Communication methods and communication devices

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 receives downlink information for triggering a report from a network device. The terminal device determines whether an estimation failure occurs in a data processing model. The terminal device transmits a report to the network device. The report indicates whether an estimation failure occurs in the data processing model and / or whether information in the report is generated based on the data processing model. In this way, the network device can be informed of the estimation failure and the accuracy of information transmitted from the terminal device can be improved.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of telecommunications, and more particularly, to methods, apparatuses, and computer-readable media for communication.

Background Art

[0002] To improve communication performance, several techniques have been proposed. For example, a communication device can utilize an artificial intelligence / machine learning (AI / ML) model to improve communication quality. The AI / ML model can be applied to various scenarios to achieve better performance.

Summary of the Invention

Problems to be Solved by the Invention

[0003] Generally, exemplary embodiments of the present disclosure provide solutions for communication.

Means for Solving the Problems

[0004] In a first aspect, a communication method is provided. The communication method includes receiving, at a terminal device, downlink information for triggering a report from a network device; determining, at the terminal device, whether an estimation failure has occurred in a data processing model; and transmitting the report to the network device, where the report indicates at least whether an estimation failure has occurred in the data processing model.

[0005] In a second aspect, a communication method is provided. The communication method includes transmitting, at a network device, downlink information for triggering a report to a terminal device; and receiving the report from the terminal device, where the report indicates at least whether an estimation failure has occurred in a data processing model.

[0006] In a third aspect, a terminal device is provided. The terminal device includes a processor unit and a memory coupled to the processor unit in which instructions are stored. When the instructions are executed by the processor unit, they cause the terminal device to perform an operation. The operation includes receiving downlink information from a network device to trigger a report, determining in the terminal device whether an estimation failure has occurred in the data processing model, and sending the report to the network device. The report indicates at least whether an estimation failure has occurred in the data processing model.

[0007] In a fourth aspect, a network device is provided. The network device includes a processor unit and a memory coupled to the processor unit in which instructions are stored. When the instructions are executed by the processor unit, they cause the network device to perform an action. The action includes sending downlink information to a terminal device to trigger a report and receiving a report from the terminal device. The report indicates at least whether an estimation failure occurred in the data processing model.

[0008] In a fifth aspect, a computer-readable medium is provided on which instructions are stored. When the instructions are executed on at least one processor, they cause at least one processor to perform the method according to the first or second aspect.

[0009] Other features of this disclosure should be easily understood through the following description. [Brief explanation of the drawing]

[0010] The accompanying drawings will provide a more detailed description of some exemplary embodiments of this disclosure, which should make the above and other objectives, features, and advantages of this disclosure clearer.

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

[0012] [Figure 2] Shows the signaling flow of communication according to some embodiments of the present disclosure.

[0013] [Figure 3A] Respectively show schematic diagrams of AI / ML-based beam management according to some embodiments of the present disclosure. [Figure 3B] Respectively show schematic diagrams of AI / ML-based beam management according to some embodiments of the present disclosure. [Figure 3C] Respectively show schematic diagrams of AI / ML-based beam management according to some embodiments of the present disclosure. [Figure 3D] Respectively show schematic diagrams of AI / ML-based beam management according to some embodiments of the present disclosure. [Figure 3E] Respectively show schematic diagrams of AI / ML-based beam management according to some embodiments of the present disclosure.

[0014] [Figure 4A] Respectively show schematic diagrams of AI / ML-based channel state information (CSI) feedback according to some embodiments of the present disclosure. [Figure 4B] Respectively show schematic diagrams of AI / ML-based channel state information (CSI) feedback according to some embodiments of the present disclosure.

[0015] [Figure 5] Shows a schematic diagram of AI / ML-based demodulation reference signal (DMRS) according to some embodiments of the present disclosure.

[0016] [Figure 6] Shows a schematic diagram of AI / ML-based demodulated CSI-RS according to some embodiments of the present disclosure.

[0017] [Figure 7] Is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0018] [Figure 8] This is a flowchart illustrating an exemplary method according to an embodiment of the present disclosure.

[0019] [Figure 9] This is a schematic block diagram of an apparatus suitable for carrying out the embodiments of the present disclosure.

[0020] Throughout all drawings, the same or similar reference numbers represent the same or similar elements. [Modes for carrying out the invention]

[0021] The principles of this disclosure will be described with reference to several exemplary embodiments. These embodiments are described for illustrative purposes only and should be understood as helpful to those skilled in the art in understanding and implementing this disclosure, and should not be considered as limiting the scope of this disclosure. The disclosure described herein can be implemented in various ways other than those described below.

[0022] In the following description and claims, unless otherwise defined, all technical and scientific terms used have the same meaning as those commonly understood by those skilled in the art to which this disclosure pertains.

[0023] In this specification, the term "terminal device" refers to any device having wireless or wired communication capabilities. Examples of terminal devices include user terminals (UEs), personal computers, desktops, mobile phones, cell phones, smartphones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, IoT (Internet of Things) devices, ultra-high reliability low latency (URLLC) devices, IoE (Internet of Everything) devices, machine-type communication (MTC) equipment, vehicle-mounted equipment for V2X communication (where X means pedestrian, vehicle, or infrastructure / network), IAB (Integrated Access and Backhaul) devices, spacecraft or aircraft in non-terrestrial networks (NTN) including HAP (High Altitude Platforms) encompassing unmanned aerial vehicle systems (UAS), and XR (Extended Reality) including different types of reality such as augmented reality (AR), mixed reality (MR), and virtual reality (VR). Examples of "terminal devices" include, but are not limited to, Reality devices, unmanned aerial vehicles (UAVs) that do not require a human pilot, commonly known as drones, devices on high-speed trains (HSTs), imaging devices such as digital cameras, sensors, gaming devices, music storage and playback devices, or internet devices that enable wireless / wired internet access and browsing. "Terminal devices" may also have multicast / broadcast capabilities and support public safety and mission-critical, V2X applications, transparent IPv4 / IPv6 multicast distribution, IPTV, smart TV, wireless services, wireless software distribution, group communications, and IoT applications. They may also incorporate one or more subscriber identification modules (SIMs), as is known as multi-SIM. The term "terminal device" can be used interchangeably with UE, mobile station, subscriber equipment, mobile terminal, user terminal, or wireless device. In the following description, the terms "terminal device," "communication device," "terminal," "user terminal," and "UE" may be used interchangeably.

[0024] Terminal devices or network devices may have artificial intelligence (AL) or machine learning capabilities. Generally, this includes models that can be used to predict certain information by learning from a large amount of data collected for a specific function.

[0025] Terminal or network devices may operate in multiple frequency ranges, such as FR1 (410 MHz to 7125 MHz), FR2 (24.25 GHz to 71 GHz), frequency bands above 100 GHz, and terahertz (THz). Furthermore, they can operate in licensed / unlicensed / shared spectrum. In multi-radio dual connectivity (MR-DC) application scenarios, terminal devices may have multiple connections to network devices. Terminal or network devices can operate in full-duplex, flexible-duplex, and cross-division duplex modes.

[0026] The term "network device" refers to a device that can provide or host a cell or coverage from which terminal devices can communicate. Examples of network devices include, but are not limited to, Node B (NodeB or NB), Evolved Node B (eNodeB or eNB), Next Generation Node B (gNB), Transmit / Receive Point (TRP), Remote Radio Unit (RRU), Radio Head (RH), Remote Radio Head (RRH), IAB nodes, femtonodes, piconodes and other low-power nodes, and RIS (Reconfigurable Intelligent Surface).

[0027] In one embodiment, the terminal device may be connected to a first network device and a second network device. One of the first and second network devices may be a master node and the other a secondary node. The first and second network devices 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 related to different RATs may be transmitted to the terminal device from at least one of the first and second network devices. In one embodiment, first information may be transmitted from the first network device to the terminal device, and second information may be transmitted directly from the second network device to the terminal device or via the first network device. In one embodiment, information related to the configuration of the terminal device set by the second network device may be transmitted from the second network device via the first network device. Information related to the reconfiguration of the terminal device set by the second network device may be transmitted directly from the second network device to the terminal device or via the first network device.

[0028] The communications discussed herein may conform to any appropriate standard, including, but are 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, and Global System for Mobile Communications (GSM). Furthermore, communications may be performed in accordance with any generation of communication protocol currently known or to be 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 technologies described herein may be used not only with the wireless networks and technologies described above, but also with other wireless networks and technologies. Embodiments of this disclosure may be implemented in accordance with any generation of communication protocols that are currently known or will be 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 Network, or sixth-generation (6G) networks.

[0029] As used herein, the term “circuit” may mean a hardware circuit and / or a combination of a hardware circuit and software. For example, a circuit may be a combination of an analog hardware circuit and / or a digital hardware circuit and software / firmware. As a further example, a circuit may be any part of a software-equipped hardware processor, such as a digital signal processor, software, and memory, which work together to enable a device such as a terminal or network device to perform various functions. In yet another example, a circuit may be a hardware circuit and / or processor such as a microprocessor or a part of a microprocessor that requires software / firmware for operation, but where the software may not be present when not needed for operation. As used herein, the term “circuit” also encompasses a mere hardware circuit or processor, or a part of a hardware circuit or processor, and the implementation of its (or their) accompanying software and / or firmware.

