Communication method and communication device

Abstract

The embodiments of the present disclosure relate to a method, an apparatus, and a computer-readable medium for communication. According to the embodiments of the present disclosure, a terminal device reports one or more capabilities of the terminal device to a network device. The one or more capabilities indicate that the terminal device supports an artificial intelligence / machine learning (AI / ML) model. The terminal device receives an instruction from the network device to trigger a channel state information (CSI) report for the AI / ML model. The terminal device measures a set of reference signals for the CSI report. In this way, the AI / ML model can be trained to improve the accuracy of beam management.

Description

[Technical field]

[0001] TECHNICAL FIELD Embodiments of the present disclosure relate generally to the field of telecommunications, and more particularly to communication methods, apparatus, and computer-readable media. [Background technology]

[0002] Several techniques have been proposed to improve communication performance. For example, a communication device may employ artificial intelligence / machine learning (AI / ML) models to improve performance. In some situations, the communication device may perform beam management based on the AI / ML models. Summary of the Invention [Problem to be solved by the invention]

[0003] Overall, the exemplary embodiments of the present disclosure provide a solution for communications. [Means for solving the problem]

[0004] In a first aspect, a communication method is provided, the communication method including: in a terminal device, reporting to a network device one or more capabilities of the terminal device, the one or more capabilities at least indicating that the terminal device supports a data processing model; receiving from the network device one or more settings associated with the data processing model; and receiving from the network device an instruction to trigger a report for obtaining information associated with the data processing model.

[0005] In a second aspect, a communication method is provided, the communication method including: receiving, in a network device, from a terminal device, a capability report including one or more capabilities of the terminal device, the one or more capabilities at least indicating that the terminal device supports a data processing model; sending, to the terminal device, one or more settings associated with the data processing model; and sending, to the terminal device, an instruction to trigger a report for obtaining information associated with the data processing model.

[0006] In a third aspect, a terminal device is provided, the terminal device comprising: a processing unit; and a memory coupled to the processing unit and storing instructions, which, when executed by the processing unit, cause the terminal device to perform operations including: reporting, at the terminal device, to a network device, one or more capabilities of the terminal device, the one or more capabilities indicating at least that the terminal device supports a data processing model; receiving, from the network device, one or more settings associated with the data processing model; and receiving, from the network device, an instruction to trigger a report for obtaining information associated with the data processing model.

[0007] In a fourth aspect, a network device is provided, the network device comprising: a processing unit; and a memory coupled to the processing unit and storing instructions, the instructions, when executed by the processing unit, cause the network device to perform operations including: receiving, at the network device, a capability report from a terminal device, the capability report including one or more capabilities of the terminal device, the one or more capabilities indicating at least that the terminal device supports a data processing model; sending, to the terminal device, one or more settings associated with the AI / ML model; and sending, to the terminal device, an instruction to trigger a report for obtaining information associated with the data processing model.

[0008] In a fifth aspect, there is provided a computer readable medium storing instructions which, when executed on at least one processor, cause the at least one processor to perform a method according to the first or second aspect.

[0009] Other features of the present disclosure will become readily apparent from the following description. [Brief description of the drawings]

[0010] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description of several exemplary embodiments of the present disclosure in the accompanying drawings.

[0011] [Figure 1] FIG. 1 is a schematic diagram of a communication environment in which embodiments of the present disclosure can be implemented.

[0012] [Diagram 2] FIG. 2 illustrates a signaling flow for communication in accordance with some embodiments of the present disclosure.

[0013] [Figure 3A] FIG. 1 is a schematic diagram illustrating detection of a fault in an AI / ML model, in accordance with some embodiments of the present disclosure.

[0014] [Figure 3B] FIG. 1 is a schematic diagram illustrating detection of a fault in an AI / ML model, in accordance with some embodiments of the present disclosure.

[0015] [Figure 4] FIG. 1 is a schematic diagram illustrating training of an AI / ML model, in accordance with some embodiments of the present disclosure.

[0016] [Diagram 5] 1 is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0017] [Figure 6] 1 is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0018] [Figure 7] FIG. 1 is a schematic block diagram of an apparatus suitable for implementing embodiments of the present disclosure.

[0019] In the drawings, the same or similar reference numbers represent the same or similar elements. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] The principles of the present disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are provided for illustrative purposes only, to assist those skilled in the art in understanding and implementing the present disclosure, and do not imply any limitations on the scope of the present disclosure. The disclosure described herein can be implemented in various ways different from those described below.

[0021] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0022] As used herein, the term "terminal device" refers to any device having wireless or wired communication capabilities. Examples of terminal devices include User Equipment (UE), personal computers, desktops, mobile phones, cellular phones, smartphones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, Internet of Things (IoT) devices, Ultra-Reliable Low Latency Communication (URLLC) devices, Internet of Everything (IoE) devices, machine type communication (MTC) devices, vehicle-mounted devices for V2X communications where X stands for pedestrian, vehicle, or infrastructure / network, devices for integrated access and integrated access and backhaul (IAB), satellite-based or airborne vehicles in a non-terrestrial network (NTN) including High Altitude Platforms (HAPs) including satellites and Unmanned Aircraft Systems (UASs), extended reality (XR) devices including different types of reality such as augmented reality (AR), mixed reality (MR), virtual reality (VR), unmanned aerial vehicles (UAVs), which are aircraft without a human pilot and commonly referred to as drones. "Terminal Device" includes, but is not limited to, a vehicle (V2X), a device on a high speed train (HST), or an image capture device such as a digital camera, a sensor game device, a music storage and playback device, or an Internet appliance that allows wireless or wired Internet access and browsing, etc. The "Terminal Device" may further have "multicast / broadcast" capabilities to support V2X applications, transparent IPv4 / IPv6 multicast distribution, IPTV, smart TV, wireless services, over-the-air software distribution, group communication, and IoT applications that are public safety and mission critical. It may also incorporate one or more Subscriber Identity Modules (SIMs), known as multi-SIMs.The term "terminal" may be used interchangeably with UE, mobile station, subscriber station, mobile terminal, user terminal, or wireless device. In the following description, the terms "terminal", "communications device", "terminal", "user equipment" and "UE" may be used interchangeably.

[0023] The terminal device or network device may have the capability of artificial intelligence (AI) or machine learning. Generally, it includes a model trained from a large amount of data collected for a specific function and can be used to predict some information.

[0024] The terminal device or network device may operate on several frequency ranges, such as FR1 (410 MHz-7125 MHz), FR2 (24.25 GHz-71 GHz), frequency bands greater than 100 GHz, and Tera Hertz (THz). It can also operate on licensed / unlicensed / shared spectrum. The terminal device may have two or more connections with the network device under Multi-Radio Dual Connectivity (MR-DC) application scenarios. The terminal device or network device can operate in full duplex, flexible duplex, and cross division duplex modes.

[0025] The term "network device" refers to a device capable of providing or hosting a cell or coverage in which a terminal device can communicate. Examples of network devices include, but are not limited to, a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), a next generation Node B (gNB), a transmit / receive point (TRP), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), an IAB node, a femto node, a pico node, a low power node such as a reconfigurable intelligent surface (RIS), etc.

[0026] In one embodiment, the terminal device can connect to a first network device and a second network device. One of the first network device and the second network device may be a master node and the other may be a secondary node. The first network device and the second network device may use different radio access technologies (RATs). In one embodiment, the first network device may be a first RAT device and the second network device may be a second RAT device. In one embodiment, the first RAT device is an eNB and the second RAT device is a gNB. Information about the different RATs may be transmitted from at least one of the first network device and the second network device to the terminal device. In one embodiment, the first information may be transmitted from the first network device to the terminal device, and the second information may be transmitted from the second network device to the terminal device directly or via the first network device. In one embodiment, information about the configuration of the terminal device configured by the second network device may be transmitted from the second network device via the first network device. Information regarding the reconfiguration of the terminal device configured by the second network device may be transmitted from the second network device directly to the terminal device or via the first network device.

[0027] The communications described herein may conform to any suitable standard, including, but not limited to, New Radio Access (NR), Long Term Evolution (LTE), LTE-Evolution, LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), cdma2000, Global System for Mobile Communications (GSM), and the like. Furthermore, the communications may be performed according to any generation of communication protocols now known or developed in the future. Examples of communication protocols include, but are not limited to, first generation (1G), second generation (2G), 2.5G, 2.85G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G), and sixth generation (6G) communication protocols. The techniques described herein may be used for the wireless networks and radio technologies mentioned above, as well as other wireless networks and radio technologies. The embodiments of the present disclosure may be performed according to any generation of communication protocols now known or developed in the future. Examples of communication protocols include, but are not limited to, first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G) communication protocols, 5.5G, 5G-Advanced networks, or sixth generation (6G) networks.

[0028] The term "circuitry" as used herein may refer to a hardware circuit and / or a combination of a hardware circuit and software. For example, a circuit may be a combination of analog and / or digital hardware circuitry and software / firmware. As yet another example, a circuit may be any portion of a hardware processor having software, including a digital signal processor, software, and one or more memories, that cooperate to cause a device, such as a terminal device or a network device, to perform various functions. In yet another example, a circuit may be a hardware circuit and / or a processor, such as a microprocessor or a portion thereof, that requires software / firmware for operation, but the software may not be present if not required for operation. As used herein, the term "circuitry" also includes an implementation of only a hardware circuit or one or more processors, or a portion of a hardware circuit or one or more processors and its (or their) associated software and / or firmware.

[0029] As used herein, the singular forms "a," "an," and "said" include the plural forms unless the context clearly indicates otherwise. The term "comprises" and variations thereof should be understood as open-ended terms meaning "including, but not limited to." The term "based on" should be understood as "based at least in part on." The terms "one embodiment" and "embodiment" should be understood as "at least one embodiment." The term "another embodiment" should be understood as "at least one other embodiment." Terms such as "first," "second," and the like may refer to different or the same object. The following may include other explicit and implicit definitions.

[0030] In some instances, values, procedures, or devices are referred to as "best," "lowest," "highest," "minimum," "maximum," etc. It should be understood that such descriptions are intended to illustrate that selections can be made from among many functional alternatives used, and that such selections are not necessarily better, smaller, higher, or otherwise more preferred than other selections.

[0031] As mentioned above, the communication device can perform beam management on the AI / ML model. Massive MIMO (mMIMO) and beamforming are widely used in the communication industry. The terms "beamforming" and "mMIMO" may be used interchangeably. Generally speaking, beamforming uses multiple antennas to control the direction of a wavefront by appropriately weighting the amplitude and phase of individual antenna signals in an array of multiple antennas. The most commonly found definition is that mMIMO is a system in which the number of antennas exceeds the number of users. Coverage is beam-based, not cell-based, in 5G. There is no cell-level reference channel by which the coverage of a cell can be measured. Instead, each cell has one or more Synchronization Signal Block (SSB) beams. SSB beams are static or semi-static and always point in the same direction. They form a grid of beams that cover the entire cell area. The user equipment (UE) searches and measures the beams and maintains a set of candidate beams. The set of candidate beams may include beams from multiple cells. With 5G millimeter wave (mmWave) enabling directional communication with more antenna elements and providing additional beamforming gain, efficient beam management becomes important where the UE and gNB periodically identify the best beam to work with at any given time.

