Communication processing method, terminal, device, communication system and storage medium

Abstract

Provided in the embodiments of the present disclosure are a communication processing method, a terminal, a device, a communication system and a storage medium. The communication processing method comprises: on the basis of a first identifier, a first terminal determining a data set corresponding to the first identifier, wherein different data sets are associated with corresponding auxiliary information, the auxiliary information is configured for indicating feature information corresponding to the data sets, and the data sets are configured for prediction based on artificial intelligence (AI) or machine learning (ML). In the method of the present disclosure, the data sets in the prediction based on AI or ML can be classified by means of the first identifier, and the feature information of the data sets can be referenced, so that the characteristics or uses of the data sets are combined in the classification, thereby helping to improve the rationality and efficiency of data set collection and application.

Description

Communication processing method, terminal, device, communication system and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to a communication processing method, terminal, device, communication system, and storage medium. Background Art

[0002] In predictions based on artificial intelligence (AI) / machine learning (ML), such as channel state information prediction, beam prediction, and positioning information prediction, many different types of data are involved, and different types of data can be processed or applied differently.

[0003] Summary of the Invention

[0004] The potential impact of data collection on AI- or ML-based predictions needs to be considered.

[0005] The embodiments of the present disclosure provide a communication processing method, a terminal, a device, a communication system, and a storage medium.

[0006] In a first aspect, an embodiment of the present disclosure provides a communication processing method, including:

[0007] The first terminal determines a data set corresponding to the first identifier according to the first identifier, and associates corresponding auxiliary information with different data sets;

[0008] The auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on artificial intelligence AI or machine learning ML.

[0009] In the method disclosed herein, the data set in AI or ML-based prediction can be classified by the first identifier, and the characteristic information of the data set can be referred to, so that the characteristics or uses of the data set can be combined in the classification, which is conducive to improving the rationality and efficiency of data set collection and application.

[0010] In a second aspect, an embodiment of the present disclosure provides a communication processing method, including:

[0011] The first device sends first information to the first terminal, where the first information includes a first data set, the auxiliary information corresponding to the first data set, and / or a first identifier corresponding to the first data set;

[0012] The first terminal is used to determine the data set corresponding to the first identifier based on the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

[0013] In a third aspect, an embodiment of the present disclosure provides a terminal, including:

[0014] a transceiver module, configured to determine a data set corresponding to a first identifier according to the first identifier, and to associate corresponding auxiliary information with different data sets;

[0015] The auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on artificial intelligence AI or machine learning ML.

[0016] In a fourth aspect, an embodiment of the present disclosure provides a device, including:

[0017] a transceiver module, configured to send first information to a first terminal, where the first information includes a first data set, the auxiliary information corresponding to the first data set, and / or a first identifier corresponding to the first data set;

[0018] The first terminal is used to determine the data set corresponding to the first identifier based on the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

[0019] In a fifth aspect, an embodiment of the present disclosure provides a terminal, including:

[0020] one or more processors;

[0021] The terminal is used to execute the method described in the first aspect.

[0022] In a sixth aspect, an embodiment of the present disclosure provides a device, including:

[0023] one or more processors;

[0024] The device is used to execute the method described in the second aspect.

[0025] In a seventh aspect, an embodiment of the present disclosure provides a communication system, including a first terminal and a first device, wherein:

[0026] The first terminal is configured to execute the method according to the first aspect;

[0027] The first device is configured to perform the method described in the second aspect.

[0028] In an eighth aspect, an embodiment of the present disclosure provides a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0030] FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0031] Figures 2a and 2b are exemplary interaction diagrams of a method provided according to an embodiment of the present disclosure;

[0032] FIG2c is a schematic diagram of two CSIs provided according to an embodiment of the present disclosure;

[0033] 3a to 3e are exemplary flowcharts of a method according to an embodiment of the present disclosure;

[0034] 4a to 4e are exemplary flowcharts of a method according to an embodiment of the present disclosure;

[0035] FIG5a is a schematic structural diagram of a terminal according to an embodiment of the present disclosure;

[0036] FIG5b is a schematic structural diagram of a first device according to an embodiment of the present disclosure;

[0037] FIG6a is a schematic diagram of a communication device according to an embodiment of the present disclosure;

[0038] FIG6 b is a schematic diagram of a communication device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] The embodiments of the present disclosure provide a communication processing method, a terminal, a device, a communication system, and a storage medium.