[0030] As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural unless otherwise clearly indicated in the context. The term “including” and its variations are interpreted as an open term meaning “including but not limited to.” The term “based on” is interpreted as “at least partially based on.” The terms “one embodiment” and “one embodiment” are interpreted as “at least one embodiment.” The term “another embodiment” is interpreted as “at least one other embodiment.” Terms such as “first,” “second,” etc., may refer to different or the same subject. The following may include other explicit and implicit definitions.

[0031] In some examples, values, procedures, or devices are referred to as "optimal," "lowest," "highest," "minimum," "maximum," etc. It is understood that such descriptions are intended to indicate that a choice is available from among several functional alternatives, and that such a choice does not necessarily have to be superior, smaller, more expensive, or more preferable than the other choices.

[0032] As described above, AI / ML models can be applied to various scenarios to achieve better performance. In some embodiments, the AI / ML model can be implemented on the network device side. Alternatively, the AI / ML model can be implemented on the terminal device side. In other embodiments, the AI / ML model can be implemented on both the network device and the terminal device.

[0033] For example, terminal devices can perform beam management in an AI / ML model. In this case, the terminal device can measure a portion of candidate beam pairs and use AI or ML to estimate the quality of all candidate beam pairs. Massive MIMO (mMIMO) and beamforming are widely used in the telecommunications industry. The terms "beamforming" and "mMIMO" are sometimes used interchangeably. Generally, beamforming uses multiple antennas and controls the direction of the wavefront by appropriately weighting the magnitude and phase of the signals from individual antennas in an array of multiple antennas. The most common definition of mMIMO is a system where the number of antennas exceeds the number of users. In 5G, coverage is beam-based, not cell-based. There is no cell-level reference channel that can measure cell coverage. Instead, each cell has one or more synchronous signal block beams (SSBs). SSB beams are static or semi-static and always point in the same direction. The SSB beams form a beam grid that covers the entire cell. User terminals (UEs) search for and measure beams and maintain a set of candidate beams. The candidate beamset may include beams from multiple cells. 5G millimeter wave (mmWave) enables directional communications with more antenna elements and provides additional beamforming gain, but with these 5G mmWaves, efficient beam management is crucial, where the UE and gNB periodically identify the optimal beam for operation at a given time.

[0034] Furthermore, terminal devices can perform CSI feedback based on AI / ML models. In this scenario, the original CSI information can be compressed by an AI encoder located in the terminal device and restored by an AI decoder located in the network device. AI / ML models can also be used to reduce reference signal (RS) overhead. For example, terminal devices can use new RS patterns, such as low-density DMRS or fewer CSI-RS ports.

[0035] However, AI / ML models can experience inference failures. As used herein, "inference failure" refers to a situation where the AI / ML model is unable to function correctly to obtain an estimation result. Therefore, it is important to notify network devices of inference failures.

[0036] In this embodiment, a solution for improving AI / ML models is proposed. According to this embodiment, a terminal device receives downlink information from a network device to trigger a report. The terminal device determines whether an estimation failure has occurred in the data processing model. The terminal device sends a report to the network device. The report indicates whether an estimation failure has occurred in the data processing model and / or whether the information in the report was generated based on the data processing model. In this way, the network device can be notified about estimation failures, and the accuracy of the information sent from the terminal device can be improved.

[0037] Figure 1 shows a schematic diagram of a communication system that can implement an embodiment of the present disclosure. The communication system 100, which is part of a communication network, includes terminal devices 110-1, 110-2, ..., 110-N, which can be collectively referred to as "terminal devices 110". The number N can be any appropriate integer. The terminal devices 110 can communicate with each other.

[0038] The communication system 100 further includes network equipment. In the communication system 100, the network equipment 120 and the terminal equipment 110 can communicate data and control information with each other. The number of terminal equipment shown in Figure 1 is for illustrative purposes only and does not imply any limitation.

[0039] Communication in the communication system 100 may be carried out in accordance with any suitable communication protocol. Communication protocols include, but are not limited to, cellular communication protocols such as first-generation (1G), second-generation (2G), third-generation (3G), fourth-generation (4G), and fifth-generation (5G), wireless local network communication protocols such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11, and / or any other protocols currently known or to be developed in the future. Furthermore, communication may utilize any suitable wireless communication technology. Wireless communication technologies include, but are not limited to, code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), frequency division duplexing (FDD), time division duplexing (TDD), MIMO (Multiple-Input Multiple-Output), orthogonal frequency division multiple access (OFDMA), and / or any other technologies currently known or to be developed in the future.

[0040] Embodiments of the present disclosure can be applied to any preferred scenario. For example, embodiments of the present disclosure can be implemented in low-capacity NR devices. Alternatively, embodiments of the present disclosure can be implemented in any of the following: New Wireless (NR) MIMO (multiple-input and multiple-output), NR Sidelink Enhancements, NR systems at frequencies above 52.6 GHz, NR operation extension up to 71 GHz, Narrow Band IoT (NB-IoT) / Enhanced Machine Type Communication (eMTC) on non-terrestrial networks (NTN), NTN, UE power saving function enhancements, NR coverage extensions, NB-IoT and LTE-MTC, Integrated Access and Backhaul (IAB), NR multicast / broadcast services, or multi-wireless dual connectivity enhancements.

[0041] As used herein, the term “slot” refers to a dynamic scheduling unit. A slot contains a predetermined number of symbols. The term “downlink (DL) subslot” may refer to a virtual subslot built upon an uplink (UL) subslot. A DL subslot may contain fewer symbols than a single DL slot. A slot, as used herein, may refer to a regular slot containing a predetermined number of symbols, or to a subslot containing fewer symbols than a predetermined number.

[0042] The embodiments of this disclosure will be described in detail below. First, please refer to Figure 2. Figure 2 shows a signaling chart illustrating a process 200 between a terminal device and a network device according to some exemplary embodiments of this disclosure. For discussion purposes only, the process 200 will be described with reference to Figure 1. The process 200 may relate to the terminal device 110-1 and the network device 120 in Figure 1. In some embodiments, the process 200 can be applied to AI / ML-based beam control. Alternatively, the process 200 can be applied to AI / ML-based CSI feedback. In other embodiments, the process 200 can be applied to an AI / ML-based DMRS. In some embodiments, the process 200 can be applied to an AI / ML-based CSI-RS.

[0043] In some embodiments, terminal device 110-1 may report one or more capabilities of terminal device 110-1 to network device 120 (2010). One or more capabilities indicate at least that terminal device 110-1 supports a data processing model. The data processing model may be an AI / ML model. As used herein, the term "AI / ML model" may refer to a program or algorithm that utilizes a dataset that enables the recognition of a particular pattern, thereby drawing conclusions or making predictions when given sufficient information. Generally, an AI / ML model may be a mathematical algorithm that "learns" using data and input from human experts to reproduce decisions that experts would make when given the same information.

[0044] In some embodiments, capability may demonstrate the ability to support AI / ML. In some embodiments, capability may demonstrate the ability to support beam management based on AI / ML. Alternatively, or additionally, capability may demonstrate the ability to support CSI feedback based on AI / ML. Furthermore, capability may demonstrate that terminal device 110-1 supports the ability to support DMRS based on AI / ML. In some other embodiments, capability may demonstrate that terminal device 110-1 supports the ability to support CSI-RS based on AI / ML.

[0045] Network device 120 transmits downlink information (2020) to trigger a report. In some embodiments, the report can be a periodic CSI report configurable by RRC signaling. Alternatively, the report can be a semi-persistent CSI report. In this case, such a CSI report can be activated by a media access control element (MAC CE) from network device 120. In other embodiments, the report can be a non-periodic CSI report or an SP-CSI report. In this case, the CSI report can be triggered by downlink control information (DCI) from network device 120. In some embodiments, the downlink information may indicate that the report includes an estimated failure indicator (IFI). In some embodiments, the downlink information may indicate a first time offset associated with the transmission of the report. Alternatively, or additionally, the downlink information may indicate a second time offset associated with the transmission of the report. In other embodiments, the second time offset can be transmitted in other downlink information. The second time offset is greater than the first time offset.

[0046] In some embodiments, the network device 120 may transmit report settings. For example, report settings can be transmitted via the higher-layer settings of CSI-ReportConfig. The report settings 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 can be periodic. Alternatively, the CSI-RS resource set can be semi-permanent. In other embodiments, the CSI-RS resource set can be aperiodic. In some embodiments, each CSI-RS resource within the CSI-RS resource set can correspond to a beam.

[0047] Terminal device 110-1 determines whether an estimation failure has occurred in the data processing model (2030). For example, if terminal device 110-1 is performing estimation based on normal AI / ML, terminal device 110-1 can determine whether the AI / ML mode processing has been interrupted. AI / ML mode processing may be interrupted by one of the following: memory overflow, CPU overload / overheating, or timeout. If the AI / ML mode processing has been interrupted, terminal device 110-1 can determine that an estimation failure has occurred in the data processing model.

