Channel state information reporting configuration method, base station, user terminal, and communication system

Abstract

The present disclosure relates to a channel state information reporting configuration method, a base station, a user terminal and a communication system. The method comprises: for artificial intelligence-based channel state information prediction, the base station configures and optimizes the feedback reporting process of channel state information for the user terminal according to configuration conditions, wherein the configuration conditions include at least one of the network environment, the channel condition, the user terminal position moving speed, and the inference accuracy of the current cell. The present disclosure can configure and optimize the CSI feedback reporting process to support high-precision CSI prediction.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication, and in particular to a channel state information reporting configuration method, a base station, a user terminal, and a communication system. Background Technology

[0002] The related technologies, wireless air interface AI (Artificial Intelligence) / ML (Machine Learning) SI, define three initial use cases, including defining CSI (Channel State Information) feedback enhancement, beam management and positioning accuracy enhancement.

[0003] For the enhancement of CSI feedback for wireless air interface AI / ML, we will focus on CSI prediction based on unilateral model deployment as a representative sub-use case. According to the current standardization progress, AI / ML-based CSI prediction needs to add support for AI / ML-based CSI prediction function on the basis of the current 5G NR (New Radio) based CSI feedback, without affecting the existing CSI feedback function.

[0004] AI-based temporal CSI prediction utilizes temporal correlation and inputs historical information with timestamps to predict CSI information at future moments. This can help the network resist CSI feedback aging and achieve more accurate CSI recovery. Summary of the Invention

[0005] The inventors discovered through research that the specific definition of feedback reports for AI / ML-based CSI predictions has not yet been determined.

[0006] In view of at least one of the above technical problems, this disclosure provides a channel state information reporting configuration method, a base station, a user terminal, and a communication system, which can configure and optimize the CSI feedback reporting process to support high-precision CSI prediction.

[0007] According to one aspect of this disclosure, a channel state information reporting configuration method is provided, comprising:

[0008] For AI-based channel state information prediction, the base station configures and optimizes the channel state information feedback reporting process for user terminals according to configuration conditions. The configuration conditions include at least one of the following: the network environment of the cell, channel conditions, the moving speed of the user terminal, and inference accuracy.

[0009] In some embodiments of this disclosure, the user terminal position movement speed includes the user terminal position movement speed or the user terminal group position movement speed;

[0010] The feedback reporting process for the base station to configure and optimize channel state information for user terminals based on configuration conditions includes:

[0011] The base station configures and optimizes the feedback reporting process of channel status information for all user terminals, some user terminals, user terminal groups, or individual user terminals within the cell coverage area through higher-layer signaling based on configuration conditions.

[0012] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information includes:

[0013] The base station configures user terminals to use channel state information feature vectors and corresponding time information, and / or channel state information full channel matrix and corresponding time information as report content.

[0014] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information includes:

[0015] The base station is configured to allow user terminals to adaptively and selectively report channel status information for operating in multi-frequency, same-frequency, and adjacent-frequency bands.

[0016] In some embodiments of this disclosure, adaptive selective reporting includes any one of the following reporting methods, wherein:

[0017] The user terminal obtains channel state information for multiple frequency bands, same frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache. The user terminal adaptively selects one frequency band and / or multiple frequency bands and reports one and / or multiple moments of channel state information together.

[0018] Each time a user terminal acquires channel state information at a given moment while operating in multiple frequency, same frequency, or adjacent frequency bands, it reports the channel state information once.

[0019] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information includes:

[0020] The base station configures time and frequency resources for channel state information reporting for cell user terminals or user terminal groups to accommodate the reporting of different types of channel state information.

[0021] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information further includes: defining the relationship between the time-frequency resources of AI-based channel state information reporting and the bandwidth of the time-frequency resources of New Radio Channel State Information reporting as at least one of the following steps, wherein:

[0022] Artificial intelligence-based channel state information reporting occupies part or all of the frequency domain bandwidth resources and all time-frequency resources of the new air interface channel state information reporting.

[0023] AI-based channel state information reporting occupies part or all of the time domain resources and part of the bandwidth resources of the new air interface channel state information reporting.

[0024] The channel state information reporting based on artificial intelligence occupies part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting.

[0025] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information includes:

[0026] Define channel state information reporting based on artificial intelligence, where the time domain resources occupied are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols;

[0027] The channel state information reporting based on artificial intelligence is defined as the frequency domain resources occupied by continuous or non-contiguous physical resource blocks or a set of physical resource blocks in the partial bandwidth of the new air interface channel state information reporting.

[0028] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information includes at least one of the following steps, wherein:

[0029] Define a frequency domain resource indicator for channel state information reporting based on artificial intelligence;

[0030] Define a time-domain resource indicator for channel state information reporting based on artificial intelligence.

[0031] In some embodiments of this disclosure, the definition of the frequency domain resource indication for channel state information reporting based on artificial intelligence includes:

[0032] Using the lowest point of the carrier bandwidth frequency or the starting point of the bandwidth for reporting new air interface channel status information as a reference point, the sub-bands in the frequency domain resources that need to report channel status information are indicated in the form of a bitmap or by using a resource indicator value scheme. The indication method is to indicate the entire bandwidth or to indicate the frequency band configured for reporting new air interface channel status information.

[0033] In some embodiments of this disclosure, the definition of the time-domain resource indication for channel state information reporting based on artificial intelligence includes:

[0034] The time slots for reporting channel status information in the occupied time domain resources are indicated by configuring relevant parameters or by using a bitmap. The relevant parameters include at least one of time domain period, time slot offset, symbol start position, and number of symbols. The indication method is to indicate the time slots in the entire bandwidth or to indicate the time slots in the new air interface channel status information reporting time domain.

[0035] In some embodiments of this disclosure, the channel state information reporting configuration method further includes:

[0036] Based on the configuration conditions, the base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information.

[0037] In some embodiments of this disclosure, the base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information according to the configuration conditions, including:

[0038] The AI-based channel state information reporting is activated when at least one of the following activation conditions is met: the network environment and channel conditions are below a predetermined threshold, the moving speed of the user terminal or user terminal group is greater than a predetermined value, and the inference accuracy requirement is higher than a predetermined accuracy.

[0039] In some embodiments of this disclosure, the base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information according to the configuration conditions, including:

[0040] The AI-based channel state information reporting is deactivated when all of the following deactivation conditions are met: network environment and channel conditions are higher than a predetermined threshold, the moving speed of the user terminal or user terminal group is less than a predetermined value, and the inference accuracy requirement is lower than a predetermined accuracy.