[0040] In a first aspect, an embodiment of the present disclosure provides a communication processing method, including:

[0041] The first terminal determines a data set corresponding to the first identifier according to the first identifier, and associates corresponding auxiliary information with different data sets;

[0042] The auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on artificial intelligence AI or machine learning ML.

[0043] In the above embodiment, the data set in the AI ​​or ML-based prediction can be classified by the first identifier, and the characteristic information of the data set can be referred to, so that the characteristics or uses of the data set can be combined in the classification, which is conducive to improving the rationality and efficiency of the data set collection and application process.

[0044] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0045] The first terminal receives first information sent by a first device, where the first information includes a first data set, and the auxiliary information corresponding to the first data set and / or a first identifier corresponding to the first data set.

[0046] In the above embodiment, the first terminal can obtain a data set, as well as auxiliary information and / or a first identifier corresponding to the data set from other entities such as the first device, to obtain the required data set in an interactive manner, and can reasonably apply the data set based on the classification of the data set.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0048] The first terminal sends second information to the first device, where the second information is used to indicate a first identifier corresponding to the second data set requested by the first terminal;

[0049] The first terminal receives the second data set and / or the auxiliary information corresponding to the second data set sent by the first device.

[0050] In the above embodiment, the first terminal may send the first identifier to the first device to request the data set and / or auxiliary information corresponding to the first identifier, so as to perform a corresponding application based on the obtained data.

[0051] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0052] The first terminal sends capability information to the first device, where the capability information includes auxiliary information corresponding to a third data set to which the model is applicable or a second identifier of the corresponding auxiliary information.

[0053] In the above embodiment, the first terminal may report capability information so that the first device may learn about the model-related capabilities of the first terminal.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, the second identifier includes at least one of the following:

[0055] a first identifier corresponding to the third data set;

[0056] an identification of the model;

[0057] functional identification of the model;

[0058] The model identifier and / or the function identifier corresponds to the first identifier.

[0059] In the above embodiment, the second identifier reported by the first terminal may be associated with auxiliary information or a model, so that the first device may clearly know the capabilities related to the model on the first terminal side.

[0060] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0061] The first terminal receives third information sent by the first device, where the third information includes a first identifier corresponding to the third data set, and the third information is used to request auxiliary information corresponding to the third data set;

[0062] The first terminal sends auxiliary information corresponding to the third data set to the first device.

[0063] In the above embodiment, the first terminal may send the auxiliary information required by the first device to the first device according to the request of the first device, so that the first device can obtain the required information, which facilitates the reasonable use of the model.

[0064] In conjunction with some embodiments of the first aspect, in some embodiments, the first device is one of the following:

[0065] Network equipment;

[0066] Second terminal;

[0067] server.

[0068] In the above embodiment, the first terminal may transmit the required data set or auxiliary information based on interaction with the network device, other terminals or servers, so as to facilitate reasonable application of the obtained data.

[0069] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0070] The first terminal receives configuration information sent by a first device, where the configuration information is used to configure the auxiliary information, wherein the first device is a network device.

[0071] In the above embodiment, the auxiliary information may be configured by the network device, which enables flexibility in defining the auxiliary information.

[0072] In combination with some embodiments of the first aspect, in some embodiments, the auxiliary information is predefined.

[0073] In the above embodiment, the auxiliary information may be predefined to be applied to the server, and may reduce configuration operations of the network device and save wireless resources.