[0048] Terminal device 110-1 sends a report to network device 120 (2040). The report indicates at least whether an estimation failure occurred in the data processing model. For example, if the report includes CSI information and further indicates that an estimation failure occurred in the data processing model, this means that the CSI information was not acquired based on the data processing model. Alternatively, if the report includes CSI information and further indicates that no estimation failure occurred in the data processing model, this means that the CSI information was acquired based on the data processing model. In some embodiments, if an estimation failure occurs, the CSI field in the report can be set to a predefined bit value. For example, all predefined bit values ​​can be set to 0. Alternatively, all predefined bit values ​​can be set to 1. In this way, the network device 120 can know whether an estimation failure occurred. Alternatively, some of the CSI fields can be set to predefined values. For example, the Reference Signal Received Power (RSRP) bit can be set to a predefined value, and the bits in the CSI-RS Resource Indicator (CRI) field do not need to be set to a predefined value. This allows network devices to identify estimated failures and determine the optimal beam.

[0049] In some embodiments, as described above, the downlink information may indicate that the report includes IFI. In this case, the report may include IFI and the CSI corresponding to IFI. In some embodiments, the report may include a single part showing IFI and CSI. Alternatively, the report may include a first part and a second part. In this scenario, the first part may include an estimated failure indicator, and the second part may include a CSI corresponding to the estimated failure indicator. In some other embodiments, the first part may include at least one of IFI, Rank Indication (RI), CSI-RS Resource Indicator (CRI), Channel Quality Indicator (CQI), or an indication of the number of non-zero broadband amplitude coefficients per layer of a Type 2 CSI, and the second part may include a precoding matrix indicator (PMI). Alternatively, the report may include a first part, a second part, and a third part. In this case, the first part may include IFI, the second part may include at least one of RI, CRI, CQI, or an indication, and the third part may include a PMI.

[0050] In some other embodiments, as described above, a first time offset and a second time offset can be set in the terminal device 110-1. In this case, if no estimation failure occurs in the data processing model, the terminal device 110-1 can send a report to the network device 120 according to the first time offset. In this case, the report may indicate a set of beams based on the data processing model, and the terminal device 110-1 can ignore the second time offset. Alternatively, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send a report to the network device 120 according to the second time offset. In this case, the report may indicate a set of beams not based on the data processing model, and the terminal device 110-1 can ignore the first time offset. In some other embodiments, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send an IFI according to the first time offset and then send a report to the network device 120. In this case, the report may indicate a set of beams not based on the data processing model. In this way, the network device 120 can learn about estimated failures and actual optimal beam information as quickly as possible.

[0051] In some embodiments, the terminal device 110-1 may be configured with an activation parameter. The activation parameter can enable CSI feedback based on the data processing model. The terminal device 110-1 may receive a setting indicating the type of codebook. In this case, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send a report based on the codebook.

[0052] Alternatively, terminal device 110-1 can determine the type of codebook. In this case, the report may include a bit field to indicate the type of codebook.

[0053] In some embodiments, if estimation failure occurs in the data processing model, the report may include a negative response (NACK) for all hybrid automatic retransmission request-acknowledgment (HARQ-ACK) information bits. In other words, the report may indicate HARQ information. Alternatively, if estimation failure occurs in the data processing model, the report may include HARQ-ACK information bits, and the uplink channel to which the report is sent may include an IFI field. In some embodiments, the uplink channel may be a physical uplink control channel.

[0054] In other embodiments, if an estimation failure occurs in the data processing model, the terminal device 110-1 may scramble the uplink channel with an AI or ML-specific scrambling sequence associated with the estimation failure. In this case, a report can be transmitted over the uplink channel. In some embodiments, the uplink channel may point to PUCCH. Alternatively, the uplink channel may point to PUSCH. In other words, the terminal device 110-1 may scramble PUSCH / PUCCH transmitting CSI or HRRQ information using an AI / ML-specific scrambling sequence. The scrambled PUSCH / PUCCH can be used to indicate that an estimation failure has occurred at the terminal device 110-1.

[0055] In some embodiments, the scrambling sequence can be generated based on the Radio Network Temporary Identification Information (RNTI) of the AL or ML. For example, a new RNTI for AI / ML, such as AI-RNTI, can be introduced. Specifically, when scrambling a PUCCH or PUSCH transmitting CSI or HRRQ information, the terminal device 110-1 first generates a scrambling sequence c according to the value of the RNTI. init The following may be calculated. If estimation failure occurs, terminal device 110-1 will perform a scrambling sequence c according to the RNTI value of AI / ML, for example, AI-RNTI.init You may calculate this.

[0056] Alternatively, the scrambling sequence can be an AI / ML-specific pseudo-random sequence. For example, terminal device 110-1 can use a new DMRS sequence corresponding to PUSCH / PUCCH transmitting CSI or HRRQ information to indicate estimation failure. Specifically, the new DMRS sequence can be AI / ML-specific, and the new DMRS sequence is an AI / ML-specific pseudo-random sequence c init It may have c initは AI / ML-specific scrambling IDs (for example, TIFF0007848875000001.tif1111 or TIFF0007848875000002.tif1211 may have a higher layer setting for scramblingID0 or be set by scramblingID0.

[0057] In other embodiments, the scrambling sequence can be an AI / ML-specific mask. For example, if an estimation failure occurs in the data processing model, the terminal device 110-1 can add a cyclic redundancy check (CRC) with a mask (e.g., an AI / ML-specific mask) to the transport block (i.e., the raw data or CSI of PUSCH).

[0058] An exemplary embodiment will be described in detail with reference to Figure 3A-6. Note that the exemplary embodiments described below can be implemented separately or in combination.

[0059] Figures 3A to 3E show the AI / ML-based beam management process 300, respectively.

[0060] The terminal device 110-1 may report the ability to support AI / ML-based BM (3010). Then, the network device 120 may set activation parameters to enable the functions of the AI-ML-based BM by RRC signaling (3020). The network device 120 can transmit DCI for triggering the CSI report of BM (3030). The reported amount related to the CSI report can be set to "CRI-RSRP" (or other amounts, e.g., Synchronization Signal / Physical Broadcast Channel (SS / PBCH) Resource Block Indicator (SSBRI)-RSRP). The CSI-RS report can be associated with a CSI resource set including N CSI-RS resources, and each CSI-RS resource can correspond to a beam (direction). The network device 120 can transmit a set of reference signals to the terminal device 110-1 (3040).

[0061] The terminal device 110-1 can estimate the beam quality (e.g., L1-RSRP) of all N candidate beams according to M (M < N, M = N / a, a ∈ [1, N]) beams out of the N candidate beams by using AI prediction. Next, the terminal device 110-1 determines K optimal beams (i.e., having the maximum L1-RSRP), and after a set time offset (set by the upper layer setting of reportSlotOffset), reports the K (set by the existing upper layer setting nrofReportedRS, e.g., K = 1, 2, or 4) optimal beams to the network device 120. Specifically, it can report the K CRIs and L1-RSRP corresponding to the K optimal beams.

[0062] Terminal device 110-1 can transmit a CSI report to network device 120 (3050). If estimation failure occurs in the data processing model, the beam quality of all N candidate beams becomes unestimable. In this case, a predefined bit value (or state) can be introduced as an indicator in the CSI field. The indicator can be used to indicate that estimation failure has occurred in terminal device 110-1. For example, the entire CSI field, including all CRI fields and all RSRP fields in the CSI report, can be set to all "0" or all "1". This allows the network device to know whether or not estimation failure has occurred. Alternatively, partial CSI fields in the CSI report can be set to all "0" or all "1". For example, only all RSRP fields in the CSI report can be set to all "0" or all "1". Furthermore, the K CRI values ​​corresponding to the K optimal beams out of M candidate beams can be shown in the CRI field. In this way, if an estimation failure occurs, the network device can know both that an estimation failure has occurred and the (IDs) of the K optimal beams (determined from the M candidate beams).

[0063] In some embodiments, a new bit field can be introduced as an Estimated Failure Indicator (IFI). Specifically, a new bit field can be introduced into the CSI field. Furthermore, the IFI field may contain one bit, which indicates whether or not an estimated failure occurred. In other words, this means whether or not the corresponding CSI (e.g., CRI-RSRP, SSBRI-RSRP, CRI-Signal-to-Interference Noise Ratio (SINR), SSBRI-SINR) can be obtained based on the AI ​​prediction (i.e., the data processing model). The term "corresponding CSI" can refer to the CSI in the PUCCH / PUSCH resource where the IFI is located, or the CSI in the same report as the IFI.

[0064] In some embodiments, IFI can be introduced into the CSI report as a report quantity. Referring to Figure 3B, network device 120 can send a DCI to trigger the BM's CSI report (3031). The report quantity associated with the CSI report (set in the higher-layer setting of reportQuantity) can be set to "IFI-CRI-RSRP". IFI can occupy a 1-bit field. In this case, terminal device 110-1 can send a PUCCH / PUSCH containing the IFI field to network device 120 (3051). If estimation failure occurs, terminal device 110-1 may report "0" in the IFI field, which also means that the CRI and L1-RSRP corresponding to IFI are not based on AI predictions (i.e., data processing models). If estimation failure does not occur, terminal device 110-1 may report "1" in the IFI field, which also means that the CRI and L1-RSRP corresponding to IFI are based on AI predictions (i.e., data processing models). In other embodiments, the IFI may contain more than one bit. In this case, the terminal device may also report an index of the data processing model of estimated failures to the network device.