[0041] According to another aspect of this disclosure, a channel state information reporting method is provided, comprising:

[0042] The user terminal receives configuration information sent by the base station, wherein the configuration information is configuration information configured by the base station according to the channel state information reporting configuration method described in any of the above embodiments;

[0043] The user terminal reports channel status information to the base station based on the configuration information.

[0044] In some embodiments of this disclosure, the user terminal reporting channel state information to the base station according to the configuration information includes at least one of the following steps, wherein:

[0045] User terminals use channel state information feature vectors and corresponding time information, and / or channel state information full channel matrix and corresponding time information as report content;

[0046] User terminals adaptively and selectively report channel status information for operating in multi-frequency, same-frequency, and adjacent-frequency bands;

[0047] User terminals report different types of channel state information based on the time and frequency resources configured by the base station for channel state information reports for cell user terminals or user terminal groups.

[0048] In some embodiments of this disclosure, the user terminal adaptively and selectively reports channel state information operating in multi-frequency, same-frequency, and adjacent-frequency bands, including:

[0049] The user terminal obtains channel state information for multiple frequency bands, same frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache. The user terminal adaptively selects one frequency band and / or multiple frequency bands and reports one and / or multiple moments of channel state information together.

[0050] Each time a user terminal acquires channel state information at a given moment while operating in multiple frequency, same frequency, or adjacent frequency bands, it reports the channel state information once.

[0051] In some embodiments of this disclosure, the user terminal reporting channel state information to the base station according to the configuration information includes at least one of the following steps, wherein:

[0052] Artificial intelligence-based channel state information reporting occupies part or all of the frequency domain bandwidth resources and all time-frequency resources of the new air interface channel state information reporting.

[0053] AI-based channel state information reporting occupies part or all of the time domain resources and part of the bandwidth resources of the new air interface channel state information reporting.

[0054] The channel state information reporting based on artificial intelligence occupies part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting.

[0055] In some embodiments of this disclosure, the time-domain resources occupied by the channel state information reporting based on artificial intelligence are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols.

[0056] In some embodiments of this disclosure, the frequency domain resources occupied by the AI-based channel state information reporting are continuous or non-contiguous physical resource blocks or a set of physical resource blocks in a portion of the bandwidth of the new air interface channel state information reporting.

[0057] In some embodiments of this disclosure, the user terminal reporting channel state information to the base station according to the configuration information includes:

[0058] The user terminal uses the lowest point of the carrier bandwidth frequency or the starting point of the bandwidth for reporting new air interface channel status information as a reference point. It uses a bitmap or a resource indicator value scheme to indicate the sub-band of the frequency domain resources that need to report channel status information. The indication method is to indicate it in the entire bandwidth band or in the band configured for reporting new air interface channel status information.

[0059] In some embodiments of this disclosure, the user terminal reporting channel state information to the base station according to the configuration information includes:

[0060] The user terminal indicates the time slots in the time domain resources that need to report channel status information by configuring relevant parameters or using bitmaps. The relevant parameters include at least one of time domain period, time slot offset, symbol start position and symbol number. The indication method is to indicate in the entire bandwidth or in the time domain configured for new air interface channel status information reporting.

[0061] In some embodiments of this disclosure, the user terminal reporting channel state information to the base station according to the configuration information includes:

[0062] According to the configuration information, the user terminal simultaneously reports the channel state information of artificial intelligence and the channel state information of the new air interface in the frequency domain bandwidth.

[0063] According to another aspect of this disclosure, a base station is provided, comprising:

[0064] The configuration module is configured to configure and optimize the feedback reporting process of channel state information for user terminals based on artificial intelligence-based channel state information prediction, according to configuration conditions. The configuration conditions include at least one of the following: network environment of the cell, channel conditions, user terminal location movement speed, and inference accuracy.

[0065] According to another aspect of this disclosure, a base station is provided, comprising:

[0066] The memory is configured to store instructions;

[0067] The processor is configured to execute the instructions, causing the base station to perform operations implementing the channel state information reporting configuration method as described in any of the above embodiments.

[0068] According to another aspect of this disclosure, a user terminal is provided, comprising:

[0069] The configuration information receiving module is configured to receive configuration information sent by the base station, wherein the configuration information is configuration information configured by the base station according to the channel state information reporting configuration method described in any of the above embodiments;

[0070] The channel state information reporting module is configured to report channel state information to the base station according to the configuration information.

[0071] According to another aspect of this disclosure, a user terminal is provided, comprising:

[0072] The memory is configured to store instructions;

[0073] The processor is configured to execute the instructions, causing the user terminal to perform operations implementing the channel state information reporting method as described in any of the above embodiments.

[0074] According to another aspect of this disclosure, a communication system is provided, including a base station as described in any of the above embodiments and a user terminal as described in any of the above embodiments.

[0075] According to another aspect of this disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the channel state information reporting configuration method as described in any of the foregoing embodiments, and / or the paging method as described in any of the foregoing embodiments.

[0076] This disclosure allows for the configuration and optimization of the CSI feedback reporting process to support high-accuracy CSI prediction. Attached Figure Description

[0077] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 These are schematic diagrams illustrating some embodiments of the public channel state information reporting configuration method.

[0079] Figure 2 These are schematic diagrams of other embodiments of the public channel state information reporting configuration method.

[0080] Figure 3 This is a schematic diagram illustrating the time-frequency resource usage of CSI reporting based on AI / ML in some embodiments of this disclosure.

[0081] Figure 4 This is a schematic diagram illustrating the time-frequency resource usage of CSI reporting based on AI / ML in other embodiments of this disclosure.

[0082] Figure 5This is a schematic diagram of time-frequency resource usage reported by AI / ML-based CSI in some embodiments of this disclosure.

[0083] Figure 6 This is a schematic diagram of some embodiments of the public channel state information reporting method.

[0084] Figure 7 This is a schematic diagram of some embodiments of the base station disclosed herein.

[0085] Figure 8 This is a schematic diagram of the structure of some other embodiments of the base station disclosed herein.

[0086] Figure 9 This is a schematic diagram of some embodiments of the user terminal disclosed herein.

[0087] Figure 10 This is a schematic diagram of the structure of some other embodiments of the user terminal disclosed herein.

[0088] Figure 11 This is a schematic diagram of the structure of some embodiments of the communication system disclosed herein. Detailed Implementation

[0089] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0090] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0091] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0092] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0093] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0094] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0095] The inventors also discovered through research that the traditional CSI information reporting in related technologies R16 / R17 does not have timestamp information, and the current CSI reporting method cannot be used for AI-based time-domain CSI prediction. Therefore, this disclosure requires the design of a channel state information reporting method with timestamp information.