[0074] In conjunction with some embodiments of the first aspect, in some embodiments, the dataset includes at least one of the following:

[0075] Channel state information CSI data set;

[0076] Beam identification dataset;

[0077] Beam reference signal received power RSRP dataset;

[0078] Positioning information dataset.

[0079] In the above embodiments, in various information predictions based on AI or ML, the first identifier can be used to classify various data sets, so that reasonable applications can be made according to the categories of the data sets in the predictions based on AI or ML.

[0080] In combination with some embodiments of the first aspect, in some embodiments, the dataset is a CSI dataset, and the dataset is used for at least one of the model training stage, model inference stage, and model supervision stage of CSI prediction based on AI or ML.

[0081] In the above embodiment, at different stages of AI or ML-based CSI prediction, the data sets involved in the different stages may be classified according to the first identifier to improve the processing and utilization efficiency of the data sets.

[0082] In conjunction with some embodiments of the first aspect, in some embodiments, the auxiliary information includes at least one of the following:

[0083] The time interval between adjacent CSIs;

[0084] The number of CSIs;

[0085] Frequency domain characteristics;

[0086] Movement speed;

[0087] scene.

[0088] In the above embodiment, corresponding auxiliary information can be defined according to different feature dimensions of the data set, so that different features of the data set can be fully considered in the process of classifying the data set, thereby improving the accuracy and rationality of the classification.

[0089] In conjunction with some embodiments of the first aspect, in some embodiments, the time interval of adjacent CSIs includes at least one of the following:

[0090] The time interval between adjacent CSI measurements in the observation window;

[0091] The time interval between adjacent predicted CSIs in the prediction window;

[0092] The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window;

[0093] The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

[0094] In the above-described embodiments, the AI- or ML-based CSI prediction includes observation windows and prediction windows. The first terminal behaves differently in different windows and needs to know the intervals between adjacent CSI values ​​in a dataset so that the first terminal can properly process or apply the data based on the relevant characteristics of the dataset. Specifically, by combining the CSI intervals in different observation windows, the relevant datasets can be used to predict CSI based on different measured CSI intervals; and by combining the CSI intervals in different prediction windows, the relevant datasets can be used to obtain predicted CSI at different intervals.

[0095] In conjunction with some embodiments of the first aspect, in some embodiments, the number of CSIs includes at least one of the following:

[0096] The number of measured CSIs in the observation window;

[0097] The number of predicted CSIs in the prediction window.

[0098] In the above embodiment, the number of CSIs is helpful in indicating the characteristics of the data set in the corresponding window, so that the first terminal can reasonably process or apply the data set according to the relevant characteristics of the data set.

[0099] In combination with some embodiments of the first aspect, in some embodiments, the data set includes one or more data, and when the data set is a CSI data set, the data is a channel state information reference signal CSI-RS or CSI.

[0100] In the above embodiments, in the AI ​​or ML-based CSI prediction, the relevant data may be either CSI-RS or CSI, so that the first terminal may train or apply a model based on the CSI or the CSI obtained by CSI-RS measurement.

[0101] In conjunction with some embodiments of the first aspect, in some embodiments, the CSI is at least one of the following:

[0102] Channel matrix information;

[0103] Channel feature vector information.

[0104] In the above embodiment, the CSI may be information related to the feedback channel state, so that the network device can perform reasonable channel assessment after receiving the prediction result of the first terminal.

[0105] In a second aspect, an embodiment of the present disclosure provides a communication processing method, including:

[0106] The first device sends first information to the first terminal, where the first information includes a first data set, the auxiliary information corresponding to the first data set, and / or a first identifier corresponding to the first data set;

[0107] The first terminal is used to determine the data set corresponding to the first identifier based on the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

[0108] In the above embodiment, the data set in the AI ​​or ML-based prediction can be classified by the first identifier, and the characteristic information of the data set can be referred to, so that the characteristics or uses of the data set can be combined in the classification, which is conducive to improving the rationality and efficiency of data set collection and application.