[0065] In some embodiments, IFI and CRI-RSRP may be included in the same part, or they may not be included in the same part. For example, if the report volume is set to "IFI-CRI-RSRP", the CSI report may include a single part, or it may include two parts, a first part and a second part. In this case, the first part may include IFI and the second part may include CRI-RSRP. The first part and the second part refer to part 1 and part 2, respectively, i.e., part 1 and part 2 in a Type 1 or Type 2 CSI feedback. In this way, the network device can know whether an estimation failure has occurred and whether the reported K optimal beams are based on AI predictions.

[0066] In some other embodiments, a single CSI report can be associated with two time offsets, each used for AI-based beam reporting and legacy (or default, alternative) beam reporting. Specifically, terminal device 110-1 can receive a DCI in which a CSI report for BM is scheduled. The DCI can simultaneously show two time offsets (e.g., t1 and t2). That is, the CSI report is associated with two time offsets, t1 and t2. The value of t1 can be set according to the time required to measure M candidate beams and the delay (relatively small) of the AI ​​process. The value of t2 can be set according to the time required to measure all N candidate beams. Therefore, the value of t2 is greater than the value of t1. In some embodiments, the two time offsets can be shown in two separate fields of the DCI, which are selected from the same time offset list set by the RRC (e.g., reportSlotOffsetList) or from two separate time offset lists (e.g., reportSlotOffsetList and reportSlotOffsetListForAI). Alternatively, one time offset can be represented by DCI, and the other time offset can be associated with a time offset, or a list of time offsets containing the time offset. For example, the other time offset can be set to a CSI trigger state (e.g., CSI-AperiodicTriggerState) or a CSI report (CSI-ReportConfig).

[0067] Referring to Figures 3C-3E, terminal device 110-1 can be configured with time slots 310 and 320. Assume that the scheduling DCI can be placed in slot n. As shown in Figure 3C, if no estimation failure occurs, terminal device 110-1 can transmit an AI-based beam report on the PUSCH in slot n+K2 (3052). K2 can refer to the slot interval between the PUSCH transmitting the CSI and the scheduling DCI. In this case, the value of K2 can be determined according to the minimum time offset 310 (and, if any, other indicated time offsets, e.g., time offsets associated with other CSI reports, time offsets in the TDRA field of the scheduling DCI), and the maximum time offset 320 can be ignored. In this way, the network device can know that no estimation failure has occurred in terminal device 110-1 and can know the K optimal beams based on AI prediction. Alternatively, as shown in Figure 3D, if an estimation failure occurs, terminal device 110-1 can transmit a legacy beam report on the PUSCH in slot n+K2 (3053). In this case, the value of K2 can be determined according to the maximum time offset 320 (and any other indicated time offsets, if any), and the minimum time offset 310 can be ignored. In this way, the network device can know that an estimation failure has occurred at the terminal device and can know the optimal beam based on legacy beam measurements, i.e., measurements of all N candidate beams.

[0068] Alternatively, the AI-based beam report at t1 may include at least IFI information (e.g., a predefined bit value in the CRI-RSRP field or RSRP field), and the legacy beam report at t2 may include beam information (e.g., CRI-RSRP). Referring to Figure 3E, terminal device 110-1 may send PUCCH 1, which includes at least IFI information, to network device 120 (3054). Subsequently, terminal device 110-1 may send PUCCH 2, which includes beam information, to network device 120 (3055). In this way, if an estimation failure occurs, the terminal device does not have to wait for another CSI report to be triggered by the network device, so the network device can not only know of the estimation failure in a timely manner, but also know the actual optimal beam information as quickly as possible.

[0069] Figures 4A-4B show the AI / ML-based CSI feedback process 400, respectively. Terminal device 110-1 may report its ability to support AI / ML-based CSI feedback (4010). Network device 120 may then set enable parameters to enable the AI-ML-based CSI feedback function by RRC signaling (i.e., upper layer configuration) (4020). Network device 120 can transmit DCI to trigger a CSI report for CSI acquisition (4030). The report quantity (i.e., report content) associated with the CSI report can be set to "CRI-RI-PMI-CQI" (or other quantities such as compressed CSI). A CSI-RS report can be associated with a CSI resource set containing N CSI-RS resources, where each CSI-RS resource can correspond to a beam (direction).

[0070] Network device 120 can transmit a reference signal set to terminal device 110-1 (4040). Terminal device 110-1 can calculate the compressed CSI (i.e., bitstream) according to the CSI-RS resource associated with the CSI report by using the AI ​​encoder. After a set time offset (set by the higher layer setting of reportSlotOffset), terminal device 110-1 can transmit the compressed bitstream of the CSI field of PUSCH to network device 120.

[0071] In some embodiments, a new bit field can be introduced as IFI. Specifically, a new bit field can be introduced in the CSI field. Furthermore, the IFI field may contain 1 bit, which indicates whether or not an estimation failure occurred. In other words, this means whether or not the corresponding CSI (e.g., CRI, Rank Indicator (RI), Precoding Matrix Indicator (PMI), Channel Quality Indicator (CQI), Compressed CSI) was obtained based on AI prediction. The term "corresponding CSI" can refer to the CSI in the PUCCH / PUSCH resource where the IFI is located, or the CSI in the same report as the IFI. In this case, referring to Figure 4A, terminal device 110-1 may send a PUCCH containing the IFI to network device 120 (4050). If an estimation failure occurs, terminal device 110-1 may report "0" in the IFI field, which also means that the CRI, RI, CQI and / or PMI corresponding to the IFI are not based on the AI ​​encoder. If no estimated failure occurs, the terminal device 110-1 may report "1" in the IFI field, which also means that the CRI, RI, CQI and / or PMI corresponding to the IFI are based on the AI ​​encoder. In other embodiments, the IFI may contain more than one bit. In this case, the terminal device may also report an index of the data processing model for estimated failures to the network device.

[0072] In some embodiments, IFI and CRI-RI-PMI-CQI may be included in the same part, or they may not be included in the same part. Specifically, for Type 1, Type 2, and Extended Type 2, CSI feedback is possible in PUCCH / PUSCH. In some embodiments, the CSI report includes two parts, Part 1 and Part 2. In this case, Part 1 may include at least one of the following: IFI, RI, CRI, CQI, and an indication of the number of non-zero broadband amplitude coefficients per layer of the Type 2 CSI. Part 2 may include PMI. Alternatively, the CSI report may include three parts, Part 1, Part 2, and Part 3. In this case, Part 1 may include IFI. Parts 2 and 3 refer to Part 1 and Part 2, respectively. In this way, the network device can know whether an estimation failure has occurred and whether the reported CSI is based on the AI ​​encoder.

[0073] In some other embodiments, if the terminal device 110-1 is provided with an enable parameter to enable AI / ML-based CSI feedback, the terminal device 110-1 expects the codebook type to be set. Referring to Figure 4B, if the terminal device 110-1 is set with the enable parameter to enable AI / ML-based CSI feedback, the network device 120 may also set the codebook type (4021). For example, the network device 120 may transmit the codebook setting (e.g., via a higher layer setting). In this case, if an estimation failure occurs, the terminal device 110-1 may send a PUSCH based on the set codebook (4051). Alternatively, the terminal device 110-1 can select the codebook type. In this case, a new bit field may be introduced to indicate the selected codebook to the network device 120. For example, part 1 of the PUSCH may include a new bit field to indicate the codebook type (part 2). In this way, in addition to the occurrence of estimation failures and the AI ​​encoder-based reported PUSCH, the network device also knows which codebook the terminal device used to calculate the reported PUSCH.

[0074] Figure 5 shows the AI / ML-based DMRS process 500. Terminal device 110-1 may report its ability to support AI / ML-based DMRS (5010). Network device 120 may then set enable parameters to enable the AI-ML-based DMRS functionality by RRC signaling (5020). Network device 120 may transmit a PDCCH with low-density DMRS (5030). DMRS can be used to demodulate the PDCCH. Specifically, terminal device 110-1 can estimate the channels in all resources using the channel response in the DMRS resources by using AI DMRS. Next, network device 120 can schedule a PDSCH with low-density DMRS (5040). Terminal device 110-1 may also demodulate the scheduled PDSCH according to the same AI DMRS. Finally, the terminal device 110-1 may decide whether or not to report a HARQ-ACK or HARQ-NACK depending on whether the received PDSCH is correct or not (for example, by using a CRC check).