[0096] When a base station provides CSI prediction services based on AI / ML, this disclosure requires defining corresponding frequency resources for uplink data transmission related to CSI prediction based on the BWP (Bandwidth Part) of the CSI feedback report based on relevant technologies.

[0097] The specific definition of feedback reports for AI / ML-based CSI predictions has not yet been determined.

[0098] Therefore, this disclosure requires the design of time-frequency resource allocation, occupancy, and indication methods for CSI prediction feedback reporting based on AI / ML.

[0099] The present disclosure will now be described through specific embodiments.

[0100] Figure 1 This diagram illustrates some embodiments of the channel state information reporting configuration method disclosed herein. Preferably, this embodiment can be executed by the base station or communication system disclosed herein. The method may include step 10, wherein:

[0101] Step 10: For channel state information prediction based on AI (Artificial Intelligence), the base station configures and optimizes the feedback reporting process of channel state information for the user terminal according to the configuration conditions. The configuration conditions include at least one of the following: network environment of the cell, channel conditions, user terminal location movement speed, inference accuracy, etc.

[0102] In some embodiments of this disclosure, artificial intelligence may include machine learning (ML).

[0103] The above embodiments of the present invention provide a method for reporting channel state information for wireless intelligent air interfaces.

[0104] Figure 2 These are schematic diagrams illustrating other embodiments of the channel state information reporting configuration method disclosed herein. Preferably, this embodiment can be executed by the base station or communication system disclosed herein. Figure 2 As shown, Figure 2 The channel state information reporting configuration method of the embodiment (e.g.) Figure 1 Step 10 of the embodiment may include at least one of steps 11 to 14, wherein:

[0105] Step 11, configuration for reporting CSI predictions based on AI / ML.

[0106] In some embodiments of this disclosure, the AI / ML-based CSI prediction employs a one-sided model inference approach deployed on the gNB side.

[0107] In some embodiments of this disclosure, the user terminal position movement speed includes the user terminal position movement speed or the user terminal group position movement speed.

[0108] In some embodiments of this disclosure, step 11 may include: a feedback reporting process in which the base station configures and optimizes channel state information for all user terminals, some user terminals, user terminal groups, or individual user terminals within the cell coverage area through higher-layer signaling according to configuration conditions.

[0109] In some embodiments of this disclosure, step 11 may include: for AI / ML-based CSI prediction, the base station configures and optimizes the CSI feedback reporting process for all UEs, some UEs, UE groups, and individual UEs within the cell coverage area through higher-layer signaling based on information such as the network environment, channel conditions, UE / UE group location movement speed, and inference accuracy of the cell. This is to support high-precision CSI prediction.

[0110] In some embodiments of this disclosure, the feedback reporting process for configuring and optimizing channel state information includes: the base station configuring the user terminal to use the channel state information feature vector and corresponding time information (CRI-V-TI), and / or the channel state information full channel matrix and corresponding time information as the report content (CRI-H-TI), wherein CRI-V is the channel state information feature vector, CRI-H is the channel state information full channel matrix, and CRI is (CSI-RS Resource Indicator).

[0111] In some embodiments of this disclosure, the content of the CSI prediction report can be defined as follows: channel state information feature vector and corresponding time information, and / or channel state information full channel matrix and corresponding time information, such as: CRI-V-TI and / or CRI-H-TI.

[0112] In some embodiments of this disclosure, the base station configures the user terminal with channel feature vectors and / or full-channel matrices containing time information and Doppler information as reporting content.

[0113] In some embodiments of this disclosure, step 11 may include: the base station configuring the user terminal to adaptively and selectively report channel state information operating in multi-frequency, same-frequency, and adjacent-frequency bands.

[0114] In some embodiments of this disclosure, step 11 may include: configuring the base station to allow the user terminal to adaptively and selectively report CSI measurement information operating in multi-frequency, same-frequency, and adjacent-frequency bands.

[0115] In some embodiments of this disclosure, adaptive selective reporting includes any one of the following reporting methods, wherein: the user terminal obtains channel state information (CSI measurement information) operating in multiple frequency bands, co-frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache; the user terminal adaptively selects one frequency band and / or multiple frequency bands and reports one and / or multiple moments of channel state information together; the user terminal reports channel state information once for each moment of channel state information obtained operating in multiple frequency bands, co-frequency bands, and adjacent frequency bands.

[0116] In some embodiments of this disclosure, step 11 may include: the base station configuring time-frequency resources for channel state information reporting for cell user terminals or user terminal groups to adapt to the reporting of different channel state information types.

[0117] In some embodiments of this disclosure, step 11 may include: the base station configuring time-frequency resources for AI / ML-based CSI reports for cell users / user groups to accommodate different CSI types of reporting.

[0118] Step 12: Configure the time and frequency resource usage for CSI reporting based on AI / ML.

[0119] In some embodiments of this disclosure, the time-frequency resources of the AI / ML-based CSI reporting are adapted to AI / ML-based CSI prediction and other AI / ML-based CSI enhancement techniques.

[0120] In some embodiments of this disclosure, step 12 may include: defining the relationship between the time-frequency resources of AI-based channel state information reporting and the bandwidth of the time-frequency resources of New Radio Channel State Information Reporting (NR traditional CSI reporting) as at least one of steps 121 to 123, wherein:

[0121] Step 121: Channel state information reporting based on artificial intelligence occupies part or all of the frequency domain bandwidth resources and all time-frequency resources of the new air interface channel state information reporting. Figure 3 This is a schematic diagram illustrating the time-frequency resource usage of CSI reporting based on AI / ML in some embodiments of this disclosure. For example... Figure 3As shown, AI / ML-based CSI reporting occupies part / all of the frequency domain BWP resources + all of the time-frequency resources of traditional NR CSI reporting.

[0122] Step 122: Channel state information reporting based on artificial intelligence occupies part or all of the time domain resources and part of the frequency domain bandwidth resources of the new air interface channel state information reporting. Figure 4 This is a schematic diagram illustrating the time-frequency resource usage of CSI reporting based on AI / ML in other embodiments of this disclosure. For example... Figure 4 As shown, AI / ML-based CSI reporting consumes part / all of the time domain resources + all of the BWP resources of traditional NR CSI reporting.

[0123] Step 123: Channel state information reporting based on artificial intelligence occupies part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting. Figure 5 This is a schematic diagram illustrating the time-frequency resource usage of CSI reporting based on AI / ML in some embodiments of this disclosure. For example... Figure 5 As shown, CSI reporting in AI / ML occupies part of the frequency domain BWP resources and part of the time domain resources of traditional NR CSI reporting.