[0032] The AI / ML model may be deployed in the gNB in ​​the first phase. It is natural to discuss AI / ML for the NR air interface starting from the gNB, since the gNB is more powerful in dealing with data, models, and computing load for AI / ML. For beam prediction in the spatial domain, the AI / ML model trained at the gNB side is not accurate for some UEs. That is, the predicted (or inferred) optimal beam is inconsistent with the actual optimal beam from the UE side. For example, the UE can measure the quality (e.g., Layer 1 Reference Signal Received Power (L1-RSRP)) of beams F, G, J, and K to estimate the quality of all candidate beams based on the AI / ML model trained at the gNB side, and determine the optimal beam (assuming beam A) with the maximum L1-RSRP based on the L1-RSRP of all candidate beams. However, in reality, beam A (i.e., Rx beam) of the UE is covered by a shelter. If beam A is continued to be used, the transmission quality will be seriously affected. Offline training of the AI / ML model is not possible to consider all "accident" occurrences. To ensure that the AI / ML model can adapt to the UE's environment, online training needs to be added to the offline trained model. For example, online training can be performed on the gNB side. However, in this situation, the UE needs to provide a large amount of data required for training, which may cause huge UL overhead. Therefore, online training should be performed on the UE side.

[0033] According to an embodiment, a solution for improving an AI / ML model is proposed. According to an embodiment of the present disclosure, a terminal device reports one or more capabilities of the terminal device to a network device. The one or more capabilities indicate that the terminal device supports an AI / ML model. The terminal device receives one or more settings associated with the AI / ML model from the network device. The terminal device receives an instruction from the network device to trigger a report to obtain information associated with the AI / ML model. In this way, the AI / ML model can be trained to improve the accuracy of beam management.

[0034] FIG. 1 is a schematic diagram of a communication system in which embodiments of the present disclosure can be implemented. The communication system 100, which is part of a communication network, includes terminal devices 110-1, 110-2, ..., 110-N, which may be collectively referred to as "terminal devices 110." The number N may be any suitable integer. The terminal devices 110 may communicate with each other. By way of example only, terminal device 110-1 may be configured with multiple beams, shown as beams 131-1, 132-1, 133-1, and 134-1. The network device 120 may be configured with multiple beams, shown as beams 131-2, 132-2, 133-2, and 134-2. A beam pair may include two beams, e.g., beams 131-1 and 131-2, 132-1 and 132-2, 133-1 and 133-2, and 134-1 and 134-2. It should be noted that the number of beams shown in FIG. 1 is not limiting and is merely an example.

[0035] The communication system 100 further includes a network device. In the communication system 100, the network device 120 and the terminal device 110 can communicate data and control information with each other. The number of terminal devices shown in FIG. 1 is shown for illustrative purposes only and does not imply any limitation.

[0036] Communications in the communications system 100 may be achieved according to any suitable communications protocol, including, but not limited to, cellular communications protocols, such as first generation (1G), second generation (2G), third generation (3G), fourth generation (4G), and fifth generation (5G), wireless local area network communications protocols, such as Institute for Electrical and Electronics Engineers (IEEE) 802.11, and / or any other protocols now known or developed in the future. Furthermore, communications may utilize any suitable wireless communications technology, including, but not limited to, code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), frequency division duplexer (FDD), time division duplexer (TDD), multiple input multiple output (MIMO), orthogonal frequency division multiple access (OFDMA), and / or any other technology now known or developed in the future.

[0037] The embodiments of the present disclosure may be applied to any suitable scenario. For example, the embodiments of the present disclosure may be implemented on a reduced capability NR device. Alternatively, the embodiments of the present disclosure may be implemented within one of NR multiple input multiple output (MIMO), NR sidelink enhancements, NR systems at frequencies higher than 52.6 GHz, enhanced NR operation up to 71 GHz, Narrowband Internet of Things (NB-IOT) / enhanced machine type communication (eMTC) over non-terrestrial based networks (NTN), NTN, UE power saving enhancements, NR coverage enhancements, NB-IOT and LTE-MTC, integrated access and backhaul (IAB), NR multicast and broadcast services, or multi-radio dual connectivity enhancements.

[0038] The term "slot" as used herein means a dynamic scheduling unit. A slot includes a predetermined number of symbols. The term "downlink (DL) subslot" may refer to a virtual subslot built on an uplink (UL) subslot. A DL subslot may include fewer symbols than a DL slot. A slot as used herein may refer to a normal slot including a predetermined number of symbols and a subslot including fewer symbols than the predetermined number of symbols.

[0039] In the following, embodiments of the present disclosure will be described in detail. First, reference is made to FIG. 2, which illustrates a signaling diagram illustrating a process 200 between a terminal device and a network device according to some exemplary embodiments of the present disclosure. For illustrative purposes only, the process 200 will be described with reference to FIG. 1. The process 200 may involve the terminal device 110-1 and the network device 120 of FIG. 1. In some embodiments, the process 200 is applicable to detecting faults in an AI / ML model. Alternatively, the process 200 is applicable to online training of an AI / ML model.

[0040] The terminal device 110-1 reports (2010) one or more capabilities of the terminal device 110-1 to the network device 120. The one or more capabilities at least indicate that the terminal device 110-1 supports a data processing model. The data processing model may be an AI / ML model. As used herein, an "AI / ML model" may refer to a program or algorithm that utilizes a set of data that allows for the recognition of certain patterns, thereby allowing a conclusion to be reached or a prediction to be made when provided with sufficient information. Generally speaking, an AI / ML model may be a mathematical algorithm that is "trained" with data and the input of a human expert to reproduce the decision that the expert would make when provided with the same information.

[0041] In some embodiments, the capability may indicate a capability to support AI / ML. In some embodiments, the capability may indicate a capability to support beam management based on AI / ML. Alternatively or additionally, the capability may indicate a capability to support beam prediction in the spatial domain based on AI / ML. Additionally, the capability may indicate that the terminal device 110-1 supports a capability to support online training. For example, fine-tuning is applicable to online training. It should be noted that other suitable methods are also applicable to online training. The capability may also indicate an index of an AI / ML model supported by the terminal device 110-1. In some other embodiments, the capability may indicate a first time delay, which is a minimum time required to detect a fault in the AI / ML model at the terminal device 110-1 side. Thus, the network device 120 can configure the corresponding AI / ML model and associated activation parameters for the terminal device 110-1.

[0042] In another embodiment, the capability may indicate a second time delay, which is a minimum time required for online training of the AI / ML model at the terminal device 110-1, thereby facilitating joint analysis of the AI / ML model.

[0043] The network device 120 transmits (2020) one or more configurations associated with the AI / ML model to the terminal device 110-1. The configurations may be transmitted via higher layer signaling. For example, the configurations may be transmitted via RRC signaling.

[0044] In some embodiments, the one or more settings may include a first setting. In this case, the first setting may indicate an index of the AI / ML model. Additionally, the first setting may indicate a first type of parameter of the AI / ML model. In this case, the first type of parameter may include a structural parameter of the AI / ML model. For example, the structural parameter may indicate a deep neural network (DNN) of the AI / ML model. Alternatively, the structural parameter may indicate a convolutional neural network (CNN) of the AI / ML model. The structural parameter may also indicate a number of layers of the AI / ML model. The structural parameter may indicate a type of layer of the AI / ML model. Additionally, the structural parameter may indicate a number of neurons of the AI / ML model. In some other embodiments, the first type of parameter may include a factor of the AI / ML model. For example, the factor may be a weight factor. Alternatively or additionally, the factor may be a bias factor. Additionally or alternatively, the first setting may indicate the first type of parameter. In other words, the terminal device 110-1 may be configured to have an AI / ML model and corresponding first type parameters. The first type parameters may include a data format of an input of the AI / ML model. For example, the data format of the input may include a number of rows and a number of columns of the input data. Additionally, the data format of the input may include a unit of the input data. The data format of the input may include an interpretation of the input data.

[0045] Additionally, the first type of parameter may include a data format of the output of the AI / ML model. Similarly, the data format of the output may include a number of rows and columns of the output data. Additionally, the data format of the output may include a unit of the output data. The data format of the output may include an interpretation of the output data.

[0046] In some embodiments, the first type of parameters may include pre-processing parameters of the AI / ML model. The first type of parameters may also include post-processing parameters of the AI / ML model. For example, the first type of parameters may include a standardization factor. The term "standardization factor" may refer to performing a normalization operation on the input data to flatten the input and output data into the same distribution and accelerate the training network. For example, in the case of maximum-based normalization, all input data may be divided by a maximum value, which is the standardization factor.

[0047] Additionally, the first configuration may also include a first enabling parameter. The first enabling parameter may be used to enable the terminal device 110-1 to detect a fault in the AI / ML model. For example, if the first configuration does not include the first enabling parameter, the terminal device 110-1 may not perform detection of a fault in the AI / ML model. Alternatively, if the first enabling parameter is set to disabled (e.g., set to “0”), the terminal device 110-1 may not perform detection of a fault in the AI / ML model. In other embodiments, if the first enabling parameter is set to enabled (e.g., set to “1”), the terminal device 110-1 may perform detection of a fault in the AI / ML model. In some embodiments, the first enabling parameter may be transmitted within a CSI reporting configuration. Alternatively, the first enabling parameter may be transmitted via other higher layer signaling. In this way, the terminal device may determine the AI / ML model and the executable behavior.

[0048] In another embodiment, the first setting may include a third enabling parameter. The third enabling parameter may be used to enable the terminal device 110-1 to perform online training of the AI / ML model. For example, if the first setting does not include the third enabling parameter, the terminal device 110-1 may not perform online training of the AI / ML model. Alternatively, if the third enabling parameter is set to disabled (e.g., set to “0”), the terminal device 110-1 may not perform online training of the AI / ML model. In another embodiment, if the third enabling parameter is set to enabled (e.g., set to “1”), the terminal device 110-1 may perform online training of the AI / ML model. In this way, the terminal device may perform online training of the AI / ML model.

[0049] The network device 120 sends (2030) an indication to trigger a report to obtain information associated with the AI / ML model. In some embodiments, the CSI reporting may be periodic CSI reporting configurable by RRC signaling. Alternatively, the CSI reporting may be semi-persistent CSI reporting. In this case, such CSI reporting may be activated by a media access control element (MAC CE) from the network device 120. In other embodiments, the CSI reporting may be aperiodic CSI reporting or SP-CSI reporting. In this case, the CSI reporting may be triggered by downlink control information (DCI) from the network device 120.