[0075] If a presumptive failure occurs, terminal device 110-1 can implicitly notify network device 120 of the occurrence of the presumptive failure. For example, if a presumptive failure occurs, terminal device 110-1 may generate a NACK for all HARQ-ACK information bits in the PUCCH, regardless of whether other PDSCHs were correctly decoded. Terminal device 110-1 can send the PUCCH containing the NACK to network device 120 (5050). In this way, if the network device receives a PUCCH in which all HARQ information bits are NACK, it can know that a presumptive failure has occurred at the terminal device. Alternatively, terminal device 110-1 can explicitly notify network device 120 of the occurrence of the presumptive failure. For example, a new bit field can be introduced as the IFI of the PUCCH. Specifically, the IFI field may contain 1 bit, which indicates whether or not a presumptive failure has occurred. In this case, terminal device 110-1 can send the PUCCH containing the IFI field to network device 120 (5050). The HARQ-ACK information bits of a PUCCH can indicate whether the corresponding PDSCH was decoded correctly. In this way, network devices can know whether an estimated failure has occurred, and the HARQ feedback of other PDSCHs will not be affected.

[0076] Figure 6 shows the process for AI / ML-based CSI-RS. Terminal device 110-1 can report its ability to support AI / ML-based CSI-RS (6010). Network device 120 may then set an enable parameter to enable the AI-ML-based CSI-RS function by RRC signaling (6020). Assume that network device 120 wants to know the DL CSI corresponding to 32 antenna ports. Network device 120 can send a DCI to trigger a CSI report for CSI-RS (6030). Network device 120 may send a 16-port CSI-RS (6040). After receiving the CSI-RS, terminal device can use AI CSI-RS to estimate the channel in the resource corresponding to the 32-port CSI-RS based on the channel in the resource corresponding to the 16-port CSI-RS. Then, terminal device can report the CSI corresponding to the 32 antenna ports to network device 120 after a set time offset.

[0077] In some embodiments, a new bit field can be introduced as an estimated failure indicator (IFI). Specifically, a new bit field can be introduced in the CSI field. Furthermore, the IFI field may contain one bit, which indicates whether or not an estimated failure occurred. In other words, this means whether or not the corresponding CSI (e.g., CRI, LI, RI, PMI, CQI, i1) was obtained based on the AI ​​CSI-RS. The term "corresponding CSI" can refer to the CSI in the PUCCH / PUSCH resource where the IFI is located, or the CSI in the same report as the IFI.

[0078] In some embodiments, IFI can be introduced into the CSI as a report quantity. The report quantity associated with the CSI report (set in the higher-layer setting of reportQuantity) can be set to "IFI-CRI-RI-PMI-CQI". IFI can occupy a 1-bit field. In this case, terminal device 110-1 can send a PUCCH / PUSCH containing the IFI field to network device 120 (6050). If an estimated failure occurs, terminal device 110-1 may report "0" in the IFI field, which also means that the CRI, RI, CQI, and PMI corresponding to IFI are not based on AI CSI-RS, i.e., the reported CRI, RI, CQI, and PMI were determined according to the 16 ports of CSI-RS. If no estimated failure occurs, terminal device 110-1 may report "1" in the IFI field, which also means that the CRI, RI, CQI, and PMI corresponding to the IFI are based on the AI ​​encoder, i.e., the reported CRI, RI, CQI, and PMI were determined according to the 32 ports of the CSI-RS. In other embodiments, the IFI may contain more than one bit. In this case, terminal device 110-1 may also report an index of the data processing model for estimated failures to network device 120. In this way, the network device can know whether an estimated failure occurred and whether the reported CSI is based on the AI ​​encoder.

[0079] Figure 7 shows a flowchart of an exemplary method 700 according to an embodiment of the present disclosure. Method 700 can be carried out in any suitable apparatus. For illustrative purposes only, Method 700 can be carried out in the terminal device 110-1 shown in Figure 1.

[0080] In some embodiments, terminal device 110-1 may report one or more capabilities of terminal device 110-1 to network device 120. One or more capabilities shall at least indicate that terminal device 110-1 supports a data processing model. The data processing model may be an AI / ML model. As used herein, the term "AI / ML model" may refer to a program or algorithm that utilizes a dataset that enables the recognition of a particular pattern, thereby drawing conclusions or making predictions when given sufficient information. Generally, an AI / ML model may be a mathematical algorithm that "learns" using data and input from human experts to reproduce decisions that experts would make when given the same information.

[0081] In some embodiments, capability may demonstrate the ability to support AI / ML. In some embodiments, capability may demonstrate the ability to support beam management based on AI / ML. Alternatively, or additionally, capability may demonstrate the ability to support CSI feedback based on AI / ML. Furthermore, capability may demonstrate that terminal device 110-1 supports the ability to support DMRS based on AI / ML. In some other embodiments, capability may demonstrate that terminal device 110-1 supports the ability to support CSI-RS based on AI / ML.

[0082] In block 710, terminal device 110-1 receives downlink information from network device 120 to trigger a report. In some embodiments, the report can be a periodic CSI report configurable by RRC signaling. Alternatively, the report can be a semi-persistent CSI report. In this case, such a CSI report can be activated by MAC CE from network device 120. In other embodiments, the report can be a non-periodic CSI report or an SP-CSI report. In this case, the CSI report can be triggered by DCI from network device 120. In some embodiments, the downlink information may indicate that the report includes IFI. In some embodiments, the downlink information may indicate a first time offset associated with the transmission of the report. Alternatively, or additionally, the downlink information may indicate a second time offset associated with the transmission of the report. In other embodiments, the second time offset can be transmitted in other downlink information. The second time offset is greater than the first time offset.

[0083] In some embodiments, the terminal device 110-1 may receive report settings. For example, report settings can be transmitted via the settings of the higher layer of CSI-ReportConfig. The report settings 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 can be periodic. Alternatively, the CSI-RS resource set can be semi-permanent. In other embodiments, the CSI-RS resource set can be aperiodic. In some embodiments, each CSI-RS resource in the CSI-RS resource set can correspond to a beam.

[0084] In block 720, terminal device 110-1 determines whether an estimation failure has occurred in the data processing model. For example, if terminal device 110-1 is performing estimation based on normal AI / ML, terminal device 110-1 can determine whether the AI / ML mode processing has been interrupted. AI / ML mode processing may be interrupted by one of the following: memory overflow, CPU overload / overheating, or timeout. If the AI / ML mode processing is interrupted, terminal device 110-1 can determine that an estimation failure has occurred in the data processing model.

[0085] In block 730, terminal device 110-1 sends a report to network device 120. The report indicates at least whether or not an estimation failure occurred in the data processing model. For example, if the report includes CSI information and further indicates that an estimation failure occurred in the data processing model, this means that CSI information was not acquired based on the data processing model. Alternatively, if the report includes CSI information and further indicates that no estimation failure occurred in the data processing model, this means that CSI information was acquired based on the data processing model. In some embodiments, if an estimation failure occurs, the CSI field in the report can be set to a predefined bit value. For example, all predefined bit values ​​can be set to 0. Alternatively, all predefined bit values ​​can be set to 1. In this way, the network device 120 can know whether or not an estimation failure occurred. Alternatively, some of the CSI field can be set to a predefined value. For example, the RSRP bit can be set to a predefined value, and the CRI field bit does not need to be set to a predefined value. In this way, the network device can know when an estimation failure occurred and when the optimal beam is available.

[0086] In some embodiments, as described above, the downlink information may indicate that the report includes IFI. In this case, the report may include IFF and the CSI corresponding to the IFI. In some embodiments, the report may include a single part showing the IFI and the CSI. Alternatively, the report may include a first part and a second part. In this situation, the first part may include an estimated failure indicator, and the second part may include a CSI corresponding to the estimated failure indicator. In some other embodiments, the first part may include at least one of the following: IFI, RI, CSI-RS, CRI, CQI, or an indication of the number of non-zero broadband amplitude coefficients per layer for Type 2 CSI, and the second part may include a PMI. Alternatively, the report may include a first part, a second part, and a third part. In this case, the first part may include IFI, the second part may include at least one of the following: RI, CRI, CQI, or an indication, and the third part may include a PMI.

[0087] In some other embodiments, as described above, a first time offset and a second time offset can be set in the terminal device 110-1. In this case, if no estimation failure occurs in the data processing model, the terminal device 110-1 can send a report to the network device 120 according to the first time offset. The report may indicate a set of beams based on the data processing model, and the terminal device 110-1 can ignore the second time offset. Alternatively, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send a report to the network device 120 according to the second time offset. In this case, the report may indicate a set of beams not based on the data processing model, and the terminal device 110-1 can ignore the first time offset. In some other embodiments, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send an IFI according to the first time offset and then send a report to the network device 120. In this case, the report may indicate a set of beams not based on the data processing model. In this way, the network device 120 can learn about estimated failures and actual optimal beam information as quickly as possible.

[0088] In some embodiments, the terminal device 110-1 may be configured with an activation parameter. The activation parameter can enable CSI feedback based on the data processing model. The terminal device 110-1 may receive a setting indicating the type of codebook. In this case, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send a report based on the codebook.

[0089] Alternatively, terminal device 110-1 can determine the type of codebook. In this case, the report may include a bit field to indicate the type of codebook.