[0124] In some embodiments of this disclosure, step 12 may further include: defining an AI-based channel state information reporting system, the time domain resources of which are at least one of continuous or discontinuous ( / periodic continuous) slots, mini-slots, and OFDM (Orthogonal Frequency-Division Multiplexing) symbols.

[0125] In some embodiments of this disclosure, step 12 may further include: defining an AI-based channel state information reporting, the frequency domain resources of which are continuous or non-continuous ( / periodic continuous) physical resource blocks (RBs) or a set of physical resource blocks (RBGs) in the partial bandwidth (BWP) of the new air interface channel state information reporting.

[0126] Step 13: Configure AI / ML-based CSI reporting time and frequency resource indication.

[0127] In some embodiments of this disclosure, step 13 may include at least one of steps 131 to 132, wherein:

[0128] Step 131: Define the frequency domain resource indicator for channel state information reporting based on artificial intelligence.

[0129] In some embodiments of this disclosure, step 131 may include: using the lowest point of the carrier bandwidth frequency PointA or the starting point of the new air interface channel state information reporting (NR traditional CSI reporting) partial bandwidth (BWP) as a reference point, indicating the sub-band of the occupied frequency domain resources that needs to report channel state information in the form of a bitmap or using the RIV (Resource Indicator Value) scheme, wherein the indication method is to indicate in the entire partial bandwidth frequency band, or to indicate in the frequency band configured for new air interface channel state information reporting.

[0130] In some embodiments of this disclosure, when no configuration is performed, it indicates that the frequency domain resources are fully occupied.

[0131] Step 132: Define the time-domain resource indicator for channel state information reporting based on artificial intelligence.

[0132] In some embodiments of this disclosure, step 132 may include: indicating the time slots in the occupied time domain resources that need to report channel state information by configuring relevant parameters or using a bitmap, wherein the relevant parameters include at least one of parameters such as time domain period, time slot offset, symbol start position and number of symbols, wherein the indication method is to indicate in the entire part of the bandwidth frequency band, or to indicate in the time domain configured for new air interface channel state information reporting.

[0133] In some embodiments of this disclosure, when no configuration is performed, it indicates that time-domain resources are fully occupied.

[0134] In some embodiments of this disclosure, the UE performs AI / ML and traditional NR CSI reporting simultaneously on the BWP, depending on the base station's resource configuration.

[0135] Step 14: The base station performs AI / ML-based CSI and NR traditional CSI listening and receiving on the BWP according to the configuration.

[0136] Step 15: The base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information according to the configuration conditions.

[0137] In some embodiments of this disclosure, step 15 may include: the base station may activate / deactivate monitoring and reception based on AI / ML and NR traditional CSI reports according to the network environment, channel conditions, UE / UE group location movement speed, inference accuracy, etc. of the cell.

[0138] In some embodiments of this disclosure, step 15 may include: activating AI-based channel state information reporting when at least one of the following activation conditions is met, wherein the activation conditions include network environment and channel conditions being lower than a predetermined threshold, the user terminal or user terminal group moving speed being greater than a predetermined value, and the inference accuracy requirement being higher than a predetermined accuracy.

[0139] For example, in situations where the network environment and channel conditions are poor, the UE / UE group moves quickly, and the inference accuracy requirement is high, this disclosure activates high-precision CSI reporting based on AI / ML.

[0140] In some embodiments of this disclosure, step 15 may include: deactivating the AI-based channel state information reporting when the following deactivation conditions are met, wherein the deactivation conditions include network environment and channel conditions being higher than a predetermined threshold, the user terminal or user terminal group moving speed being less than a predetermined value, and the inference accuracy requirement being lower than a predetermined accuracy.

[0141] For example, if the network environment and channel conditions are good, the UE / UE group location moves slowly, and there is no high accuracy requirement, then the high-precision CSI reporting based on AI / ML will be deactivated.

[0142] The embodiments of this disclosure based on AI / ML for CSI prediction add support for AI / ML-based CSI prediction functionality to the existing 5G NR-based CSI feedback without affecting the functionality of the existing CSI feedback.

[0143] The embodiments of this disclosure present a channel state information (CSI) reporting method with timestamp information. The AI-based time-domain CSI prediction of the embodiments of this disclosure utilizes time-domain correlation and inputs historical information with timestamps to predict CSI information at future moments, thereby assisting the network in resisting CSI feedback aging and achieving more accurate CSI recovery.

[0144] The embodiments of this disclosure specifically define the feedback report for CSI prediction based on AI / ML. The embodiments of this disclosure also design the time-frequency resource allocation, occupancy, and indication methods for CSI prediction feedback reporting based on AI / ML.

[0145] In the embodiments described above, when a base station provides an AI / ML-based CSI prediction service, it is necessary to define corresponding frequency resources based on the existing BWP (Browser Window) reported by CSI feedback for the transmission of uplink data information related to AI / ML-based CSI prediction.

[0146] There is currently no relevant configuration method for CSI reporting in AI / ML-based CSI prediction.

[0147] The embodiments of this disclosure support a variety of flexible configuration schemes for time-frequency resources based on AI / ML-based CSI prediction and for traditional 5G NR services, including partial / full occupation of time-frequency resources and configuration of continuous and discontinuous time-frequency resources. This enables base stations to make flexible and adaptive CSI feedback reporting configurations and adjustments for all UEs / partial UEs / UE groups / individual UEs within the cell coverage area based on information such as the network environment, channel conditions, UE / UE group location movement speed, and inference accuracy of the cell. Thus, the embodiments of this disclosure can, on the one hand, achieve cell-level / UE(group)-level dedicated configurations based on customer / scenario / service requirements for different cell and UE service conditions, and obtain high-precision CSI information; on the other hand, they can also meet the terminal energy-saving requirements by adaptively adjusting the time-frequency resource configuration.

[0148] The embodiments of this disclosure, for CSI reporting types based on AI / ML CSI prediction, select high-precision channel information such as feature vectors and / or full-channel matrices with Doppler information, along with corresponding timestamp information, as the report content. These are then used as input to the CSI predictor to obtain future high-precision CSI information. This effectively solves the problem of outdated CSI information, eliminates the need for additional radio resources, and improves system performance and resource utilization.

[0149] The above embodiments of this disclosure allow the UE to adaptively select reporting methods, including: 1) The UE acquires CSI measurement information operating in multiple frequency bands, co-frequency bands, adjacent frequency bands, and multiple historical moments, and stores it in the UE-side cache. The UE can adaptively select one frequency band and / or one or more moments of CSI from multiple frequency bands to report together; 2) The UE reports one CSI measurement information for each moment of CSI measurement information acquired from multiple frequency bands, co-frequency bands, and adjacent frequency bands. Therefore, the above embodiments of this disclosure allow for flexible multi-frequency collaborative measurement information reporting, enabling AI to intrinsically improve network resource utilization and system performance, meeting the intelligent, high-precision, high-efficiency, and low-power requirements of future 6G ultra-large-scale antenna technology and multi-spectrum fusion technology.