[0050] In some embodiments, the network device 120 may transmit a configuration of the CSI report. For example, the configuration of the CSI report may be transmitted via an upper layer configuration of CSI-ReportConfig. The configuration of the CSI report may indicate that the CSI report is associated with one or more CSI-RS or SSB resource sets. In some embodiments, the CSI-RS resource set may be periodic. Alternatively, the CSI-RS resource set may be semi-persistent. In other embodiments, the CSI-RS resource set may be aperiodic. In some embodiments, each CSI-RS resource in the CSI-RS resource set may correspond to a beam. The configuration of the CSI report may indicate an index of an AI / ML model, which means that the CSI report is associated with an AI / ML model. In this way, the terminal device can know which AI / ML model the CSI report applies to.

[0051] The network device 120 may transmit the set of reference signals to the terminal device 110-1 (2040). The network device 120 may transmit the set of reference signals based on the CSI-RS resource set.

[0052] The terminal device 110-1 may measure the set of reference signals (2050). The terminal device 110-1 may measure reference signal received power (RSRP) for the set of reference signals. For example, the terminal device 110-1 may measure a CSI-RS resource indicator (CRI)-RSRP based on the set of reference signals. Alternatively, the terminal device 110-1 may measure an SS / PBCH resource block indicator (SSBRI)-RSRP. In another embodiment, the terminal device 110-1 may measure a CRI signal-to-interference plus noise ratio (SINR) based on the set of reference signals. Alternatively, the terminal device 110-1 may measure an SSBRI-SINR based on the set of reference signals.

[0053] The terminal device 110-1 may transmit a report to the network device 120 (2060). In some embodiments, the report may include information associated with the AI / ML model. For example, the report may include first information indicating whether the AI / ML model has failed. The first information may be a report amount. For example, the first information may occupy a bit field in the report. For illustration, when the first information indicates "1", it means that an AI / ML model failure has occurred. When the first information indicates "0", it means that an AI / ML model failure has not occurred. In this way, the terminal device can notify the network device of whether the AI / ML model has failed by reporting the first information.

[0054] In other embodiments, the report may indicate a measurement result. For example, the report may include one of CRI-RSRP, SSBRI-RSRP, CRI-SINR, or SSBRI-SINR. In some embodiments, the one or more configurations described above may include a second enabling parameter. The second enabling parameter may be used to indicate that the associated report is used to detect a failure in the AI / ML model. In this case, if a failure is detected in the AI / ML model, the terminal device 110-1 may not report any quantity, in other words, the terminal device 110-1 may not transmit a report. The transmission of the report may be skipped. At the same time, if there are no other conflicting reports in the time domain, the network device 120 may not receive any report. In this case, the network device 120 may determine that an AI / ML model failure has occurred. Alternatively, if no failure is detected in the AI / ML model, the terminal device 110-1 may report K (K>0) optimal beams and corresponding beam qualities, i.e., K CRIs and corresponding L1-RSRPs, in the report. Thus, although the reporting quality is set to "CRI-RSRP" because the report is associated with the second enabling parameter, the terminal device also knows that the report will be used to detect AI / ML model failures.

[0055] Alternatively, if a failure is detected in the AI / ML model, the report may include a bit field indicating a predetermined value, for example, if a failure of the AI / ML model occurs, the value of the bit field corresponding to the CRI and / or the L1-RSRP field in the report may be set to all "1".

[0056] In some embodiments, the one or more settings described above may include a second setting. The second setting may indicate a time offset. In this case, the terminal device 110-1 may transmit a report on a slot determined based on the time offset. In some embodiments, the time offset may depend on a first time delay that is a minimum time required to detect a failure in the AI / ML model in the terminal device 110-1. Alternatively, the time offset may depend on a second time delay that is a minimum time required for online training of the AI / ML model in the terminal device 110-1. The time offset may indicate on which slot the terminal device 110-1 forwards the report quantity. In some embodiments, the time offset may refer to a time offset according to a higher layer setting of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Alternatively, the time offset may be configured via an RRC configuration independent of the higher layer setting of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. In this case, if the first time offset is configured in the RRC configuration, the terminal device 110-1 may ignore the value provided by the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. In this case, the terminal device can know when to perform the report.

[0057] The terminal device 110-1 may determine that a failure has occurred on the AI / ML model if at least one condition is met. For example, the terminal device 110-1 may determine that a failure has occurred on the AI / ML model if a first target beam determined based on the AI / ML model differs from a second target beam determined based on measurements of the set of reference signals. Alternatively, the terminal device 110-1 may determine that a failure has occurred on the AI / ML model if a difference between a first amount of the first target beam and a second amount of the second target beam exceeds a first threshold. In another embodiment, the terminal device 110-1 may determine that a failure has occurred on the AI / ML model if the number of times the condition is met exceeds a second threshold. For example, the second threshold may be set via RRC signaling. In this case, the terminal device and the network device know under what conditions the AI / ML model occurs.

[0058] In some embodiments, the terminal device 110-1 may transmit multiple reports. In this case, each of the multiple reports may indicate a corresponding priority. For example, if other uplink information (e.g., another report) needs to be transmitted, the terminal device 110-1 may determine whether the other uplink information collides with the report. If the other uplink information collides with the report, the terminal device 110-1 may compare a first priority of the report with a second priority of the other uplink information. In this case, if the first priority is higher than the second priority, the terminal device 110-1 may transmit the report to the network device. The value of the first priority may be determined from a set of values. The value of the second priority may also be determined from the set of values. The set of values ​​may include 0, 1, and 2. It should be noted that the set of values ​​may also include other values. In this way, if multiple reports carrying different contents collide, the terminal device knows which CSI report to transmit first.

[0059] Alternatively, the network device 120 may determine whether a failure has occurred on the AI / ML model. In this case, a new report quantity, for example, second information, may be introduced. The second information may include inferred information and actual information. In this case, the inferred information includes at least one of an inferred beam determined based on the AI / ML model, or a beam quality corresponding to the inferred beam. Additionally, the actual information includes at least one of an actual beam determined based on measurements of a set of reference signals associated with the report, or a beam quality corresponding to the actual beam. In some embodiments, the beam quality may be RSRP. Alternatively, the beam quality may be SINR. In some embodiments, the beam quality may be defined by a 7-bit value. The beam quality may be in the range of -140 dBm to -44 dBm with a step size of 1 dB. In some embodiments, the bit fields corresponding to the inferred beam and the beam quality corresponding to the inferred beam are before the bit fields corresponding to the actual beam and the beam quality corresponding to the actual beam. Alternatively, the bit fields corresponding to the inferred beam and the beam quality corresponding to the inferred beam are after the bit fields corresponding to the actual beam and the beam quality corresponding to the actual beam.

[0060] In some embodiments, the report may be associated with third information. In this case, the third information may include a second type of parameter of the trained AI / ML model updated based on the AI / ML model. For example, the third information may mainly include the amount of change of the trained AI / ML model compared to the AI / ML model, such as weighting factors and bias factors. In this way, it is possible to facilitate joint analysis of the models on the network device side. Alternatively, the terminal device 110-1 may not report the trained AI / ML model. In this case, the terminal device 110-1 may only report the K optimal beams.

[0061] An exemplary embodiment will be described in detail with reference to Figures 3A to 5. Figure 3A illustrates a process in which a fault in an AI / ML model is detected by the terminal device 110-1.

[0062] The terminal device 110-1 reports (3010) one or more capabilities of the terminal device 110-1 to the network device 120. The one or more capabilities at least indicate that the terminal device 110-1 supports an AI / ML model.

[0063] In some embodiments, the capability may indicate a capability to support AI / ML. In some embodiments, the capability may indicate a capability to support beam management based on AI / ML. Alternatively or additionally, the capability may indicate a capability to support beam prediction in the spatial domain based on AI / ML. Additionally, the capability may indicate that the terminal device 110-1 supports a capability to support online training. For example, fine-tuning is applicable to online training. It should be noted that other suitable methods are also applicable to online training. The capability may also indicate an index of an AI / ML model supported by the terminal device 110-1. In some other embodiments, the capability may indicate a first time delay, which is a minimum time required to detect a fault in the AI / ML model at the terminal device 110-1 side. Thus, the network device 120 can configure the corresponding AI / ML model and associated activation parameters for the terminal device 110-1.

[0064] The terminal device 110-1 may be configured to have an AI / ML model and a first enabling parameter by a higher layer configuration (e.g., RRC signaling). The AI / ML model may include an index (ID) of the AI / ML model, structural parameters (e.g., DNN, CNN, number of layers, type of layer, number of neurons), and factors (e.g., weight factor, bias factor). The first enabling parameter is used to enable the terminal device 110-1 to perform a function of detecting an AI / ML model failure. If the terminal device 110-1 is not configured to have the first enabling parameter or the first enabling parameter is set to disabled, the terminal device 110-1 is not expected to perform detection of an AI / ML model failure.

[0065] In some embodiments, the terminal device 110-1 may also be configured to have a first enablement parameter and a first type of parameter. The first type of parameter may include a data format of an input / output of an AI / ML model, a pre-processing / post-processing parameter (e.g., a normalization coefficient). The AI / ML model may be associated with the first type of parameter. For example, the terminal device 110-1 is configured to have an AI / ML model and a corresponding first type of parameter.

[0066] The network device 120 triggers a CSI report for the terminal device 110-1 (3020). The CSI report may be a periodic CSI report configured by RRC, or a semi-persistent CSI report activated by MAC-CE, or an aperiodic CSI report or SP-CSI report triggered by DCI. The CSI report may refer to a higher layer configuration of CSI-ReportConfig.

[0067] The CSI report is associated with one or more CSI-RS (or SSB) resource sets 3030 with retransmission turned off. The type of CSI-RS resource set may be periodic, semi-persistent, or aperiodic. Each CSI-RS resource in the CSI-RS resource set corresponds to a beam. On the terminal device side, these CSI-RS resources are utilized to collect input / output data for detecting AI / ML model failure. For example, each CSI-RS resource may correspond to beams 131-1, 132-1, 133-1, and 134-1, respectively.

[0068] The CSI report may be associated with an AI / ML model. For example, an index of the AI / ML model may be configured in the higher layer configuration of the CSI report (e.g., CSI-ReportConfig).

[0069] The terminal device 110-1 reports the first information (3040). The first information may be a newly introduced reporting amount, i.e., the content reported by the terminal device 110-1. Specifically, the first information may be used to indicate whether or not the AI / ML model associated with the CSI report has failed. The first information report occupies a 1-bit field. "1" means "occurrence of AI / ML model failure" and "0" means "no occurrence of AI / ML model failure."

