positioning information. 5 . The wireless device of claim 1 , wherein the wireless device is further configured to send capability information to the network device, the capability information indicating use cases supported by the wireless device and a model ID corresponding to the use case.

6. The wireless device of claim 1, wherein the wireless device is further configured to activate the AI ​​model based on the RRC configuration message.

7. The wireless device of claim 1, wherein the wireless device is further configured to activate the AI ​​model based on a medium access control (MAC) control element (CE) or downlink control information (DCI) received from the network device.

8. The wireless device of claim 1 , wherein the wireless device is further configured to monitor the performance of the AI ​​model based on a physical downlink shared channel (PDSCH) block error rate (BLER).

9. The wireless device of claim 1, wherein the AI-based feedback message comprises an AI-based report number and a traditional report number for monitoring the performance of the AI ​​model.

10. The wireless device of claim 1, wherein the wireless device is further configured to switch to another AI model based on one of an RRC reconfiguration message, a MAC CE, or a DCI.

11. The wireless device of claim 1, wherein the wireless device is further configured to report the updated AI model to the network device through User Assisted Information (UAI) or MAC CE.

12. The wireless device of claim 1, wherein the wireless device is further configured to collect measurement data for training the AI ​​model based on the AI ​​training set ID received from the network device.

13. A network device, comprising: at least one antenna; and processor; The network device is configured as follows: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and An AI-based feedback message generated based on the AI ​​model is received from the wireless device.

14. The network device according to claim 13, wherein the RRC configuration message includes a model ID of the AI ​​model.

15. The network device according to claim 13, wherein the RRC configuration message comprises a report quantity field, the report quantity field indicating the number of AI-based reports to be fed back. The network device according to claim 15 , wherein the reported quantity is channel state information or positioning information.

17. The network device of claim 13, wherein the network device is further configured to determine the AI ​​model based on capability information received from the network device.

18. The network device of claim 17, wherein the capability information indicates use cases supported by the wireless device and a model ID corresponding to the use case.

19. The network device of claim 13, wherein the network device is further configured to send a medium access control (MAC) control element (CE) or downlink control information (DCI) to the wireless device for activating the AI ​​model.

20. The network device of claim 13, wherein the network device is further configured to monitor the performance of the AI ​​model based on an acknowledgement (ACK) message or a negative acknowledgement (NACK) message.

21. The network device of claim 13, wherein the AI-based feedback message comprises an AI-based report quantity and a traditional report quantity for monitoring the performance of the AI ​​model.

22. The network device of claim 13, wherein the network device is further configured to send one of an RRC reconfiguration message, a MAC CE, or a DCI to the wireless device for switching to another AI model.

23. The network device of claim 13, wherein the network device is further configured to receive an updated AI model from the wireless device through User Assisted Information (UAI) or MAC CE.

24. The network device according to claim 23, wherein the network device is further configured to assign a new model ID to the updated AI model or update a model description of the AI ​​model.

25. The network device of claim 13, wherein the network device is further configured to send an AI training set ID to the wireless device for collecting measurement data for training the AI ​​model.

26. A method performed by a wireless device, the method comprising: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and Sending an AI-based message generated based on the AI ​​model to the network device.

27. A method performed by a network device, the method comprising: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and An AI-based message generated based on the AI ​​model is received from the wireless device.

28. A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processor, cause a wireless device to: receiving a radio resource control (RRC) configuration message from a network device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and Sending an AI-based message generated based on the AI ​​model to the network device.

29. A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processor, cause a network device to: sending a radio resource control (RRC) configuration message to a wireless device, the RRC configuration message indicating an artificial intelligence (AI) model to be used by the wireless device; and An AI-based message generated based on the AI ​​model is received from the wireless device.

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