[0150] In addition, the adaptive full-spectrum reporting method of the above embodiments of this disclosure can effectively reduce unnecessary signaling interactions between base stations and terminals, enabling base stations and terminals to use their own resources more rationally and efficiently, and reducing energy consumption.

[0151] The relevant 3GPP has determined to study and design a time-domain CSI prediction scheme based on AI / ML. The above-disclosed embodiment of time-domain CSI prediction based on AI / ML uses CSI information for training during the model training phase. After the model training is completed, inference prediction is performed. This phase does not require frequent CSI information reporting, thereby occupying fewer RB / REs and achieving the goal of saving uplink resources.

[0152] The embodiments disclosed above can be applied to 5G / 6G and wireless AI CSI prediction. For current communication systems, the physical layer is the foundation for ensuring communication services; MIMO is a fundamental supporting technology for the physical layer; and for MIMO, accurately acquiring real-time channel quality and providing effective feedback and efficient utilization are essential key issues. In high-speed mobile scenarios, rapid channel changes caused by Doppler shift will lead to aging of the CSI obtained from channel estimation, resulting in nonlinear channel characteristics. This severely degrades the performance of high-speed mobile communication systems. AI / ML-based channel prediction, because it can predict future CSI in advance and offset delays through time spans, can effectively solve the problems of CSI aging and nonlinearity in high-speed mobile scenarios. Therefore, the embodiments of this disclosure have significant application value.

[0153] The above embodiments of this disclosure mainly address the problem of CSI information reporting by networks and terminals based on AI / ML-based CSI prediction. The above embodiments of this disclosure propose specific design schemes for CSI reporting configuration based on AI / ML and coexistence with traditional CSI reporting.

[0154] Figure 6 This is a schematic diagram of some embodiments of the channel state information reporting method disclosed herein. Preferably, this embodiment can be executed by the user terminal or the communication system disclosed herein. The method may include at least one of steps 50 to 60, wherein:

[0155] Step 50: The user terminal receives configuration information sent by the base station, wherein the configuration information is configured by the base station according to any of the above embodiments (e.g., Figure 1 or Figure 2 The configuration information configured in the channel state information reporting configuration method described in the embodiment)

[0156] Step 60: The user terminal reports channel status information to the base station according to the configuration information.

[0157] In some embodiments of this disclosure, step 60 may include at least one of steps 61 to 66, wherein:

[0158] Step 61: The user terminal uses the channel state information feature vector and the corresponding time information, and / or the channel state information full channel matrix and the corresponding time information as the report content.

[0159] Step 62: The user terminal adaptively and selectively reports channel status information for operating in multi-frequency, same-frequency, and adjacent-frequency bands.

[0160] In some embodiments of this disclosure, step 62 may include at least one of steps 621 to 622, wherein:

[0161] Step 621: The user terminal obtains channel state information for multiple frequency bands, same frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache. The user terminal adaptively selects one frequency band and / or multiple frequency bands and reports one and / or multiple moments of channel state information together.

[0162] Step 622: The user terminal reports the channel state information once for each moment when it obtains the channel state information at a time when it is operating in multiple frequency, same frequency, or adjacent frequency bands.

[0163] Step 63: The user terminal reports different types of channel state information based on the time and frequency resources configured by the base station for the channel state information report of the cell user terminal or user terminal group.

[0164] In some embodiments of this disclosure, step 63 may include at least one of steps 631 to 635, wherein:

[0165] Step 631, channel state information reporting based on artificial intelligence, occupies part or all of the frequency domain bandwidth resources and all time-frequency resources of the new air interface channel state information reporting, such as... Figure 3 As shown.

[0166] Step 632, channel state information reporting based on artificial intelligence, occupies part or all of the time domain resources and part of the frequency domain bandwidth resources of the new air interface channel state information reporting, such as... Figure 4 As shown.

[0167] Step 633, channel state information reporting based on artificial intelligence, occupies part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting, such as... Figure 5 As shown.

[0168] Step 634: Channel state information is reported based on artificial intelligence. The time domain resources occupied are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols.

[0169] Step 635, channel state information reporting based on artificial intelligence, the frequency domain resources occupied are continuous or non-contiguous physical resource blocks or a set of physical resource blocks in the partial bandwidth of the new air interface channel state information reporting.

[0170] Step 64: The user terminal uses the lowest point of the carrier bandwidth frequency or the starting point of the bandwidth for reporting new air interface channel status information as a reference point, and indicates the sub-band of the frequency domain resources that need to report channel status information in the form of a bitmap or by using a resource indicator value scheme. The indication method is to indicate in the entire bandwidth frequency band or in the frequency band configured for reporting new air interface channel status information.

[0171] Step 65: The user terminal indicates the time slot in the time domain resources occupied that needs to report channel status information by configuring relevant parameters or using a bitmap. The relevant parameters include at least one of time domain period, time slot offset, symbol start position and symbol number. The indication method is to indicate it in the entire bandwidth frequency band or in the time domain configured for new air interface channel status information reporting.

[0172] Step 66: The user terminal reports the channel state information of artificial intelligence and the channel state information of the new air interface simultaneously in the frequency domain bandwidth according to the configuration information.

[0173] Figure 7 This is a schematic diagram of some embodiments of the base station disclosed herein. Preferably, in this embodiment, the base station 70 may include a configuration module 71, wherein:

[0174] The configuration module 71 is configured to configure and optimize the feedback reporting process of channel state information for user terminals based on artificial intelligence-based channel state information prediction, according to configuration conditions, wherein the configuration conditions include at least one of the following: network environment of the cell, channel conditions, user terminal location movement speed, and inference accuracy.

[0175] In some embodiments of this disclosure, the user terminal position movement speed includes the user terminal position movement speed or the user terminal group position movement speed.

[0176] In some embodiments of this disclosure, the configuration module 71 is configured to configure and optimize the feedback reporting process of channel state information for all user terminals, some user terminals, user terminal groups, or individual user terminals within the cell coverage area through higher-layer signaling according to configuration conditions.

[0177] In some embodiments of this disclosure, the configuration module 71 is configured to configure the user terminal to use the channel state information feature vector and the corresponding time information, and / or the channel state information full channel matrix and the corresponding time information as the report content.