[0070] Alternatively, the reporting amount associated with the CSI report may be set to "CRI-RSRP" (or "SSBRI-RSRP", "CRI-SINR", "SSBRI-SINR"), and at the same time, the CSI report may be associated with a second enabling parameter. In this case, the second enabling parameter may be used to indicate that the associated CSI report is used to detect an AI / ML model failure. In particular, if an AI / ML model failure occurs, the terminal device 110-1 does not report any amount. In other words, the terminal device 110-1 does not forward the CSI report to the network device 120. At this time, if there is no other colliding CSI report in the time domain, the network device 120 will not receive any CSI report and will therefore consider that an AI / ML model failure has occurred. In other cases (i.e., if an AI / ML model failure does not occur), the terminal device 110-1 may report K (K>0) optimal beams and corresponding beam qualities, i.e., K CRIs and corresponding L1-RSRPs. Optionally, if an AI / ML model failure occurs, the value of the bit field corresponding to the CRI and / or L1-RSRP reported by the UE is “1111…” (i.e., all “1’s”).

[0071] In some embodiments, the CSI report may be associated with a first time offset 310. The first time offset 310 may be utilized to indicate on which slot the terminal device 110-1 will forward the reporting quantity. Specifically, the first time offset 310 may refer to a time offset according to the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Furthermore, the value of the first time offset 310 depends on the reported first time delay. Optionally, the first time offset 310 may be configured via RRC independently of the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Furthermore, if the first time offset 310 is configured (or the field of the first time offset is present), the terminal device 110-1 should ignore the value provided by the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList.

[0072] The terminal device 110-1 may determine the availability of the AI / ML model according to the following criteria: In this case, the terminal device 110-1 may report first information indicating whether an AI / ML model failure has occurred.

[0073] Criterion-1: The optimal beam inferred based on the AI / ML model is inconsistent with the actual optimal beam not based on the AI / ML model. Specifically, the "inferred optimal beam" means "the CRI corresponding to the inferred CSI-RS resource having the largest amount (e.g., L1-RSRP, L1-SINR)" and the "actual optimal beam" means the CRI corresponding to the actual CSI-RS resource having the largest amount. For example, the inferred optimal beam based on the AI / ML model may be beam 131-1, and the actual optimal beam not based on the AI / ML model may be beam 133-1. Criterion-2: The difference between the amount corresponding to the estimated optimal beam (e.g., L1-RSRP, L1-SINR) and the amount corresponding to the actual optimal beam is greater than a first threshold (set by RRC). For example, the difference between the L1-RSRP of beam 131-1 and the L1-RSRP of beam 133-1 may exceed a first threshold. Specifically, if criterion 1 or criterion 2 is met, the terminal device 110-1 may determine that an AI / ML model failure has occurred. Furthermore, the terminal device 110-1 may be configured to have a second threshold (configured by RRC). Assuming that the value of the second threshold is 3, if criterion 1 or criterion 2 is met three or more consecutive times, the AI / ML model is unavailable. Otherwise, the AI / ML model is available.

[0074] When multiple CSI reports are triggered by the network device 120 at the same time, these CSI reports may collide. For example, two CSI reports may be considered as colliding if the time occupancy of the physical channel scheduled to carry these CSI reports overlaps within at least one OFDM symbol and are transmitted on the same carrier. In this case, a priority should be defined for the CSI reports. For example, if a CSI report associated with the first information collides with another CSI report for beam (e.g., CRI-RSRP) or CSI acquisition (e.g., CRI-RI-PMI-CQI) and the time domain types corresponding to these CSI reports are the same (e.g., aperiodic), the terminal device 110-1 preferentially transmits the CSI report associated with (e.g., carrying) the first information. In this case, k=0 for the CSI report carrying the first information, and k=1, 2 for the CSI report carrying L1-RSRP or L1-SINR and the CSI report not carrying L1-RSRP, L1-SINR, or the first information, respectively, where "k" is a parameter used to calculate the priority value of the CSI report. Optionally, the values ​​of k corresponding to the CSI report carrying the first information, the CSI report carrying L1-RSRP or L1-SINR, and the CSI report not carrying L1-RSRP, L1-SINR, or the first information are also [1,0,2] or [2,0,1], respectively. That is, the CSI report carrying the first information has medium priority or the lowest priority.

[0075] FIG. 3B illustrates a process by which a fault in an AI / ML model is detected by network device 120.

[0076] As shown in FIG. 3B, the terminal device 110-1 reports the second information (3050). In this case, in some embodiments, the second information may be a new reporting amount. The second information may include the inferred optimal beam (e.g., beam 131-1), the actual optimal beam (e.g., beam 133-1), the corresponding beam quality, i.e., the CRI and corresponding amount corresponding to the estimated CSI-RS resource with the largest amount (e.g., L1-RSRP), and the CRI corresponding to the actual CSI-RS resource with the largest amount. In some embodiments, the reported L1-RSRP values ​​corresponding to the estimated optimal beam and the actual optimal beam may be defined by a 7-bit value in the range of [-140, -44] dBm with a step size of 1 dB. Furthermore, the estimated (or actual) optimal beam and the corresponding L1-RSRP may occupy the front bit field, and the other may occupy the remaining bit field. Optionally, the reporting amount may be set to "CRI-RSRP" (or "SSBRI-RSRP", "CRI-SINR", "SSBRI-SINR"), and at the same time, when CSI reporting is associated with a second enabled parameter, the terminal device 110-1 is not expected to report K optimal beams (reusing R15 / 16), and the terminal device 110-1 should report the inferred optimal beam, the actual optimal beam, and the corresponding beam quality.

[0077] FIG. 4 shows a process in which online training of an AI / ML model is performed by terminal device 110-1.

[0078] The terminal device 110-1 reports (4010) one or more capabilities of the terminal device 110-1 to the network device 120. The one or more capabilities at least indicate that the terminal device 110-1 supports AI / ML. In some embodiments, the capabilities may indicate a second time delay, which is a minimum time required for online training on the terminal device 110-1 side. In this way, it is possible to facilitate joint analysis of the AI / ML model.

[0079] The terminal device 110-1 may be configured to have an AI / ML model and a third enabling parameter. The third enabling parameter may be used to enable the terminal device 110-1 to execute an online training function for the AI / ML model. In this case, if the terminal device 110-1 is not configured to have the third enabling parameter or the third enabling parameter is set to disabled, the terminal device 110-1 is not expected to execute online training for the AI / ML model.

[0080] The network device 120 triggers a CSI report for the terminal device 110-1 (4020). The CSI report may be a periodic CSI report configured by RRC, or a semi-persistent CSI report activated by MAC-CE, or an aperiodic CSI report or SP-CSI report triggered by DCI. The CSI report may refer to a higher layer configuration of CSI-ReportConfig.

[0081] In some embodiments, a CSI report may be associated with a CSI-RS resource set 4030. Compared to a failure detection, a CSI report may be associated with a larger number of CSI-RS resource sets because more data is required for training an AI / ML model.

[0082] The terminal device 110-1 reports the trained AI / ML model (4040). For example, the CSI report may be associated with third information. In this case, the third information may be a newly introduced reporting amount. Specifically, the third information may include trained AI / ML model related parameters, for example, may mainly include the amount of change of the trained AI / ML model compared to the AI / ML model, for example, weighting factors and bias factors. Optionally, the terminal device 110-1 may not report the trained AI / ML model. For example, the reporting amount may be set to "CRI-RSRP", and the terminal device 110-1 may only report K optimal beams.

[0083] In some embodiments, the CSI report may be associated with a second time offset 410. The second time offset 410 may be utilized to indicate on which slot the terminal device 110-1 will forward the reporting quantity. Specifically, the second time offset 410 may refer to a time offset by the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Furthermore, the value of the second time offset 410 depends on the reported second time delay. Optionally, the second time offset 410 may be configured via RRC independently of the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Furthermore, if the second time offset 410 is configured (or the field of the second time offset is present), the terminal device 110-1 should ignore the value provided by the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList.

[0084] In some embodiments, a priority should be defined for the CSI reports. In some embodiments, the first information and the third information may coexist. The value of k corresponding to the CSI report carrying the first information, the CSI report carrying the third information, the CSI report carrying L1-RSRP or L1-SINR, and the CSI report not carrying L1-RSRP, L1-SINR, the first information, or the third information is [0,1,2,3], [2,3,0,1], [0,2,1,3], or [0,3,1,2].

[0085] 5 is a flow chart of an example method 500 according to an embodiment of the present disclosure. The method 500 may be implemented in any suitable device. For illustrative purposes only, the method 500 may be implemented in a terminal device 110-1 as shown in FIG.

[0086] In block 510, the terminal device 110-1 reports one or more capabilities of the terminal device 110-1 to the network device 120. The one or more capabilities at least indicate that the terminal device 110-1 supports an AI / ML model.

[0087] In some embodiments, the capability may indicate a capability to support AI / ML. In some embodiments, the capability may indicate a capability to support beam management based on AI / ML. Alternatively or additionally, the capability may indicate a capability to support beam prediction in the spatial domain based on AI / ML. Additionally, the capability may indicate that the terminal device 110-1 supports a capability to support online training. For example, fine-tuning is applicable to online training. It should be noted that other suitable methods are also applicable to online training. The capability may also indicate an index of an AI / ML model supported by the terminal device 110-1. In some other embodiments, the capability may indicate a first time delay, which is a minimum time required to detect a fault in the AI / ML model at the terminal device 110-1 side. Thus, the network device 120 can configure the corresponding AI / ML model and associated activation parameters for the terminal device 110-1.

[0088] In another embodiment, the capability may indicate a second time delay, which is a minimum time required for online training of the AI / ML model at the terminal device 110-1, thereby facilitating joint analysis of the AI / ML model.

[0089] In block 520, the terminal device 110-1 receives one or more configurations of the data processing model from the network device 120. The one or more configurations may be transmitted via higher layer signaling. For example, the one or more configurations may be transmitted via RRC signaling.

[0090] In some embodiments, the one or more configurations may include a first configuration. In this case, the first configuration may indicate an index of the AI / ML model. Additionally, the first configuration may indicate a first type of parameter of the AI / ML model. In this case, the first type of parameter may include a structural parameter of the AI / ML model. For example, the structural parameter may indicate a deep neural network (DNN) of the AI / ML model. Alternatively, the structural parameter may indicate a convolutional neural network (CNN) of the AI / ML model. The structural parameter may also indicate a number of layers of the AI / ML model. The structural parameter may indicate a type of layer of the AI / ML model. Additionally, the structural parameter may indicate a number of neurons of the AI / ML model. In some other embodiments, the first configuration may indicate a factor of the AI / ML model. For example, the factor may be a weight factor. Alternatively or additionally, the factor may be a bias factor.