[0090] In some embodiments, if estimation failure occurs in the data processing model, the report may include a NACK for all HARQ-ACK information bits. In other words, the report may indicate HARQ information. Alternatively, if estimation failure occurs in the data processing model, the report may include an IFI and HARQ-ACK information bits, and the uplink channel to which the report is sent may include an IFI field. In some embodiments, the uplink channel may be a physical uplink control channel.

[0091] In other embodiments, if an estimation failure occurs in the data processing model, the terminal device 110-1 may scramble the uplink channel with an AI or ML-specific scrambling sequence associated with the estimation failure. In this case, a report can be transmitted over the uplink channel. In some embodiments, the uplink channel may point to PUCCH. Alternatively, the uplink channel may point to PUSCH. In other words, the terminal device 110-1 may scramble PUSCH / PUCCH transmitting CSI or HRRQ information using an AI / ML-specific scrambling sequence. The scrambled PUSCH / PUCCH can be used to indicate that an estimation failure has occurred at the terminal device 110-1.

[0092] In some embodiments, the scrambling sequence can be generated based on the RNTI of AL or ML. For example, a new RNTI for AI / ML, such as AI-RNTI, can be introduced. Specifically, when scrambling a PUCCH or PUSCH transmitting CSI or HRRQ information, the terminal device 110-1 first generates the scrambling sequence c according to the value of the RNTI. init The following may be calculated. If estimation failure occurs, terminal device 110-1 will perform a scrambling sequence c according to the RNTI value of AI / ML, for example, AI-RNTI. init You may calculate this.

[0093] Alternatively, the scrambling sequence can be an AI / ML-specific pseudo-random sequence. For example, terminal device 110-1 can use a new DMRS sequence corresponding to PUSCH / PUCCH transmitting CSI or HRRQ information to indicate estimation failure. Specifically, the new DMRS sequence can be AI / ML-specific, and the new DMRS sequence is an AI / ML-specific pseudo-random sequence c init It may have c initは AI / ML-specific scrambling IDs (for example, TIFF0007848875000003.tif99 or TIFF0007848875000004.tif99 may have a higher layer setting for scramblingID0 or be set by scramblingID0.

[0094] In other embodiments, the scrambling sequence can be an AI / ML-specific mask. For example, if an estimation failure occurs in the data processing model, the terminal device 110-1 can add a CRC with a mask (e.g., an AI / ML-specific mask) to the transport block (i.e., the raw data or CSI of PUSCH).

[0095] Figure 8 shows a flowchart of an exemplary method 800 according to an embodiment of the present disclosure. Method 800 can be carried out with any suitable device. For illustrative purposes only, Method 800 can be carried out with the network device 120 shown in Figure 1.

[0096] In some embodiments, the network device 120 may receive a report from the terminal device 110-1 indicating one or more capabilities of the terminal device 110-1. One or more capabilities indicate at least that the terminal device 110-1 supports a data processing model. The data processing model may be an AI / ML model. As used herein, the term "AI / ML model" may refer to a program or algorithm that utilizes a dataset that enables the recognition of a particular pattern, thereby drawing conclusions or making predictions when given sufficient information. Generally, an AI / ML model may be a mathematical algorithm that "learns" using data and input from human experts to reproduce decisions that experts would make when given the same information.

[0097] In some embodiments, capability may demonstrate the ability to support AI / ML. In some embodiments, capability may demonstrate the ability to support beam management based on AI / ML. Alternatively, or additionally, capability may demonstrate the ability to support CSI feedback based on AI / ML. Furthermore, capability may demonstrate that terminal device 110-1 supports the ability to support DMRS based on AI / ML. In some other embodiments, capability may demonstrate that terminal device 110-1 supports the ability to support CSI-RS based on AI / ML.

[0098] In block 810, the network device 120 transmits downlink information to trigger a report. In some embodiments, the report can be a periodic CSI report configurable by RRC signaling. Alternatively, the report can be a semi-persistent CSI report. In this case, such a CSI report can be activated by a MAC CE from the network device 120. In other embodiments, the report can be a non-periodic CSI report or an SP-CSI report. In this case, the CSI report can be triggered by a DCI from the network device 120. In some embodiments, the downlink information may indicate that the report includes an IFI. In some embodiments, the downlink information may indicate a first time offset associated with the transmission of the report. Alternatively, or additionally, the downlink information may indicate a second time offset associated with the transmission of the report. In other embodiments, the second time offset can be transmitted in other downlink information. The second time offset is greater than the first time offset.

[0099] In some embodiments, the network device 120 may transmit report settings. For example, report settings can be transmitted via the higher-layer settings of CSI-ReportConfig. The report settings 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 can be periodic. Alternatively, the CSI-RS resource set can be semi-permanent. In other embodiments, the CSI-RS resource set can be aperiodic. In some embodiments, each CSI-RS resource within the CSI-RS resource set can correspond to a beam.

[0100] In block 820, the network device 120 receives a report from the terminal device 110-1. The report indicates at least whether or not an estimation failure occurred in the data processing model. For example, if the report includes CSI information and further indicates that an estimation failure occurred in the data processing model, this means that the CSI information was not acquired based on the data processing model. Alternatively, if the report includes CSI information and further indicates that no estimation failure occurred in the data processing model, this means that the CSI information was acquired based on the data processing model. In some embodiments, if an estimation failure occurs, the CSI field in the report can be set to a predefined bit value. For example, all predefined bit values ​​can be set to 0. Alternatively, all predefined bit values ​​can be set to 1. In this way, the network device 120 can know whether or not an estimation failure occurred. Alternatively, some of the CSI field can be set to a predefined value. For example, the RSRP bit can be set to a predefined value, and the CRI field bit does not need to be set to a predefined value. In this way, the network device can know when an estimation failure occurred and when the optimal beam was set.

[0101] In some embodiments, as described above, the downlink information may indicate that the report includes IFI. In this case, the report may include IFF and the CSI corresponding to the IFI. In some embodiments, the report may include a single part showing the IFI and the CSI. Alternatively, the report may include a first part and a second part. In this situation, the first part may include an estimated failure indicator, and the second part may include a CSI corresponding to the estimated failure indicator. In some other embodiments, the first part may include at least one of the IFI, RI, CRI, CQI, or an indication of the number of non-zero broadband amplitude coefficients per layer for a Type 2 CSI, and the second part may include a PMI. Alternatively, the report may include a first part, a second part, and a third part. In this case, the first part may include an IFI, the second part may include at least one of the RI, CRI, CQI, or an indication, and the third part may include a PMI.

[0102] In some other embodiments, as described above, a first time offset and a second time offset can be set on the terminal device 110-1. In this case, if no estimation failure occurs in the data processing model, the network device 120 can receive a report according to the first time offset. The report may indicate a set of beams based on the data processing model. Alternatively, if an estimation failure occurs in the data processing model, the network device 120 can receive a report according to the second time offset. In this case, the report may indicate a set of beams not based on the data processing model. In some other embodiments, if an estimation failure occurs in the data processing model, the network device 120 can receive IFI according to the first time offset and then receive a report. In this case, the report may indicate a set of beams not based on the data processing model. In this way, the network device 120 can learn of estimation failures and actual optimal beam information as quickly as possible.

[0103] In some embodiments, the terminal device 110-1 may be configured with an activation parameter. The activation parameter can enable CSI feedback based on the data processing model. The network device 110-1 may also transmit a setting indicating the type of codebook. In this case, if an estimation failure occurs in the data processing model, the terminal device 110-1 can send a report based on the codebook.

[0104] Alternatively, terminal device 110-1 can determine the type of codebook. In this case, the report may include a bit field to indicate the type of codebook.

[0105] In some embodiments, if estimation failure occurs in the data processing model, the report may include a NACK for all HARQ-ACK information bits. Alternatively, if estimation failure occurs in the data processing model, the report may include an IFI and HARQ-ACK information bits.

[0106] In other embodiments, if an estimation failure occurs in the data processing model, the uplink channel may be scrambled with an AI or ML-specific scrambling sequence associated with the estimation failure. In this case, a report can be transmitted over the uplink channel. In some embodiments, the uplink channel may point to PUCCH. Alternatively, the uplink channel may point to PUSCH. In other words, terminal device 110-1 may scramble PUSCH / PUCCH transmitting CSI or HRRQ information using an AI / ML-specific scrambling sequence. The scrambled PUSCH / PUCCH can be used to indicate that an estimation failure has occurred at terminal device 110-1. In this case, network device 120 can determine that an estimation failure has occurred at terminal device 110-1 based on the scrambled PUSCH / PUCCH.

[0107] In some embodiments, the terminal device includes circuitry configured to receive downlink information from a network device to trigger a report, determine whether an estimation failure has occurred in the data processing model, and send a report to the network device. The report indicates at least whether an estimation failure has occurred in the data processing model.

[0108] In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending the report to the network device in accordance with the determination of the occurrence of an estimated failure. The channel status information (CSI) field in the report is set to a predefined bit value, or a portion of the CSI field is set to a predefined bit value.

[0109] In some embodiments, the report includes at least one estimation failure indicator, or a CSI corresponding to the estimation failure indicator, the estimation failure indicator indicating whether or not an estimation failure occurred in the data processing model.

[0110] In some embodiments, the downlink information indicates whether the report includes an estimated failure indicator.