[0109] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0110] The first device receives second information sent by the first terminal, where the second information is used to indicate a first identifier corresponding to a second data set requested by the first terminal;

[0111] The first device sends the auxiliary information corresponding to the second data set and / or the second data set to the first terminal.

[0112] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0113] The first device receives capability information sent by the first terminal, where the capability information includes auxiliary information corresponding to a third data set to which the model is applicable or a second identifier of the corresponding auxiliary information.

[0114] In conjunction with some embodiments of the second aspect, in some embodiments, the second identifier includes at least one of the following:

[0115] a first identifier corresponding to the third data set;

[0116] an identification of the model;

[0117] functional identification of the model;

[0118] The model identifier and / or the function identifier corresponds to the first identifier.

[0119] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0120] The first device sends third information to the first terminal, where the third information includes a first identifier corresponding to the third data set, and the third information is used to request auxiliary information corresponding to the third data set;

[0121] The first device receives auxiliary information corresponding to the third data set sent by the first terminal.

[0122] In conjunction with some embodiments of the second aspect, in some embodiments, the first device is one of the following:

[0123] Network equipment;

[0124] Second terminal;

[0125] server.

[0126] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0127] The first device sends configuration information to the first terminal, where the configuration information is used to configure the auxiliary information, and the first device is the network device.

[0128] In conjunction with some embodiments of the second aspect, in some embodiments, the dataset includes at least one of the following:

[0129] CSI dataset;

[0130] Beam identification dataset;

[0131] Beam RSRP dataset;

[0132] Positioning information dataset.

[0133] In combination with some embodiments of the second aspect, in some embodiments, the dataset is a CSI dataset, and the dataset is used for at least one of the model training stage, model inference stage, and model supervision stage of CSI prediction based on AI or ML.

[0134] In conjunction with some embodiments of the second aspect, in some embodiments, the auxiliary information includes at least one of the following:

[0135] The time interval between adjacent CSIs;

[0136] The number of CSIs;

[0137] Frequency domain characteristics;

[0138] Movement speed;

[0139] scene.

[0140] In conjunction with some embodiments of the second aspect, in some embodiments, the time interval of adjacent CSIs includes at least one of the following:

[0141] The time interval between adjacent CSI measurements in the observation window;

[0142] The time interval between adjacent predicted CSIs in the prediction window;

[0143] The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window;

[0144] The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

[0145] In conjunction with some embodiments of the second aspect, in some embodiments, the number of CSIs includes at least one of the following:

[0146] The number of measured CSIs in the observation window;

[0147] The number of predicted CSIs in the prediction window.

[0148] In combination with some embodiments of the second aspect, in some embodiments, the data set includes one or more data, and when the data set is a CSI data set, the data is a channel state information reference signal CSI-RS or CSI.

[0149] In conjunction with some embodiments of the second aspect, in some embodiments, the CSI is at least one of the following:

[0150] Channel matrix information;

[0151] Channel feature vector information.

[0152] In a third aspect, an embodiment of the present disclosure provides a terminal, including:

[0153] a transceiver module, configured to determine a data set corresponding to a first identifier according to the first identifier, and to associate corresponding auxiliary information with different data sets;

[0154] The auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on artificial intelligence AI or machine learning ML.

[0155] In a fourth aspect, an embodiment of the present disclosure provides a device, including:

[0156] a transceiver module, configured to send first information to a first terminal, where the first information includes a first data set, the auxiliary information corresponding to the first data set, and / or a first identifier corresponding to the first data set;

[0157] The first terminal is used to determine the data set corresponding to the first identifier based on the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

[0158] In a fifth aspect, an embodiment of the present disclosure provides a terminal, including:

[0159] one or more processors;

[0160] The terminal is used to execute the method described in the first aspect.

[0161] In a sixth aspect, an embodiment of the present disclosure provides a device, including:

[0162] one or more processors;

[0163] The device is used to execute the method described in the second aspect.

[0164] In a seventh aspect, an embodiment of the present disclosure provides a communication system, including a first terminal and a first device, wherein:

[0165] The first terminal is configured to execute the method according to the first aspect;

[0166] The first device is configured to perform the method described in the second aspect.