[0091] Additionally or alternatively, the first configuration may indicate a first type of parameter. In other words, the terminal device 110-1 may be configured to have an AI / ML model and a corresponding first type of parameter. The first type of parameter may include a data format of an input of the AI / ML model. For example, the data format of the input may include a number of rows and a number of columns of the input data. Additionally, the data format of the input may include a unit of the input data. The data format of the input may include an interpretation of the input data.

[0092] Additionally, the first type of parameter may include a data format of the output of the AI / ML model. Similarly, the data format of the output may include a number of rows and columns of the output data. Additionally, the data format of the output may include a unit of the output data. The data format of the output may include an interpretation of the output data.

[0093] In some embodiments, the first type of parameters may include pre-processing parameters of the AI / ML model. The first type of parameters may also include post-processing parameters of the AI / ML model. For example, the first type of parameters may include a standardization factor. The term "standardization factor" may refer to performing a normalization operation on the input data to flatten the input and output data into the same distribution and accelerate the training network. For example, in the case of maximum-based normalization, all input data may be divided by a maximum value, which is the standardization factor.

[0094] Additionally, the first configuration may also include a first enabling parameter. The first enabling parameter may be used to enable the terminal device 110-1 to detect a fault in the AI / ML model. For example, if the first configuration does not include the first enabling parameter, the terminal device 110-1 may not perform detection of a fault in the AI / ML model. Alternatively, if the first enabling parameter is set to disabled (e.g., set to “0”), the terminal device 110-1 may not perform detection of a fault in the AI / ML model. In another embodiment, if the first enabling parameter is set to enabled (e.g., set to “1”), the terminal device 110-1 may perform detection of a fault in the AI / ML model. In this way, the terminal device may determine the AI / ML model and the executable behavior.

[0095] In another embodiment, the first setting may include a third enabling parameter. The third enabling parameter may be used to enable the terminal device 110-1 to perform online training of the AI / ML model. For example, if the first setting does not include the third enabling parameter, the terminal device 110-1 may not perform online training of the AI / ML model. Alternatively, if the third enabling parameter is set to disabled (e.g., set to “0”), the terminal device 110-1 may not perform online training of the AI / ML model. In another embodiment, if the third enabling parameter is set to enabled (e.g., set to “1”), the terminal device 110-1 may perform online training of the AI / ML model. In this way, the terminal device may perform online training of the AI / ML model.

[0096] In block 530, the terminal device 110-1 receives an indication to trigger a report for obtaining information associated with the data processing model. In some embodiments, the CSI reporting may be a periodic CSI reporting configurable by RRC signaling. Alternatively, the CSI reporting may be a semi-persistent CSI reporting. In this case, such CSI reporting may be activated by a media access control element (MAC CE) from the network device 120. In other embodiments, the CSI reporting may be an aperiodic CSI reporting or a SP-CSI reporting. In this case, the CSI reporting may be triggered by a downlink control information (DCI) from the network device 120.

[0097] In some embodiments, the terminal device 110-1 may receive a CSI reporting configuration. For example, the CSI reporting configuration may be transmitted via a higher layer configuration of CSI-ReportConfig. The CSI reporting configuration may indicate that the CSI reporting is associated with one or more CSI-RS or SSB resource sets. In some embodiments, the CSI-RS resource set may be periodic. Alternatively, the CSI-RS resource set may be semi-persistent. In other embodiments, the CSI-RS resource set may be aperiodic. In some embodiments, each CSI-RS resource in the CSI-RS resource set may correspond to a beam. The CSI reporting configuration may indicate an index of an AI / ML model, which means that the CSI reporting is associated with the AI / ML model. In this way, the terminal device can know which AI / ML model the CSI reporting applies to.

[0098] In some embodiments, terminal device 110-1 may receive a set of reference signals at terminal device 110-1, and network device 120 may transmit the set of reference signals based on the CSI-RS resource set.

[0099] In some embodiments, the terminal device 110-1 may measure the set of reference signals. The terminal device 110-1 may measure RSRP for the set of reference signals. For example, the terminal device 110-1 may measure CRI-RSRP based on the set of reference signals. Alternatively, SSBRI-RSRP may be measured. In some embodiments, the terminal device 110-1 may measure CRI SINR based on the set of reference signals. Alternatively, the terminal device 110-1 may measure SSBRI-SINR based on the set of reference signals.

[0100] The terminal device 110-1 may transmit a report to the network device 120. In some embodiments, the report may include information associated with the AI / ML model. For example, the report may include first information indicating whether the AI / ML model has failed. The first information may be a report amount. For example, the first information may occupy a bit field in the report. For illustration, when the first information indicates "1", it means that an AI / ML model failure has occurred. When the first information indicates "0", it means that an AI / ML model failure has not occurred. In this way, the terminal device can notify the network device of whether the AI / ML model has failed by reporting the first information.

[0101] In other embodiments, the report may indicate a measurement result. For example, the report may include one of CRI-RSRP, SSBRI-RSRP, CRI-SINR, or SSBRI-SINR. In some embodiments, the one or more configurations described above may include a second enabling parameter. The second enabling parameter may be used to indicate that the associated report is used to detect a failure in the AI / ML model. In this case, if a failure is detected in the AI / ML model, the terminal device 110-1 may not report any quantity, in other words, the terminal device 110-1 may not transmit a report. The transmission of the report may be skipped. At the same time, if there are no other conflicting reports in the time domain, the network device 120 may not receive the CSI report. In this case, the network device 120 may determine that an AI / ML model failure has occurred. Alternatively, if no failure is detected in the AI / ML model, the terminal device 110-1 may report K (K>0) optimal beams and corresponding beam qualities, i.e., K CRIs and corresponding L1-RSRPs, in the report. Thus, although the reporting quality is set to "CRI-RSRP" because the report is associated with the second enabling parameter, the terminal device still knows that the CSI report is used to detect the AI / ML model failure.

[0102] Alternatively, if a failure is detected in the AI / ML model, the report may include a bit field indicating a predetermined value, for example, if a failure of the AI / ML model occurs, the value of the bit field corresponding to the CRI and / or the L1-RSRP field in the report may be set to all "1".

[0103] In some embodiments, the one or more settings described above may include a second setting. The second setting may indicate a time offset. In this case, the terminal device 110-1 may transmit a report on a slot determined based on the time offset. In some embodiments, the time offset may depend on a first time delay that is a minimum time required to detect a failure in the AI / ML model in the terminal device 110-1. Alternatively, the time offset may depend on a second time delay that is a minimum time required for online training of the AI / ML model in the terminal device 110-1. The time offset may indicate on which slot the terminal device 110-1 forwards the report quantity. In some embodiments, the time offset may refer to a time offset according to a higher layer setting of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Alternatively, the time offset may be configured via an RRC configuration independent of the higher layer setting of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. In this case, if the first time offset is configured in the RRC configuration, the terminal device 110-1 may ignore the value provided by the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. In this case, the terminal device can know when to perform the report.

[0104] The terminal device 110-1 may determine that a failure has occurred on the AI / ML model if at least one condition is met. For example, the terminal device 110-1 may determine that a failure has occurred on the AI / ML model if a first target beam determined based on the AI / ML model differs from a second target beam determined based on measurements of the set of reference signals. Alternatively, the terminal device 110-1 may determine that a failure has occurred on the AI / ML model if a difference between a first amount of the first target beam and a second amount of the second target beam exceeds a first threshold. In another embodiment, the terminal device 110-1 may determine that a failure has occurred on the AI / ML model if the number of times the condition is met exceeds a second threshold. For example, the second threshold may be set via RRC signaling. In this case, the terminal device and the network device know under what conditions the AI / ML model occurs.

[0105] In some embodiments, the terminal device 110-1 may transmit multiple reports. In this case, each of the multiple reports may indicate a corresponding priority. For example, if other uplink information (e.g., another report) needs to be transmitted, the terminal device 110-1 may determine whether the other uplink information collides with the report. If the other uplink information collides with the report, the terminal device 110-1 may compare a first priority of the report with a second priority of the other uplink information. In this case, if the first priority is higher than the second priority, the terminal device 110-1 may transmit the report to the network device. The value of the first priority may be determined from a set of values. The value of the second priority may also be determined from the set of values. The set of values ​​may include 0, 1, and 2. It should be noted that the set of values ​​may also include other values. In this way, if multiple reports carrying different contents collide, the terminal device knows which CSI report to transmit first.

[0106] Alternatively, the network device 120 may determine whether a failure occurs on the AI / ML model. The second information may include inferred information and actual information. In this case, the inferred information includes at least one of an inferred beam determined based on the AI / ML model or a beam quality corresponding to the inferred beam. Additionally, the actual information includes at least one of an actual beam determined based on a measurement of a set of reference signals associated with the report or a beam quality corresponding to the actual beam. In some embodiments, the beam quality may be RSRP. Alternatively, the beam quality may be SINR. In some embodiments, the beam quality may be defined by a 7-bit value. The beam quality may be −140 dBm to −44 dBm with a step size of 1 dB. In some embodiments, a bit field corresponding to the inferred beam and the beam quality corresponding to the inferred beam precedes a bit field corresponding to the actual beam and the beam quality corresponding to the actual beam. Alternatively, a bit field corresponding to the inferred beam and the beam quality corresponding to the inferred beam follows a bit field corresponding to the actual beam and the beam quality corresponding to the actual beam.

[0107] In some embodiments, the report may be associated with third information. In this case, the third information may include a second type of parameter of the trained AI / ML model updated based on the AI / ML model. For example, the third information may mainly include the amount of change of the trained AI / ML model compared to the AI / ML model, such as weighting factors and bias factors. In this way, it is possible to facilitate joint analysis of the models on the network device side. Alternatively, the terminal device 110-1 may not report the trained AI / ML model. In this case, the terminal device 110-1 may only report the K optimal beams.

[0108] 6 is a flow chart of an example method 600 according to an embodiment of the present disclosure. The method 600 may be implemented in any suitable device. For illustrative purposes only, the method 600 may be implemented in the network device 120-1 as shown in FIG.

[0109] In block 610, the network device 120 receives a capability report from the terminal device 110-1, the capability report including one or more capabilities that at least indicate that the terminal device 110-1 supports an AI / ML model.

[0110] In some embodiments, the capability may indicate a capability to support AI / ML. In some embodiments, the capability may indicate a capability to support beam management based on AI / ML. Alternatively or additionally, the capability may indicate a capability to support beam prediction in the spatial domain based on AI / ML. Additionally, the capability may indicate that the terminal device 110-1 supports a capability to support online training. For example, fine-tuning is applicable to online training. It should be noted that other suitable methods are also applicable to online training. The capability may also indicate an index of an AI / ML model supported by the terminal device 110-1. In some other embodiments, the capability may indicate a first time delay, which is a minimum time required to detect a fault in the AI / ML model at the terminal device 110-1 side. Thus, the network device 120 can configure the corresponding AI / ML model and associated activation parameters for the terminal device 110-1.