[0178] In some embodiments of this disclosure, the configuration module 71 is configured to configure the user terminal to adaptively and selectively report channel state information operating in multi-frequency, same-frequency, and adjacent-frequency bands.

[0179] In some embodiments of this disclosure, adaptive selective reporting includes any one of the following reporting methods, wherein: the user terminal obtains channel state information operating in multiple frequency bands, co-frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache; the user terminal adaptively selects one frequency band and / or multiple frequency bands and reports one and / or multiple moments of channel state information together; the user terminal reports channel state information once for each moment of channel state information obtained operating in multiple frequency bands, co-frequency bands, and adjacent frequency bands.

[0180] In some embodiments of this disclosure, the configuration module 71 is configured to configure time-frequency resources for channel state information reporting for cell user terminals or user terminal groups to adapt to the reporting of different channel state information types.

[0181] In some embodiments of this disclosure, the configuration module 71 is configured to define the relationship between the time-frequency resources of the AI-based channel state information reporting and the bandwidth of the time-frequency resources of the new air interface channel state information reporting as at least one of the following steps, wherein: the AI-based channel state information reporting occupies part or all of the frequency domain bandwidth resources and all of the time-frequency resources of the new air interface channel state information reporting; the AI-based channel state information reporting occupies part or all of the time domain resources and all of the frequency domain bandwidth resources of the new air interface channel state information reporting; and the AI-based channel state information reporting occupies the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting.

[0182] In some embodiments of this disclosure, the configuration module 71 is configured to define an AI-based channel state information reporting system, wherein the time domain resources occupied are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols; and to define an AI-based channel state information reporting system, wherein the frequency domain resources occupied are continuous or discontinuous physical resource blocks or a set of physical resource blocks in a portion of the bandwidth of the new air interface channel state information reporting system.

[0183] In some embodiments of this disclosure, configuration module 71 is configured to define a frequency domain resource indicator for channel state information reporting based on artificial intelligence; and to define a time domain resource indicator for channel state information reporting based on artificial intelligence.

[0184] In some embodiments of this disclosure, the configuration module 71 is configured to, when defining a frequency domain resource indication for channel state information reporting based on artificial intelligence, use the lowest point of the carrier bandwidth frequency or the starting point of the new air interface channel state information reporting portion bandwidth as a reference point, and indicate the sub-band of the occupied frequency domain resources that needs to report channel state information in the form of a bitmap or by adopting a resource indicator value scheme, wherein the indication method is to indicate in the entire portion bandwidth frequency band, or to indicate in the frequency band configured for new air interface channel state information reporting.

[0185] In some embodiments of this disclosure, the configuration module 71 is configured to, when defining a time-domain resource indication for channel state information reporting based on artificial intelligence, indicate the time slots in the occupied time-domain resources that need to report channel state information by configuring relevant parameters or using a bitmap. The relevant parameters include at least one of time-domain period, time slot offset, symbol start position, and number of symbols. The indication method is to indicate in the entire bandwidth or in the time domain configured for new air interface channel state information reporting.

[0186] In some embodiments of this disclosure, the configuration module 71 is configured to activate or deactivate the listening and receiving of AI-based channel state information and new air interface channel state information according to the configuration conditions.

[0187] In some embodiments of this disclosure, the configuration module 71 is configured to activate AI-based channel state information reporting when at least one of the following activation conditions is met, wherein the activation conditions include network environment and channel conditions being lower than a predetermined threshold, the user terminal or user terminal group moving speed being greater than a predetermined value, and the inference accuracy requirement being higher than a predetermined accuracy.

[0188] In some embodiments of this disclosure, the configuration module 71 is configured to deactivate the AI-based channel state information reporting when the following deactivation conditions are met, wherein the deactivation conditions include network environment and channel conditions being higher than a predetermined threshold, the user terminal or user terminal group moving speed being less than a predetermined value, and the inference accuracy requirement being lower than a predetermined accuracy.

[0189] In some embodiments of this disclosure, the base station is configured to perform implementations as described in any of the embodiments above (e.g., Figure 1 or Figure 2 The operation of the channel state information reporting configuration method involved in the embodiment) is described.

[0190] Figure 8 This is a schematic diagram of the structure of some other embodiments of the base station disclosed herein. For example... Figure 8 As shown, the base station includes a memory 81 and a processor 82.

[0191] Memory 81 is used to store instructions, and processor 82 is coupled to memory 81. Processor 82 is configured to execute instructions stored in memory to implement any of the above embodiments (e.g., Figure 1 or Figure 2 The embodiment involves a channel state information reporting configuration method.

[0192] like Figure 8 As shown, the base station also includes a communication interface 83 for exchanging information with other devices. Additionally, the base station includes a bus 84, through which the processor 82, communication interface 83, and memory 81 communicate with each other.

[0193] The memory 81 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive. The memory 81 may also be a memory array. The memory 81 may also be divided into blocks, and these blocks may be combined into virtual volumes according to certain rules.

[0194] Furthermore, processor 82 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure.

[0195] Figure 9 This is a schematic diagram of some embodiments of the user terminal of this disclosure. Preferably, this embodiment may include a configuration information receiving module 91 and a channel state information reporting module 92 in the user terminal of this disclosure, wherein:

[0196] The configuration information receiving module 91 is configured to receive configuration information sent by the base station, wherein the configuration information is generated by the base station according to any of the above embodiments (e.g., Figure 1 or Figure 2 The configuration information configured in the channel state information reporting configuration method described in the embodiment)

[0197] The channel state information reporting module 92 is configured to report channel state information to the base station according to the configuration information.

[0198] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to use the channel state information feature vector and the corresponding time information, and / or the channel state information full channel matrix and the corresponding time information as the reporting content.

[0199] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to adaptively and selectively report channel state information operating in multi-frequency, same-frequency, and adjacent-frequency bands.

[0200] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to report different types of channel state information based on the time-frequency resources of the channel state information report configured by the base station for the cell user terminal or user terminal group.

[0201] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to acquire channel state information operating in multiple frequency bands, same frequency bands, adjacent frequency bands, and multiple historical moments, store it in the user terminal cache, and adaptively select one frequency band and / or multiple frequency bands and report the channel state information of one and / or multiple moments together.

[0202] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to report channel state information once each time it acquires channel state information at a time when operating in multiple frequency, same frequency, or adjacent frequency bands.

[0203] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to report channel state information based on artificial intelligence, occupying part or all of the frequency domain bandwidth resources and all time-frequency resources of the new air interface channel state information reporting.

[0204] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to report channel state information based on artificial intelligence, occupying part or all of the time domain resources and part of the frequency domain bandwidth resources of the new air interface channel state information reporting.