[0167] In an eighth aspect, an embodiment of the present disclosure provides a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the first aspect or the second aspect.

[0168] In a ninth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

[0169] In a tenth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first and second aspects.

[0170] In an eleventh aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first and second aspects above.

[0171] It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0172] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0173] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0174] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0175] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0176] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0177] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0178] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0179] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0180] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0181] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0182] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0183] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0184] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0185] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0186] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)", etc.

[0187] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0188] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0189] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0190] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0191] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0192] As shown in FIG1 , a communication system 100 includes a first terminal 101 and a first device 102 .

[0193] In some embodiments, the first terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0194] In some embodiments, the first device 102 may be a network device, a second terminal, or a server. The second terminal refers to a terminal other than the first terminal 101, and the server may be a server of a terminal manufacturer or other server.

[0195] In some embodiments, when the first device 102 is a network device, the network device may include at least one of an access network device and a core network device.

[0196] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a wireless fidelity (WiFi) system, but is not limited thereto.

[0197] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0198] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0199] In some embodiments, the core network device can be a device including one or more network elements, or it can be multiple devices or device groups, each including all or part of one or more network elements. The network element can be virtual or physical. The core network includes, for example, at least one of the Evolved Packet Core (EPC), the 5G Core Network (5GCN), and the Next Generation Core (NGC). Alternatively, the core network device refers to a network element with a specific function, such as the Access Management Function (AMF), the Service Management Function (SMF), etc.

[0200] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution provided by the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution provided by the embodiment of the present disclosure is also applicable to similar technical problems.

[0201] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG. 1 , or a part of the main body thereof, but are not limited thereto.

[0202] The entities shown in Figure 1 are examples. The communication system may include all or part of the entities in Figure 1, and may also include other entities outside of Figure 1. The number and form of the entities are arbitrary. The connection relationship between the entities is an example. The entities may be connected or disconnected, and the connection may be in any manner, which may be direct or indirect, and may be wired or wireless.

[0203] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication processing methods, and next-generation systems based on and extending these. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0204] In the disclosed embodiments, AI-based CSI processing is used as an example. AI-based CSI compression can distinguish data from different configurations, scenarios, or locations. AI-based CSI prediction also requires the collection of diverse data during model training, inference, and monitoring. The methods or dimensions for data classification in AI-based CSI prediction need to be addressed.

[0205] Figure 2a is an interactive diagram of a communication processing method according to an embodiment of the present disclosure. As shown in Figure 2a, the present disclosure embodiment relates to a communication processing method, the method comprising:

[0206] Step S2101: The first device 102 sends configuration information to the first terminal 101.

[0207] In some embodiments, the configuration information is used to configure assistance information. In this step, the first device 102 may be a network device.

[0208] Optionally, the configuration information is used to configure at least one of the following: content or parameter items included in the auxiliary information, and the value of at least one content or at least one parameter in the auxiliary information.

[0209] In some embodiments, different auxiliary information is associated or corresponds to different datasets, that is, each dataset has corresponding or associated auxiliary information. Each dataset has a corresponding first identifier, which can be a dataset ID.

[0210] Optionally, the auxiliary information is used to indicate feature information of the corresponding data set.

[0211] Optionally, the dataset is used for AI or ML-based predictions.

[0212] In some embodiments, the dataset includes at least one of the following:

[0213] Channel State Information (CSI) dataset;

[0214] Beam identification dataset;

[0215] Beam-wise Reference Signal Receiving Power (RSRP) dataset;

[0216] Positioning information dataset.

[0217] Among them, the CSI data set is used for CSI prediction based on AI or ML, the beam identification data set is used for beam identification prediction based on AI or ML, the RSRP data set is used for RSRP prediction of beams based on AI or ML, and the positioning information data set is used for positioning information prediction based on AI or ML.