[0111] In another embodiment, the capability may indicate a second time delay, which is a minimum time required for online training of the AI / ML model at the terminal device 110-1, thereby facilitating joint analysis of the AI / ML model.

[0112] In block 620, the network device 120 transmits one or more configurations of the data processing model to the terminal device 110-1. The configurations may be transmitted via higher layer signaling. For example, the configurations may be transmitted via RRC signaling.

[0113] In some embodiments, the one or more settings may include a first setting. In this case, the first setting may indicate an index of the AI / ML model. Additionally, the first setting may indicate a first type of parameter of the AI / ML model. In this case, the first type of parameter may include a structural parameter of the AI / ML model. For example, the structural parameter may indicate a deep neural network (DNN) of the AI / ML model. Alternatively, the structural parameter may indicate a convolutional neural network (CNN) of the AI / ML model. The structural parameter may also indicate a number of layers of the AI / ML model. The structural parameter may indicate a type of layer of the AI / ML model. Additionally, the structural parameter may indicate a number of neurons of the AI / ML model. In some other embodiments, the first type of parameter may include a factor of the AI / ML model. For example, the factor may be a weight factor. Alternatively or additionally, the factor may be a bias factor. Additionally or alternatively, the first setting may indicate the first type of parameter. In other words, the terminal device 110-1 may be configured to have an AI / ML model and corresponding first type parameters. The first type parameters may include a data format of an input of the AI / ML model. For example, the data format of the input may include a number of rows and a number of columns of the input data. Additionally, the data format of the input may include a unit of the input data. The data format of the input may include an interpretation of the input data.

[0114] Additionally, the first type of parameter may include a data format of the output of the AI / ML model. Similarly, the data format of the output may include a number of rows and columns of the output data. Additionally, the data format of the output may include a unit of the output data. The data format of the output may include an interpretation of the output data.

[0115] In some embodiments, the first type of parameters may include pre-processing parameters of the AI / ML model. The first type of parameters may also include post-processing parameters of the AI / ML model. For example, the first type of parameters may include a standardization factor. The term "standardization factor" may refer to performing a normalization operation on the input data to flatten the input and output data into the same distribution and accelerate the training network. For example, in the case of maximum-based normalization, all input data may be divided by a maximum value, which is the standardization factor.

[0116] Additionally, the first configuration may also include a first enabling parameter. The first enabling parameter may be used to enable the terminal device 110-1 to detect a fault in the AI / ML model. For example, if the first configuration does not include the first enabling parameter, the terminal device 110-1 may not perform detection of a fault in the AI / ML model. Alternatively, if the first enabling parameter is set to disabled (e.g., set to “0”), the terminal device 110-1 may not perform detection of a fault in the AI / ML model. In another embodiment, if the first enabling parameter is set to enabled (e.g., set to “1”), the terminal device 110-1 may perform detection of a fault in the AI / ML model. In this way, the terminal device may determine the AI / ML model and the executable behavior.

[0117] In another embodiment, the first setting may include a third enabling parameter. The third enabling parameter may be used to enable the terminal device 110-1 to perform online training of the AI / ML model. For example, if the first setting does not include the third enabling parameter, the terminal device 110-1 may not perform online training of the AI / ML model. Alternatively, if the third enabling parameter is set to disabled (e.g., set to “0”), the terminal device 110-1 may not perform online training of the AI / ML model. In another embodiment, if the third enabling parameter is set to enabled (e.g., set to “1”), the terminal device 110-1 may perform online training of the AI / ML model. In this way, the terminal device may perform online training of the AI / ML model.

[0118] In block 630, the network device 120 sends an indication to trigger a report for obtaining information associated with the data processing model. In some embodiments, the CSI report may be a periodic CSI report configurable by RRC signaling. Alternatively, the CSI report may be a semi-persistent CSI report. In this case, such CSI report may be activated by a media access control element (MAC CE) from the network device 120. In other embodiments, the CSI report may be an aperiodic CSI report or a SP-CSI report. In this case, the CSI report may be triggered by a downlink control information (DCI) from the network device 120.

[0119] In some embodiments, the network device 120 may transmit a configuration of the CSI report. For example, the configuration of the CSI report may be transmitted via an upper layer configuration of CSI-ReportConfig. The configuration of the CSI report may indicate that the CSI report is associated with one or more CSI-RS or SSB resource sets. In some embodiments, the CSI-RS resource set may be periodic. Alternatively, the CSI-RS resource set may be semi-persistent. In other embodiments, the CSI-RS resource set may be aperiodic. In some embodiments, each CSI-RS resource in the CSI-RS resource set may correspond to a beam. The configuration of the CSI report may indicate an index of an AI / ML model, which means that the CSI report is associated with an AI / ML model. In this way, the terminal device can know which AI / ML model the CSI report applies to.

[0120] The network device 120 may transmit a set of reference signals to the terminal device 110-1. The network device 120 may transmit the set of reference signals based on the CSI-RS resource set.

[0121] The network device 120 may receive a CSI report from the terminal device 110-1. In some embodiments, the CSI report may include first information indicating whether the AI / ML model has failed. The first information may be a report amount. For example, the first information may occupy a bit field in the CSI report. For illustration, when the first information indicates "1", it means that an AI / ML model failure has occurred. When the first information indicates "0", it means that an AI / ML model failure has not occurred. In this way, the terminal device can notify the network device of whether the AI / ML model has failed by reporting the first information.

[0122] In other embodiments, the CSI report may indicate a measurement result. For example, the CSI report may include one of CRI-RSRP, SSBRI-RSRP, CRI-SINR, or SSBRI-SINR. The CSI report may be associated with a second enablement parameter. The second enablement parameter may be used to indicate that the associated CSI report is used to detect a failure in the AI / ML model. In this case, if a failure is detected in the AI / ML model, the terminal device 110-1 may not report any quantity, in other words, the terminal device 110-1 may not transmit a CSI report. The transmission of the CSI report may be skipped. At the same time, if there are no other conflicting CSI reports in the time domain, the network device 120 may not receive any CSI report. In this case, the network device 120 may determine that an AI / ML model failure has occurred. Alternatively, if no failure is detected in the AI / ML model, the terminal device 110-1 may report K (K>0) optimal beams and corresponding beam qualities, i.e., K CRIs and corresponding L1-RSRPs, in the CSI report. Thus, since the CSI report is associated with the second enabling parameter, the terminal device also knows that the CSI report is used to detect the AI / ML model failure, even though the reporting quality is set to "CRI-RSRP".

[0123] Alternatively, if a failure is detected in the AI / ML model, the CSI report may include a bit field indicating a predetermined value. For example, if a failure occurs in the AI / ML model, the value of a bit field corresponding to the CRI and / or the L1-RSRP field in the CSI report may be set to all "1".

[0124] In some embodiments, the network device 120 may transmit a second configuration to the terminal device 110-1. The second configuration may indicate a time offset. In this case, the terminal device 110-1 may transmit a CSI report on a slot determined based on the time offset. In some embodiments, the time offset may depend on a first time delay that is a minimum time required to detect a failure in the AI / ML model in the terminal device 110-1. Alternatively, the time offset may depend on a second time delay that is a minimum time required for online training of the AI / ML model in the terminal device 110-1. The time offset may indicate on which slot the terminal device 110-1 forwards the report quantity. In some embodiments, the time offset may refer to a time offset according to a higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. Alternatively, the time offset may be configured via an RRC configuration independent of the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. In this case, if the first time offset is configured in the RRC configuration, the terminal device 110-1 may ignore the value provided by the higher layer configuration of CSI-ReportPeriodicityAndOffset or reportSlotOffsetList. In this case, the terminal device can know when to perform the report.

[0125] In some embodiments, the network device 120 may receive multiple CSI reports, where each of the multiple CSI reports may indicate a corresponding priority. The priority value may be determined from the set of values. The set of values ​​may include 0, 1, and 2. It should be noted that the set of values ​​may also include other values. In this way, in case of collision of multiple CSI reports carrying different content, the terminal device knows which CSI report to transmit first.

[0126] Alternatively, the network device 120 may determine whether a failure has occurred on the AI / ML model. In this case, a new report quantity, for example, second information, may be introduced. The second information may include inferred information and actual information. In this case, the inferred information includes at least one of an inferred beam determined based on the AI / ML model, or a beam quality corresponding to the inferred beam. Additionally, the actual information includes at least one of an actual beam determined based on measurements of a set of reference signals associated with the report, or a beam quality corresponding to the actual beam. In some embodiments, the beam quality may be RSRP. Alternatively, the beam quality may be SINR. In some embodiments, the beam quality may be defined by a 7-bit value. The beam quality may be −140 dBm to −44 dBm with a step size of 1 dB. In some embodiments, a bit field corresponding to the inferred beam and the beam quality corresponding to the inferred beam precedes a bit field corresponding to the actual beam and the beam quality corresponding to the actual beam. Alternatively, a bit field corresponding to the inferred beam and the beam quality corresponding to the inferred beam follows a bit field corresponding to the actual beam and the beam quality corresponding to the actual beam.

[0127] In some embodiments, the report may be associated with third information. In this case, the third information may include a second type of parameter of the trained AI / ML model updated based on the AI / ML model. For example, the third information may mainly include the amount of change of the trained AI / ML model compared to the AI / ML model, such as weighting factors and bias factors. In this way, it is possible to facilitate joint analysis of the models on the network device side. Alternatively, the terminal device 110-1 may not report the trained AI / ML model. In this case, the terminal device 110-1 may only report the K optimal beams.

[0128] In some embodiments, a terminal device comprises circuitry configured to report to a network device one or more capabilities of the terminal device, the one or more capabilities indicating at least that the terminal device supports a data processing model, receive from the network device one or more settings associated with the data processing model, and receive from the network device an instruction to trigger a report to obtain information associated with the data processing model.

[0129] In some embodiments, the one or more capabilities further indicate at least one of: a capability to support artificial intelligence (AI) or machine learning (ML), a capability to support beam management based on AI or ML, a capability to support beam prediction in the spatial domain based on AI or ML, a capability to support online training, an index of the data processing model, a first time delay that is the minimum time required to detect a fault in the data processing model in the terminal device, or a second time delay that is the minimum time required for online training of the data processing model in the terminal device.

[0130] In some embodiments, the one or more settings further indicate at least one of an index of the data processing model, a first type of parameter of the data processing model, a first enabling parameter used to enable the terminal device to detect a fault in the data processing model, a second enabling parameter used to enable the terminal device to perform online training, or the time offset used to indicate the terminal device to transmit the report on a slot determined according to a time offset.