[0111] In some embodiments, the report includes a single part, which includes at least one of the estimated failure indicators and a CSI corresponding to the estimated failure indicator.

[0112] In some embodiments, the report includes a first part and a second part. The first part includes an estimated failure indicator, and the second part includes a CSI corresponding to the estimated failure indicator.

[0113] In some embodiments, the report includes a first part and a second part. The first part includes at least one of the following: an estimated failure indicator, a rank indicator (RI), a CSI reference signal (CSI-RS) resource indicator (CRI), a channel quality indicator (CQI), or an indicator of the number of non-zero broadband amplitude coefficients per layer of a Type 2 CSI; and the second part includes a precoding matrix indicator (PMI).

[0114] In some embodiments, the report includes a first part, a second part, and a third part. The first part includes an estimated failure indicator, the second part includes at least one of RI, CRI, CQI, or indicators, and the third part includes PMI.

[0115] In some embodiments, the terminal device includes circuitry configured to receive a setting indicating the type of codebook from the network device, in accordance with the determination that an enable parameter is set in the terminal device to enable CSI feedback based on a data processing model. In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending a report containing a CSI based on the codebook, in accordance with the determination that an estimated failure has occurred.

[0116] In some embodiments, the terminal device includes circuitry configured to determine the type of codebook. In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending a report containing a bit field indicating the type of codebook to the network device.

[0117] In some embodiments, the downlink information indicates a first time offset and a second time offset greater than the first time offset, or the downlink information indicates the first time offset and further downlink information indicates the second time offset.

[0118] In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending the report according to a first time offset, based on the determination that no estimation failures have occurred in the data processing model. The report shows a set of beams based on the data processing model, with the second time offset omitted.

[0119] In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending the report according to a second time offset, in accordance with the determination of the occurrence of an estimated failure. The report indicates a set of beams not based on a data processing model, and the first time offset is omitted.

[0120] In some embodiments, the terminal device includes circuitry configured to send a report to a network device by sending a channel status information (CSI) field or an estimated failure indicator in the report according to a first time offset, and sending a report indicating a set of beams not based on a data processing model according to a second time offset, in accordance with the determination of the occurrence of an estimated failure. The channel status information (CSI) field is set to a predefined bit value, or a portion of the CSI field is set to a predefined bit value.

[0121] In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending a report containing a negative response (NACK) for all hybrid automatic retransmission request-acknowledgment (HARQ-ACK) information bits, in accordance with the determination of the occurrence of an estimated failure.

[0122] In some embodiments, the terminal device includes circuitry configured to send a report to the network device by sending a report containing HARQ-ACK information bits over the uplink channel, in accordance with the determination of the occurrence of an estimated failure. The uplink channel includes an estimated failure indicator.

[0123] In some embodiments, the terminal device includes circuitry configured to scramble the uplink channel from which the report is transmitted with an artificial intelligence (AI) or machine learning (ML) specific scrambling sequence associated with the estimated failure, in accordance with the determination of the occurrence of an estimated failure.

[0124] In some embodiments, the scrambling sequence is generated based on the AL or ML's Radio Network Temporary Identification Information (RNTI), or the scrambling sequence is an AI or ML-specific pseudo-random sequence, or the scrambling sequence is an AI or ML-specific cyclic redundancy check (CRC) mask.

[0125] In some embodiments, the network device includes circuitry configured to send downlink information to a terminal device to trigger a report and to receive a report from the terminal device. The report indicates at least whether an estimation failure occurred in the data processing model.

[0126] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices, by receiving reports from terminal devices in accordance with the determination of the occurrence of an estimated failure. The channel status information (CSI) field in the report is set to a predefined bit value, or a portion of the CSI field is set to a predefined bit value.

[0127] In some embodiments, the report includes an estimation failure indicator and a CSI corresponding to the estimation failure indicator, where the estimation failure indicator indicates whether or not an estimation failure occurred in the data processing model.

[0128] In some embodiments, the downlink information indicates that the report includes an estimated failure indicator.

[0129] In some embodiments, the report includes a single section showing the estimated failure indicator and the CSI.

[0130] In some embodiments, the report includes a first part and a second part. The first part includes an estimated failure indicator, and the second part includes a CSI corresponding to the estimated failure indicator.

[0131] In some embodiments, the report includes a first part and a second part. The first part includes at least one of the following: an estimated failure indicator, a rank indicator (RI), a CSI reference signal (CSI-RS) resource indicator (CRI), a channel quality indicator (CQI), or an indicator of the number of non-zero broadband amplitude coefficients per layer of a Type 2 CSI; and the second part includes a precoding matrix indicator (PMI). Alternatively, the report includes a first part, a second part, and a third part. The first part includes an estimated failure indicator; the second part includes at least one of the following: RI, CRI, CQI, or indicator; and the third part includes a PMI.

[0132] In some embodiments, the network device includes circuitry configured to send a setting indicating the type of codebook to the terminal device, in accordance with the determination that an enable parameter for enabling CSI feedback based on a data processing model is set on the terminal device. In some embodiments, the network device includes circuitry configured to receive reports from the terminal device by receiving a report containing CSI feedback based on a codebook, in accordance with the determination that an estimated failure has occurred.

[0133] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving reports from terminal devices that include a bit field indicating the type of codebook.

[0134] In some embodiments, the downlink information indicates a first time offset and a second time offset greater than the first time offset, or the downlink information indicates the first time offset and further downlink information indicates the second time offset.

[0135] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving reports from terminal devices according to a first time offset, based on the determination that no estimation failures have occurred in the data processing model. The reports indicate a set of beams based on the data processing model, with a second time offset being ignored.

[0136] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving reports from terminal devices according to a second time offset, in accordance with the determination of the occurrence of an estimated failure. The reports indicate a set of beams not based on a data processing model, and the second time offset is ignored.

[0137] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving estimated failure indicators according to a first time offset, and by receiving reports from terminal devices according to a second time offset, in accordance with the determination of the occurrence of estimated failures. The reports indicate a set of beams not based on a data processing model.

[0138] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving a report containing a negative response (NACK) for all hybrid automatic retransmission request-acknowledgment (HARQ-ACK) information bits, in accordance with the determination of the occurrence of an estimated failure.

[0139] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving a report containing HARQ-ACK information bits on the uplink channel in accordance with the determination of the occurrence of an estimated failure. The uplink channel includes an estimated failure indicator.

[0140] In some embodiments, the network device includes circuitry configured to receive reports from terminal devices by receiving reports on the uplink channel according to the determination of the occurrence of estimated failures. The uplink channel is scrambled with an artificial intelligence (AI) or machine learning (ML) specific scrambling sequence associated with the estimated failure.

[0141] In some embodiments, the scrambling sequence is generated based on the AL or ML's Radio Network Temporary Identification Information (RNTI), or the scrambling sequence is an AI / ML-specific pseudo-random sequence, or the scrambling sequence is an AI / ML-specific mask.

[0142] Figure 9 is a schematic block diagram of a device 900 suitable for carrying out embodiments of the present disclosure. The device 900 can be considered a further exemplary embodiment of the terminal device 110 shown in Figure 1. Therefore, the device 900 can be implemented in or at least as part of the terminal device 110. Alternatively, the device 900 can be considered a further exemplary implementation of the network device 120 shown in Figure 1. Therefore, the device 900 can be implemented in or at least as part of the network device 120.

[0143] As shown in the figure, the device 900 includes a processor 910, a memory 920 coupled to the processor 910, appropriate transmitters (TX) and receivers (RX) 940 coupled to the processor 910, and a communication interface coupled to the TX / RX 940. The memory 920 stores at least a portion of the program 930. The TX / RX 940 is for bidirectional communication. The TX / RX 940 has at least one antenna to facilitate communication, although in practice the access node described herein may have multiple antennas. The communication interface may represent any interface necessary for communication with other network elements, for example, 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 a Uu interface for communication between an eNB and a terminal device.

[0144] The program 930 is deemed to include program instructions, and when the program is executed by the associated processor 910, it enables the device 900 to operate according to embodiments of the disclosure, as discussed herein with reference to Figures 2 to 8. Embodiments of the disclosure may be implemented by computer software, hardware, or a combination of software and hardware that can be executed by the processor 910 of the device 900. The processor 910 may be configured to implement various embodiments of the disclosure. Alternatively, a combination of the processor 910 and memory 920 may constitute processing means 950 suitable for implementing various embodiments of the disclosure.

[0145] Memory 920 may be of any type suitable for the local technical network and may be implemented using any suitable data storage technology (e.g., computer-readable non-temporary storage media, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and movable memory, etc.). Although only one memory 920 is shown for device 900, device 900 may have multiple physically different memory modules. Processor 910 may be of any type suitable for the local technical network and may include, but is not limited to, one or more of the following: general-purpose computers, dedicated computers, microprocessors, digital signal processors (DSPs), and processors based on multi-core processor configurations. Device 900 may have multiple processors, for example, application-specific integrated circuit chips that are time-dependent to a clock synchronized with a master processor.