[0205] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to report channel state information based on artificial intelligence, occupying part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting.

[0206] In some embodiments of this disclosure, the time-domain resources occupied by the channel state information reporting based on artificial intelligence are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols.

[0207] In some embodiments of this disclosure, the frequency domain resources occupied by the AI-based channel state information reporting are continuous or non-contiguous physical resource blocks or a set of physical resource blocks in a portion of the bandwidth of the new air interface channel state information reporting.

[0208] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to use the lowest point of the carrier bandwidth frequency or the starting point of the new air interface channel state information reporting portion bandwidth as a reference point to indicate the sub-bands in the occupied frequency domain resources that need to report channel state information in the form of a bitmap or by using a resource indicator value scheme. The indication method is to indicate in the entire portion bandwidth frequency band or in the frequency band configured for new air interface channel state information reporting.

[0209] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to indicate the time slots in the occupied time domain resources that need to report channel state information by configuring relevant parameters or using a bitmap. The relevant parameters include at least one of time domain period, time slot offset, symbol start position, and number of symbols. The indication method is to indicate in the entire part of the bandwidth or in the time domain configured for new air interface channel state information reporting.

[0210] In some embodiments of this disclosure, the channel state information reporting module 92 is configured to simultaneously report artificial intelligence channel state information and new air interface channel state information in the frequency domain portion of the bandwidth according to the configuration information.

[0211] In some embodiments of this disclosure, the user terminal is configured to perform an implementation as described in any of the embodiments above (e.g., Figure 6 The operation of the channel state information reporting method involved in the embodiment)

[0212] Figure 10 This is a schematic diagram of the structure of some other embodiments of the user terminal disclosed herein. Figure 10 The structure of the Chinese user terminal and Figure 8 The base stations are exactly the same, the difference is that Figure 10 The processor 82 of the user terminal is configured to implement any of the above embodiments based on memory-stored instructions (e.g., Figure 6 The method for reporting channel state information (see example).

[0213] Figure 11 This is a schematic diagram illustrating the structure of some embodiments of the communication system disclosed herein. For example... Figure 11 As shown, the communication system disclosed herein may include a base station 70 and a user terminal 90, wherein:

[0214] Base station 70 is as described in any of the above embodiments (e.g.) Figure 7 or Figure 8 The base station described in the example.

[0215] User terminal 90 is as described in any of the above embodiments (e.g.) Figure 9 or Figure 10 The user terminal described in the embodiment).

[0216] According to another aspect of this disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement any of the embodiments described above (e.g., Figure 1 or Figure 2 The channel state information reporting configuration method described in the embodiments, and / or as in any of the above embodiments (e.g.) Figure 6 The paging method described in the embodiment).

[0217] In some embodiments of this disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0218] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, apparatus, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0219] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0220] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0221] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0222] The service performance indicator acquisition device and service consumption network element described above can be implemented as a general-purpose processor, programmable logic controller (PLC), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any suitable combination thereof for performing the functions described in this application.

[0223] This concludes the detailed description of the present disclosure. To avoid obscuring the concept of the disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0224] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing the relevant hardware to implement them. The program can be stored in a non-transitory computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0225] The description in this disclosure is provided for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the disclosure to its forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of this disclosure and to enable those skilled in the art to understand this disclosure and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A channel state information reporting configuration method, comprising: For AI-based channel state information prediction, the base station configures and optimizes the feedback reporting process of channel state information for user terminals according to configuration conditions. The configuration conditions include at least one of the following: the network environment of the cell, channel conditions, the moving speed of the user terminal, and inference accuracy. The feedback reporting process for configuring and optimizing channel state information includes at least one of the following steps: Define a frequency domain resource indicator for channel state information reporting based on artificial intelligence; Define a time-domain resource indicator for channel state information reporting based on artificial intelligence.

2. The channel state information reporting configuration method according to claim 1, wherein, The user terminal position movement speed includes the user terminal position movement speed or the user terminal group position movement speed; The feedback reporting process for the base station to configure and optimize channel state information for user terminals based on configuration conditions includes: The base station configures and optimizes the feedback reporting process of channel status information for all user terminals, some user terminals, user terminal groups, or individual user terminals within the cell coverage area through higher-layer signaling based on configuration conditions.

3. The channel state information reporting configuration method according to claim 1 or 2, wherein, The feedback reporting process for configuring and optimizing channel state information includes: The base station configures user terminals to use channel state information feature vectors and corresponding time information, and / or channel state information full channel matrix and corresponding time information as report content.

4. The channel state information reporting configuration method according to claim 1 or 2, wherein, The feedback reporting process for configuring and optimizing channel state information includes: The base station is configured to allow user terminals to adaptively and selectively report channel status information for operating in multi-frequency, same-frequency, and adjacent-frequency bands.

5. The channel state information reporting configuration method according to claim 4, wherein, Adaptive selective reporting includes any of the following reporting methods, wherein: The user terminal obtains channel state information for multiple frequency bands, same frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache. The user terminal adaptively selects one frequency band and / or multiple frequency bands and reports the channel state information for one and / or multiple moments together. Each time a user terminal acquires channel state information at a given moment while operating in multiple frequency, same frequency, or adjacent frequency bands, it reports the channel state information once.

6. The channel state information reporting configuration method according to claim 1 or 2, wherein, The feedback reporting process for configuring and optimizing channel state information includes: The base station configures time and frequency resources for channel state information reporting for cell user terminals or user terminal groups to accommodate the reporting of different types of channel state information.

7. The channel state information reporting configuration method according to claim 1 or 2, wherein, The feedback reporting process for configuring and optimizing channel state information further includes: defining the relationship between the time-frequency resources reported based on artificial intelligence channel state information and the bandwidth of the time-frequency resources reported by the new air interface channel state information as at least one of the following steps, wherein: Artificial intelligence-based channel state information reporting occupies part or all of the frequency domain bandwidth resources and all of the time domain resources of the new air interface channel state information reporting. AI-based channel state information reporting occupies part or all of the time domain resources and part of the bandwidth resources of the new air interface channel state information reporting. The channel state information reporting based on artificial intelligence occupies part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting.

8. The channel state information reporting configuration method according to claim 1 or 2, wherein, The feedback reporting process for configuring and optimizing channel state information includes: Define channel state information reporting based on artificial intelligence, where the time domain resources occupied are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols; The channel state information reporting based on artificial intelligence is defined as the frequency domain resources occupied by continuous or non-contiguous physical resource blocks or a set of physical resource blocks in the partial bandwidth of the new air interface channel state information reporting.