[0131] In some embodiments, the first type of parameters include at least one of: structural parameters of the data processing model, weight factors of the data processing model, bias factors of the data processing model, a data format of an input of the data processing model, a data format of an output of the data processing model, pre-processing parameters of the data processing model, or post-processing parameters of the data processing model.

[0132] In some embodiments, the time offset is determined according to a first time delay or a second time delay, the first time delay being the minimum time required to detect a fault in the data processing model in the terminal device, and the second time delay being the minimum time required for online training of the data processing model in the terminal device.

[0133] In some embodiments, the terminal device comprises circuitry configured to, pursuant to a determination that the terminal device is configured to have the time offset, cause another time offset indicated in another configuration to be ignored.

[0134] In some embodiments, the information associated with the data processing model includes one of first information indicating whether a fault has occurred in the data processing model, second information including inferred information and actual information, or third information indicating a second type of parameter of a trained data processing model updated based on the data processing model.

[0135] In some embodiments, the inferred information includes at least one of an inferred beam determined based on the data processing model, or a beam quality corresponding to the inferred beam, and the actual information includes at least one of an actual beam determined based on measurements of a set of reference signals associated with the report, or a beam quality corresponding to the actual beam.

[0136] In some embodiments, the beam quality includes at least one of: a reference signal received power (RSRP) or a signal-to-interference-and-noise ratio (SINR).

[0137] In some embodiments, the beam quality corresponding to the inferred beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB, and the beam quality corresponding to the actual beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB.

[0138] In some embodiments, a bit field corresponding to the inferred beam and the beam quality corresponding to the inferred beam precedes or follows a bit field corresponding to the actual beam and the beam quality corresponding to the actual beam.

[0139] In some embodiments, the second type of parameters includes at least one of: structural parameters of the trained data processing model, weighting factors of the trained data processing model, or bias factors of the trained data processing model.

[0140] In some embodiments, the terminal device comprises circuitry configured to transmit the report to the network device to obtain the information associated with the data processing model.

[0141] In some embodiments, the terminal device comprises circuitry configured to transmit the report to the network device pursuant to a determination that a fault has been detected in the data processing model, a bit field corresponding to the report being set to a predetermined number.

[0142] In some embodiments, the terminal device comprises circuitry, the circuitry configured to cause skipping transmission of the report in accordance with a determination that a fault has been detected in the data processing model.

[0143] In some embodiments, the terminal device comprises circuitry configured to: determine whether second uplink information conflicts with the report in accordance with a determination that second uplink information needs to be transmitted; compare a first priority of the report with a second priority of the second uplink information in accordance with a determination that the second uplink information conflicts with the report; and transmit the report to the network device in accordance with a determination that the first priority is higher than the second priority.

[0144] In some embodiments, the first priority and the second priority are determined from a set of values ​​including 0, 1, or 2.

[0145] In some embodiments, the terminal device comprises circuitry, the circuitry configured to determine that a fault has occurred on the data processing model in accordance with determining that at least one condition is satisfied.

[0146] In some embodiments, the at least one condition includes one of a first condition in which a first target beam determined based on the data processing model is different from a second target beam determined based on measurements of a set of reference signals associated with the report, a second condition in which a difference between a beam amount of the first target beam and a beam amount of the second target beam exceeds a first threshold, or a third condition in which the number of times the first condition or the second condition is satisfied exceeds a second threshold.

[0147] In some embodiments, the reporting includes at least one of a periodic channel state information (CSI) report, a semi-persistent CSI report, or an aperiodic CSI report.

[0148] In some embodiments, a network device comprises circuitry configured to receive from a terminal device a capability report including one or more capabilities of the terminal device, the one or more capabilities indicating at least that the terminal device supports a data processing model, send to the terminal device one or more settings associated with the data processing model, and send to the terminal device an instruction to trigger a report to obtain information associated with the data processing model.

[0149] In some embodiments, the one or more capabilities further indicate at least one of: a capability to support artificial intelligence (AI) or machine learning (ML), a capability to support beam management based on AI or ML, a capability to support beam prediction in the spatial domain based on AI or ML, a capability to support online training, an index of the data processing model, a first time delay that is the minimum time required to detect a fault in the data processing model in the terminal device, or a second time delay that is the minimum time required for online training of the data processing model in the terminal device.

[0150] In some embodiments, the one or more settings further indicate at least one of an index of the data processing model, a first type of parameter of the data processing model, a first enabling parameter used to enable the terminal device to detect a fault in the data processing model, a second enabling parameter used to enable the terminal device to perform online training, or the time offset used to indicate the terminal device to transmit the report on a slot determined according to a time offset.

[0151] In some embodiments, the first type of parameters include at least one of: structural parameters of the data processing model, weight factors of the data processing model, bias factors of the data processing model, a data format of an input of the data processing model, a data format of an output of the data processing model, pre-processing parameters of the data processing model, or post-processing parameters of the data processing model.

[0152] In some embodiments, the time offset is determined according to a first time delay or a second time delay, the first time delay being the minimum time required to detect a fault in the data processing model in the terminal device, and the second time delay being the minimum time required for online training of the data processing model in the terminal device.

[0153] In some embodiments, the report is associated with the one or more settings associated with the data processing model.

[0154] In some embodiments, the information associated with the data processing model includes one of first information indicating whether a fault has occurred in the data processing model, second information including inferred information and actual information, or third information indicating a second type of parameter of a trained data processing model updated based on the data processing model.

[0155] In some embodiments, the inferred information includes at least one of an inferred beam determined based on the data processing model, or a beam quality corresponding to the inferred beam, and the actual information includes at least one of an actual beam determined based on measurements of a set of reference signals associated with the report, or a beam quality corresponding to the actual beam.

[0156] In some embodiments, the beam quality includes at least one of: a reference signal received power (RSRP) or a signal-to-interference-and-noise ratio (SINR).

[0157] In some embodiments, the beam quality corresponding to the inferred beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB, and the beam quality corresponding to the actual beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB.

[0158] In some embodiments, a bit field corresponding to the inferred beam and the beam quality corresponding to the inferred beam precedes or follows a bit field corresponding to the actual beam and the beam quality corresponding to the actual beam.

[0159] In some embodiments, the second type of parameters includes at least one of: structural parameters of the trained data processing model, weighting factors of the trained data processing model, or bias factors of the trained data processing model.

[0160] In some embodiments, the network device comprises circuitry, the circuitry configured to receive the report from the terminal device to obtain the information associated with the data processing model.

[0161] In some embodiments, the network device comprises circuitry configured to transmit the report to the network device pursuant to a determination that a fault has been detected in the data processing model, a bit field corresponding to the report being set to a predetermined number.

[0162] 7 is a schematic block diagram of an apparatus 700 suitable for implementing embodiments of the present disclosure. The apparatus 700 may be considered as another exemplary implementation of the terminal device 110 as shown in FIG. 1. Thus, the apparatus 700 may be implemented in or at least as part of the terminal device 110. Alternatively, the apparatus 700 may be considered as another exemplary implementation of the network device 120 as shown in FIG. 1. Thus, the apparatus 700 may be implemented in or at least as part of the network device 120.

[0163] As shown, the apparatus 700 comprises a processor 710, a memory 720 coupled to the processor 710, a suitable transmitter (TX) and receiver (RX) 740 coupled to the processor 710, and a communication interface coupled to the TX / RX 740. The memory 720 stores at least a portion of a program 730. The TX / RX 740 is used for bidirectional communication. The TX / RX 740 has at least one antenna to facilitate communication, although the access nodes referred to herein may in practice have multiple antennas. The communication interface may represent any interface required for communication with other network elements, such as an X2 interface for bidirectional communication between eNBs, an S1 interface for communication between a mobility management entity (MME) / serving gateway (S-GW) and an eNB, an Un interface for communication between an eNB and a relay node (RN), or an Uu interface for communication between an eNB and a terminal device.

[0164] The program 730 is assumed to include program instructions that, when executed by an associated processor 710, enable the device 700 to operate according to embodiments of the present disclosure, as described herein with reference to Figures 2-6. The embodiments of the present disclosure may be implemented by computer software executable by the processor 710 of the device 700, or by hardware, or by a combination of software and hardware. The processor 710 may be configured to implement various embodiments of the present disclosure. Furthermore, the combination of the processor 710 and the memory 720 may form a processing means 750 suitable for implementing various embodiments of the present disclosure.

[0165] The memory 720 may be of any type suitable for a local technology network and may be implemented using any suitable data storage technology, such as, by way of non-limiting example, non-transitory computer-readable storage media, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. Although only one memory 720 is shown in the device 700, there may be several physically different memory modules in the device 700. The processor 710 may be of any type suitable for a local technology network and may include, by way of non-limiting example, one or more of a general purpose computer, a special purpose computer, a microprocessor, a digital signal processor (DSP) and a processor based on a multi-core processor architecture. The device 700 may have multiple processors, for example application specific integrated circuit chips time-slaved to a clock that synchronizes the main processor.

[0166] Overall, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure have been illustrated and described using block diagrams, flow charts, or some other pictorial representations, it should be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented, by way of non-limiting examples, in hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing device, or any combination thereof.

[0167] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in a program module, that execute in a device on a target real or virtual processor to perform the process or method described above with reference to Figures 2-6. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functionality of the program modules may be combined or split between program modules as appropriate. The machine-executable instructions of the program modules may be executed in local or distributed devices. In a distributed device, the program modules may be located in both local and remote storage media.

[0168] Program codes for carrying out the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, and when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may run entirely on the machine, partially on the machine, as a separate software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0169] The above program code may be implemented on a machine-readable medium, which may be any tangible medium that can contain or store a program used by or associated with an instruction execution system, device, or apparatus. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the aforementioned media. More specific examples of the machine-readable storage medium may include an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the aforementioned.

[0170] It should be noted that although operations have been described in a particular order, it should not be understood that such operations need to be performed in the particular order shown, or in any sequential order, or that all of the operations described must be performed in order to achieve desired results. In some cases, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure, but rather as descriptions of features that may be specific to certain embodiments. Some features described in the context of individual embodiments may be realized in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may be implemented separately in multiple embodiments or in any suitable subcombination.