[0146] Typically, various embodiments of the present disclosure may be implemented by hardware or dedicated circuitry, software, logic, or any combination thereof. Some embodiments may be implemented by hardware, while others may be implemented by firmware or software that can be executed by a controller, microprocessor, or other computing device. Various embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or by any other pictorial representation, and it will be understood that the blocks, apparatus, systems, techniques, or methods described herein may be implemented by, for example, hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or any combination thereof, but are not limited thereto.

[0147] This disclosure further provides at least one computer program product stored in tangible form on a computer-readable non-temporary storage medium. The computer program product includes computer-executable instructions, such as instructions contained within a program module. These instructions are executed on a device on a target real or virtual processor, performing the processes or methods described above, for example, with reference to Figures 2 to 8. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform a specific task or implement a specific abstract data type. In various embodiments, the functions of program modules may be combined or divided among program modules as needed. The machine-readable instructions of a program module may be executed within a local or distributed device. In a distributed device, the program module may reside on either a local or remote storage medium.

[0148] Program code for performing the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, a dedicated computer, or other programmable data processing device, and when the program code is executed by the processor or controller, the functions / operations defined in the flowcharts and / or block diagrams are performed. The program code may run entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0149] The program code described above may be implemented on a machine-readable medium, which may be any tangible medium containing or storing a program used by an instruction execution system, apparatus, or device, or a program used in conjunction with such a system or device. 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More specific examples of machine-readable storage media include one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable and writable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0150] While the operations have been described in a specific order, it should not be understood that, in order to obtain the desired results, these operations must be performed in a specific order or sequence, or that all of the operations shown must be performed. In some situations, multitasking and parallel processing may be advantageous. Similarly, the above discussion includes some specific implementation details, which should be interpreted not as limitations on the scope of this disclosure, but as descriptions of features that may be specific to particular embodiments. Some features described in the context of individual embodiments may be implemented in combination in one embodiment. Conversely, various features described in the context of one embodiment may be implemented separately or in any suitable secondary combination in multiple embodiments.

[0151] While this disclosure has been described using terminology specific to structural features and / or methodological behavior, it should be understood that this disclosure, as defined by the attached claims, is not necessarily limited to the specific features or behaviors described above. Rather, the specific features and behaviors described above are disclosed as exemplary forms for implementing the claims.

[0152] In this specification, the term "terminal device" refers to any device having wireless or wired communication capabilities. Examples of terminal devices include user terminals (UEs), personal computers, desktops, mobile phones, cell phones, smartphones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, IoT (Internet of Things) devices, ultra-high reliability low latency (URLLC) devices, IoE (Internet of Everything) devices, machine-type communication (MTC) equipment, vehicle-mounted equipment for V2X communication (where X means pedestrian, vehicle, or infrastructure / network), IAB (Integrated Access and Backhaul) devices, spacecraft or aircraft in non-terrestrial networks (NTN) including HAP (High Altitude Platforms) encompassing unmanned aerial vehicle systems (UAS), and XR (Extended Reality) including different types of reality such as augmented reality (AR), mixed reality (MR), and virtual reality (VR). Examples of "terminal devices" include, but are not limited to, Reality devices, unmanned aerial vehicles (UAVs) that do not require a human pilot, commonly known as drones, devices on high-speed trains (HSTs), imaging devices such as digital cameras, sensors, gaming devices, music storage and playback devices, or internet devices that enable wireless / wired internet access and browsing. "Terminal devices" may also have multicast / broadcast capabilities and support public safety, mission-critical, V2X applications, transparent IPv4 / IPv6 multicast distribution, IPTV, smart TV, wireless services, wireless software distribution, group communications, and IoT applications. They may also incorporate one or more subscriber identification modules (SIMs), known as multi-SIMs. The term "terminal device" can be used interchangeably with UE, mobile station, subscriber equipment, mobile terminal, user terminal, or wireless device.

[0153] The term "network device" refers to a device that can provide or host a cell or coverage from which terminal devices can communicate. Examples of network devices include, but are not limited to, Node B (Node or NB), Evolved Node (anode or eNB), Next Generation Node (gNB), Transmit / Receive Point (TRP), Remote Radio Unit (RRU), Radio Head (RH), Remote Radio Head (RRH), IAB nodes, femtonodes, piconodes and other low-power nodes, and RIS (Reconfigurable Intelligent Surface).

[0154] Terminal devices or network devices may have artificial intelligence (AL) or machine learning capabilities. Generally, this includes models that can be used to predict certain information by learning from a large amount of data collected for a specific function.

[0155] Terminal or network devices may operate in multiple frequency ranges, such as FR1 (410 MHz to 7125 MHz), FR2 (24.25 GHz to 71 GHz), frequency bands above 100 GHz, and terahertz (THz). Furthermore, they can operate in licensed / unlicensed / shared spectrum. In multi-radio dual connectivity (MR-DC) application scenarios, terminal devices may have multiple connections to network devices. Terminal or network devices can operate in full-duplex, flexible-duplex, and cross-division duplex modes.

[0156] Embodiments of the present disclosure may be implemented, for example, in test equipment such as signal generators, signal analyzers, spectrum analyzers, network analyzers, test terminal devices, test network devices, and channel emulators.

[0157] Embodiments of the present disclosure may be implemented in accordance with any generation of communication protocols currently known or to be 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 Network, or sixth-generation (6G) networks.

Claims

1. In the terminal device, downlink information to trigger a report is received from the network device, The terminal device determines whether or not an estimation failure has occurred in the data processing model. The report is transmitted to the network device, Includes, The report shall at least indicate whether the estimation failure occurred in the data processing model, The report includes at least one of the following: an estimated failure indicator, or channel status information (CSI) corresponding to the estimated failure indicator. The estimation failure indicator indicates whether or not the estimation failure occurred in the data processing model. The aforementioned report includes Part 1 and Part 2, The first part includes at least one of the following: the estimated failure indicator, the rank indicator (RI), the CSI reference signal (CSI-RS) resource indicator (CRI), the channel quality indicator (CQI), or the number of non-zero broadband amplitude coefficients per layer of the Type 2 CSI. The second part includes a precoding matrix indicator (PMI), Communication method.

2. Transmitting the aforementioned report to the network device means The determination of the occurrence of the estimated failure includes transmitting the report to the network device, The CSI field in the aforementioned report is set to a predefined bit value, or A portion of the CSI field is set to the predefined bit value. The method according to claim 1.

3. The downlink information indicates whether the report includes the estimated failure indicator. The method according to claim 1.

4. The aforementioned report consists of a single part, The single component includes at least one of the estimated failure indicator and the CSI corresponding to the estimated failure indicator. The method according to claim 1.

5. The aforementioned report includes Part 1 and Part 2, The first part includes the estimated failure indicator, The second part includes the CSI corresponding to the estimated failure indicator, The method according to claim 1.

6. The aforementioned report includes Part 1, Part 2, and Part 3, The first part includes the estimated failure indicator, The second part includes at least one of RI, CRI, CQI, or instruction, The third part mentioned above includes PMI, The method according to claim 1.

7. The determination that an enable parameter for enabling CSI feedback based on the data processing model is set in the terminal device further includes receiving a setting indicating the type of codebook from the network device, Transmitting the aforementioned report to the network device means The determination of the occurrence of the estimated failure includes sending the report, which includes the CSI based on the codebook, The method according to claim 1.

8. This further includes determining the type of codebook, Transmitting the aforementioned report to the network device means The report includes transmitting the report, which includes a bit field indicating the type of the codebook, to the network device. The method according to claim 1.

9. The downlink information indicates a first time offset and a second time offset that is greater than the first time offset, or The downlink information indicates the first time offset, and further downlink information indicates the second time offset. The method according to claim 1.

10. Transmitting the aforementioned report to the network device means The data processing model determines that no estimation failure has occurred, and the report is transmitted to the network device according to the first time offset. The aforementioned report shows a set of beams based on the data processing model, The second time offset is omitted. The method according to claim 9.

11. Transmitting the aforementioned report to the network device means The determination of the occurrence of the estimated failure includes transmitting the report to the network device according to the second time offset, The aforementioned report shows a set of beams not based on the aforementioned data processing model, The first time offset is omitted. The method according to claim 9.

12. In a network device, downlink information is sent to a terminal device to trigger a report. Receiving the report from the terminal device, Includes, The aforementioned report indicates at least whether or not estimation failures occurred in the data processing model. The report includes at least one of the following: an estimated failure indicator, or channel status information (CSI) corresponding to the estimated failure indicator. The estimation failure indicator indicates whether or not the estimation failure occurred in the data processing model. The aforementioned report includes Part 1 and Part 2, The first part includes at least one of the following: the estimated failure indicator, the rank indicator (RI), the CSI reference signal (CSI-RS) resource indicator (CRI), the channel quality indicator (CQI), or the number of non-zero broadband amplitude coefficients per layer of the Type 2 CSI. The second part includes a precoding matrix indicator (PMI), Communication method.

13. Processor and The aforementioned processor is coupled to a memory in which instructions are stored, Equipped with, If the instruction is executed by the processor, it performs an operation including the method according to any one of claims 1 to 12. Communication device.

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