9. The channel state information reporting configuration method according to claim 1 or 2, wherein, The definition of frequency domain resource indication based on channel state information reporting using artificial intelligence includes: Using the lowest point of the carrier bandwidth frequency or the starting point of the bandwidth for reporting new air interface channel status information as a reference point, the sub-bands in the frequency domain resources that need to report channel status information are indicated in the form of a bitmap or by using a resource indicator value scheme. The indication method is to indicate the entire bandwidth or to indicate the frequency band configured for reporting new air interface channel status information.

10. The channel state information reporting configuration method according to claim 1 or 2, wherein, The definition of time-domain resource indication based on artificial intelligence-based channel state information reporting includes: The time slots for reporting channel status information in the occupied time domain resources are indicated by configuring relevant parameters or by using a bitmap. The relevant parameters include at least one of time domain period, time slot offset, symbol start position, and number of symbols. The indication method is to indicate the time slots in the entire bandwidth or to indicate the time slots in the new air interface channel status information reporting time domain.

11. The channel state information reporting configuration method according to claim 1 or 2, further comprising: Based on the configuration conditions, the base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information.

12. The channel state information reporting configuration method according to claim 11, wherein, The base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information according to the configuration conditions, including: The AI-based channel state information reporting is activated when at least one of the following activation conditions is met: the network environment and channel conditions are below a predetermined threshold, the moving speed of the user terminal or user terminal group is greater than a predetermined value, and the inference accuracy requirement is higher than a predetermined accuracy.

13. The channel state information reporting configuration method according to claim 11, wherein, The base station activates or deactivates the monitoring and reception of AI-based channel state information and new air interface channel state information according to the configuration conditions, including: The AI-based channel state information reporting is deactivated when all of the following deactivation conditions are met: network environment and channel conditions are higher than a predetermined threshold, the moving speed of the user terminal or user terminal group is less than a predetermined value, and the inference accuracy requirement is lower than a predetermined accuracy.

14. A method for reporting channel state information, comprising: The user terminal receives configuration information sent by the base station, wherein the configuration information is configuration information configured by the base station according to the channel state information reporting configuration method as described in any one of claims 1-13; The user terminal reports channel status information to the base station based on the configuration information.

15. The channel state information reporting method according to claim 14, wherein, The user terminal reporting channel status information to the base station according to the configuration information includes at least one of the following steps: User terminals use channel state information feature vectors and corresponding time information, and / or channel state information full channel matrix and corresponding time information as report content; User terminals adaptively and selectively report channel status information for operating in multi-frequency, same-frequency, and adjacent-frequency bands; User terminals report different types of channel state information based on the time and frequency resources configured by the base station for channel state information reports for cell user terminals or user terminal groups.

16. The channel state information reporting method according to claim 15, wherein, The user terminal adaptively and selectively reports channel state information operating in multi-frequency, same-frequency, and adjacent-frequency bands, including: The user terminal obtains channel state information for multiple frequency bands, same frequency bands, adjacent frequency bands, and multiple historical moments and stores it in the user terminal cache. The user terminal adaptively selects one frequency band and / or multiple frequency bands and reports the channel state information for one and / or multiple moments together. Each time a user terminal acquires channel state information at a given moment while operating in multiple frequency, same frequency, or adjacent frequency bands, it reports the channel state information once.

17. The channel state information reporting method according to any one of claims 14-16, wherein, The user terminal reporting channel status information to the base station according to the configuration information includes at least one of the following steps: Artificial intelligence-based channel state information reporting occupies part or all of the frequency domain bandwidth resources and all of the time domain resources of the new air interface channel state information reporting. AI-based channel state information reporting occupies part or all of the time domain resources and part of the bandwidth resources of the new air interface channel state information reporting. The channel state information reporting based on artificial intelligence occupies part of the frequency domain bandwidth resources and part of the time domain resources of the new air interface channel state information reporting.

18. The channel state information reporting method according to any one of claims 14-16, wherein: The time-domain resources used for channel state information reporting based on artificial intelligence are at least one of continuous or discontinuous time slots, micro-time slots, and orthogonal frequency division multiplexing symbols. The frequency domain resources occupied by AI-based channel state information reporting are continuous or non-contiguous physical resource blocks or sets of physical resource blocks in a portion of the bandwidth of the new air interface channel state information reporting.

19. The channel state information reporting method according to any one of claims 14-16, wherein, The user terminal reports channel status information to the base station according to the configuration information, including: The user terminal uses the lowest point of the carrier bandwidth frequency or the starting point of the bandwidth for reporting new air interface channel status information as a reference point. It uses a bitmap or a resource indicator value scheme to indicate the sub-band of the frequency domain resources that need to report channel status information. The indication method is to indicate it in the entire bandwidth band or in the band configured for reporting new air interface channel status information.

20. The channel state information reporting method according to any one of claims 14-16, wherein, The user terminal reports channel status information to the base station according to the configuration information, including: The user terminal indicates the time slots in the time domain resources that need to report channel status information by configuring relevant parameters or using bitmaps. The relevant parameters include at least one of time domain period, time slot offset, symbol start position and symbol number. The indication method is to indicate in the entire bandwidth or in the time domain configured for new air interface channel status information reporting.

21. The channel state information reporting method according to any one of claims 14-16, wherein, The user terminal reports channel status information to the base station according to the configuration information, including: According to the configuration information, the user terminal simultaneously reports the channel state information of artificial intelligence and the channel state information of the new air interface in the frequency domain bandwidth.

22. A base station, comprising: The configuration module is configured to configure and optimize the feedback reporting process of channel state information for user terminals based on artificial intelligence-based channel state information prediction, according to configuration conditions. The configuration conditions include at least one of the following: network environment of the cell, channel conditions, user terminal location movement speed, and inference accuracy.

23. A base station, comprising: The memory is configured to store instructions; The processor is configured to execute the instructions, causing the base station to perform operations implementing the channel state information reporting configuration method as described in any one of claims 1-13.

24. A user terminal, comprising: The configuration information receiving module is configured to receive configuration information sent by the base station, wherein the configuration information is configuration information configured by the base station according to the channel state information reporting configuration method as described in any one of claims 1-13; The channel state information reporting module is configured to report channel state information to the base station according to the configuration information.

25. A user terminal, comprising: The memory is configured to store instructions; The processor is configured to execute the instructions, causing the user terminal to perform operations implementing the channel state information reporting method as described in any one of claims 14-21.

26. A communication system comprising a base station as claimed in claim 22 or 23, and a user terminal as claimed in claim 24 or 25.

27. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the channel state information reporting configuration method as described in any one of claims 1-13, and / or the channel state information reporting method as described in any one of claims 14-21.

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