[0171] Although the present disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the present disclosure, as defined in the appended claims, is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[0172] As used herein, the term "terminal device" refers to any device having wireless or wired communication capabilities. Examples of terminal devices include user equipment (UE), personal computers, desktops, mobile phones, cellular phones, smartphones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, Internet of Things (Iota) devices, Ultra-Reliable Low Latency Communications (URLLC) devices, Internet of Anything (Iowa) devices, machine type communication (MTC) devices, vehicle-mounted devices for V2X communications where X stands for pedestrian, vehicle, or infrastructure / network, devices for integrated access and integrated access and backhaul (IAB), satellite-based or airborne vehicles in non-terrestrial networks (NTN) including High Altitude Platforms (HAPs) including satellites and Unmanned Aircraft Systems (UASs), extended reality (XR) devices including different types of reality such as augmented reality (AR), mixed reality (MR), virtual reality (VR), unmanned aerial vehicles (UAVs), which are aircraft without a human pilot and commonly referred to as drones, high speed trains (HSTs), and other types of real-world applications. These include, but are not limited to, devices on a train, image capture devices such as digital cameras, sensor game devices, music storage and playback devices, or Internet appliances that allow wireless or wired Internet access and browsing. The "Terminal Device" may further have "multicast / broadcast" capabilities to support public safety and mission-critical V2X applications, transparent IPv4 / IPv6 multicast distribution, IPTV, smart TV, wireless services, over-the-air software distribution, group communication, and Iota applications. It may also incorporate one or more Subscriber Identity Modules (SIMs), known as multi-SIMs.The term "terminal equipment" may be used interchangeably with UE, mobile station, subscriber station, mobile terminal, user terminal, or wireless equipment.

[0173] The term "network device" refers to a device capable of providing or hosting a cell or coverage in which a terminal device can communicate. Examples of network devices include, but are not limited to, a Node B (node ​​or NB), an evolved node (node ​​or eNB), a next generation node (gNB), a transmit / receive point (TRP), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), an IAB node, a femto node, a pico node, a low power node such as a reconfigurable intelligent surface (RIS), etc.

[0174] The terminal device or network device may have the capability of artificial intelligence (AI) or machine learning. Generally, it includes a model trained from a large amount of data collected for a specific function and can be used to predict some information.

[0175] The terminal device or network device may operate on several frequency ranges, such as FR1 (410 MHz-7125 MHz), FR2 (24.25 GHz-71 GHz), frequency bands greater than 100 GHz, and Tera Hertz (THz). It can also operate on licensed / unlicensed / shared spectrum. The terminal device may have two or more connections with the network device under Multi-Radio Dual Connectivity (MR-DC) application scenarios. The terminal device or network device can operate in full duplex, flexible duplex, and cross division duplex modes.

[0176] Embodiments of the present disclosure may be implemented in test equipment, such as, for example, a signal generator, a signal analyzer, a spectrum analyzer, a network analyzer, a test terminal equipment, a test network equipment, a channel emulator, and the like.

[0177] Embodiments of the present disclosure may be performed in accordance with any generation of communication protocols now known or developed in the future, including, but not limited to, first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G) communication protocols, 5.5G, 5G-Advanced networks, or sixth generation (6G) networks.

Claims

1. 1. A method of communication comprising: reporting, in a terminal device, to a network device, one or more capabilities of the terminal device, the one or more capabilities at least indicating that the terminal device supports a data processing model; receiving from the network device one or more settings associated with the data processing model; receiving an instruction from the network device to trigger a report to obtain information associated with the data processing model; The method includes:

2. The one or more capabilities include: Ability to support artificial intelligence (AI) or machine learning (ML); Ability to support AI or ML based beam management; Ability to support beam prediction in the spatial domain based on AI or ML; the ability to support online training; an index of said data processing model; a first time delay, which is a minimum time required to detect a fault in the data processing model in the terminal device; or a second time delay, which is the minimum time required for online training of the data processing model at the terminal device; Further indicating at least one of The method of claim 1.

3. The one or more settings include: an index of said data processing model; a first type of parameter of the data processing model; a first enabling parameter used to enable the terminal device to detect a fault in the data processing model; A second enabling parameter used to enable the terminal device to perform online training; or the time offset used to indicate to the terminal device to transmit the report on a slot determined according to the time offset; Further indicating at least one of The method of claim 1.

4. The first type of parameter is: Structural parameters of the data processing model; a weighting factor for said data processing model; A bias factor for said data processing model; A data format of an input of the data processing model; a data format of the output of said data processing model; pre-processing parameters of the data processing model; or post-processing parameters of said data processing model; Contains at least one of The method according to claim 3.

5. The time offset is determined according to a first time delay or a second time delay, the first time delay being a minimum time required for the terminal device to detect a fault in the data processing model, and the second time delay being a minimum time required for the terminal device to perform online training of the data processing model. The method according to claim 3.

6. ignoring another time offset indicated in another configuration in response to determining that the terminal device is configured with the time offset. Also includes The method according to claim 3.

7. The information associated with the data processing model includes: First information indicating whether a fault has occurred in the data processing model; Second information including inferred information and actual information; or Third information indicative of a second type of parameter of a trained data processing model updated based on the data processing model; Contains one of The method of claim 1.

8. the inferred information includes at least one of an inferred beam determined based on the data processing model or a beam quality corresponding to the inferred beam; The actual information includes at least one of an actual beam determined based on measurements of a set of reference signals associated with the report, or a beam quality corresponding to the actual beam. The method according to claim 7.

9. The beam quality includes at least one of a reference signal received power (RSRP) or a signal to interference plus noise ratio (SINR). The method according to claim 8.

10. The beam quality corresponding to the inferred beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB, and the beam quality corresponding to the actual beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB. The method according to claim 8.

11. a bit field corresponding to the inferred beam and a beam quality corresponding to the inferred beam precedes or follows a bit field corresponding to the actual beam and a beam quality corresponding to the actual beam The method according to claim 8.

12. The second type of parameters includes at least one of: structural parameters of the trained data processing model, weight factors of the trained data processing model, or bias factors of the trained data processing model. The method according to claim 7.

13. sending the report to the network device to obtain the information associated with the data processing model; The method of claim 1 further comprising:

14. and transmitting the report to the network device in response to a determination that a fault has been detected in the data processing model, wherein a bit field corresponding to the report is set to a predetermined number. The method of claim 13.

15. skipping transmission of said report in accordance with a determination that a fault has been detected in said data processing model. The method of claim 1 further comprising:

16. determining whether the second uplink information conflicts with the report in response to determining that second uplink information needs to be transmitted; pursuant to a determination that the second uplink information conflicts with the report, comparing a first priority of the report with a second priority of the second uplink information; transmitting the report to the network device in accordance with a determination that the first priority is higher than the second priority; The method of claim 13 further comprising:

17. The first priority and the second priority are determined from a set of values ​​including 0, 1, or 2.

17. The method of claim 16.

18. determining that a fault has occurred in the data processing model in accordance with determining that at least one condition has been satisfied; The method of any one of claims 1 to 17, further comprising:

19. The at least one condition is: a first condition in which a first target beam determined based on the data processing model and a second target beam determined based on measurements of a set of reference signals associated with the report are different; a second condition in which a difference between the beam amount of the first target beam and the beam amount of the second target beam exceeds a first threshold; or a third condition in which the number of times the first condition or the second condition is satisfied exceeds a second threshold; Contains one of 20. The method of claim 18.

20. The reporting includes at least one of a periodic channel state information (CSI) report, a semi-persistent CSI report, or an aperiodic CSI report. The method of claim 1.

21. 1. A method of communication comprising: receiving, in a network device, a capability report from a terminal device, the capability report including one or more capabilities of the terminal device, the one or more capabilities indicating at least that the terminal device supports a data processing model; transmitting to the terminal device one or more settings associated with the data processing model; sending an instruction to the terminal device to trigger a report for obtaining information associated with the data processing model; The method includes:

22. The one or more capabilities include: Ability to support artificial intelligence (AI) or machine learning (ML); Ability to support AI or ML based beam management; Ability to support beam prediction in the spatial domain based on AI or ML; the ability to support online training; an index of said data processing model; a first time delay, which is a minimum time required to detect a fault in the data processing model in the terminal device; or a second time delay, which is the minimum time required for online training of the data processing model at the terminal device; Further indicating at least one of 22. The method of claim 21.

23. The one or more settings include: an index of said data processing model; a first type of parameter of the data processing model; a first enabling parameter used to enable the terminal device to detect a fault in the data processing model; A second enabling parameter used to enable the terminal device to perform online training; or the time offset used to indicate to the terminal device to transmit the report on a slot determined according to the time offset; Further indicating at least one of 22. The method of claim 21.

24. The first type of parameter is: Structural parameters of the data processing model; a weighting factor for said data processing model; A bias factor for said data processing model; A data format of an input of the data processing model; a data format of the output of said data processing model; pre-processing parameters of the data processing model; or Post-processing parameters of the data processing model Contains at least one of 24. The method of claim 23.

25. The time offset is determined according to a first time delay or a second time delay, the first time delay being a minimum time required for the terminal device to detect a fault in the data processing model, and the second time delay being a minimum time required for the terminal device to perform online training of the data processing model.

24. The method of claim 23.

26. The report is associated with the one or more settings associated with the data processing model.

22. The method of claim 21.

27. The information associated with the data processing model includes: First information indicating whether a fault has occurred in the data processing model; Second information including inferred information and actual information; or Third information indicative of a second type of parameter of a trained data processing model updated based on the data processing model; Contains one of 22. The method of claim 21.

28. the inferred information includes at least one of an inferred beam determined based on the data processing model or a beam quality corresponding to the inferred beam; The actual information includes at least one of an actual beam determined based on measurements of a set of reference signals associated with the report, or a beam quality corresponding to the actual beam.

28. The method of claim 27.

29. The beam quality includes at least one of a reference signal received power (RSRP) or a signal to interference plus noise ratio (SINR).

29. The method of claim 28.

30. The beam quality corresponding to the inferred beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB, and the beam quality corresponding to the actual beam is defined by a 7-bit value in the range of -140 to -44 dBm with a step size of 1 dB.

29. The method of claim 28.

31. a bit field corresponding to the inferred beam and a beam quality corresponding to the inferred beam precedes or follows a bit field corresponding to the actual beam and a beam quality corresponding to the actual beam 29. The method of claim 28.

32. The second type of parameters includes at least one of: structural parameters of the trained data processing model, weight factors of the trained data processing model, or bias factors of the trained data processing model.

28. The method of claim 27.

33. receiving the report from the terminal device to obtain the information associated with the data processing model; 22. The method of claim 21 further comprising:

34. and transmitting the report to the network device in response to a determination that a fault has been detected in the data processing model, wherein a bit field corresponding to the report is set to a predetermined number.

34. The method of claim 33.

35. A terminal device comprising a processor and a memory coupled to the processor and storing instructions, The instructions, when executed by the processor, perform operations including the method of any one of claims 1 to 20. Terminal device.

36. 1. A network device comprising: a processor; and a memory coupled to the processor and storing instructions, The instructions, when executed by the processor, perform operations including the method of any one of claims 21 to 34. Network device.

37. storing instructions which, when executed on at least one processor, cause said at least one processor to carry out a method according to any one of claims 1 to 20 or any one of claims 21 to 34; Computer-readable medium.

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