[0218] It is worth noting that the relevant implementation methods in the embodiments of the present disclosure are applicable to the prediction of any of the above information. The following embodiments will be described illustratively using CSI prediction as an example.

[0219] In some embodiments, when the dataset is a CSI dataset, the dataset is used for at least one of a model (hereinafter referred to as the model) training phase, a model inference phase, and a model supervision phase of CSI prediction based on AI or ML.

[0220] Optionally, the AI-based CSI prediction process may include a model training process, a model inference process, and a model supervision process, wherein the AI ​​or ML-based model may be configured on the first terminal 101 side, and the first terminal 101 may obtain a prediction result by measuring a downlink reference signal such as CSI-RS, and inputting the measurement result CSI into the model after preprocessing.

[0221] In one example, as shown in Figure 2c, different CSIs can be defined using observation windows and prediction windows. Within the observation window, the network device can transmit a CSI reference signal (CSI-RS). The CSI obtained by first terminal 101 based on the measured CSI-RS is the measured CSI. Within the prediction window, the CSI obtained by first terminal 101 based on the measured CSI prediction is the predicted CSI. It will be appreciated that measured CSI can also be referred to as historical CSI, and predicted CSI can also be referred to as future CSI. The predicted CSI can be obtained based on one or more measured CSI predictions.

[0222] Optionally, during the model training process, the first terminal 101 obtains multiple data sets for training (called sample sets). After obtaining the sample sets, it distinguishes samples for the observation window and samples for the prediction window from the sample sets, and uses the two samples to train the model until the model converges.

[0223] Optionally, during the model inference process, the first terminal 101 obtains measured CSI and inputs the measured CSI into the model to output predicted CSI. Alternatively, the first terminal 101 obtains measured CSI by measuring the CSI-RS and inputs the measured CSI into the model to obtain a prediction result.

[0224] Optionally, during the model supervision process, the first terminal 101 determines the actual predicted CSI, verifies the accuracy of the predicted CSI, and determines whether it is suitable for the current channel.

[0225] Optionally, the measured CSI input to the model may be the CSI in the data set or the CSI obtained by the first terminal 101 measuring a downlink reference signal such as a CSI-RS, or the CSI obtained after preprocessing the above CSI.

[0226] In some embodiments, the three CSI prediction processes mentioned above all require the collection of data sets.

[0227] In an example, the data set includes one or more data. When the data set is a CSI data set, the data is CSI-RS or CSI.

[0228] In this example, when the data is CSI-RS, the CSI data set may be CSI obtained by the first terminal 101 through measurement.

[0229] In this example, when the data is CSI, the CSI data set may be CSI obtained by the first terminal 101 without measurement.

[0230] In this example, CSI is at least one of the following:

[0231] Channel matrix information;

[0232] Channel feature vector information.

[0233] In some embodiments, the auxiliary information includes at least one of the following:

[0234] The time interval between adjacent CSIs;

[0235] The number of CSIs;

[0236] Frequency domain characteristics;

[0237] Movement speed;

[0238] scene.

[0239] Optionally, the time interval between adjacent CSIs includes at least one of the following:

[0240] The time interval between adjacent CSI measurements in the observation window;

[0241] The time interval between adjacent predicted CSIs in the prediction window;

[0242] The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window;

[0243] The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

[0244] Optionally, the number of CSIs includes at least one of the following:

[0245] The number of measured CSIs in the observation window;

[0246] The number of predicted CSIs in the prediction window.

[0247] In one example, as shown in FIG2c , the observation window includes M measured CSIs, and the prediction window includes N predicted CSIs, where M and N are positive integers.

[0248] In this example, as shown in Figure 2c, the time interval between adjacent measured CSI in the observation window is X, the time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window is Y, and the time interval between adjacent predicted CSI in the prediction window is Z. The units of X, Y, and Z are time domain units. These time domain units can be slots, half-slots, subslots, or symbols. Taking slots as an example, the time interval can be the number of slots within a certain subcarrier spacing (SCS).

[0249] In one example, the network device may configure a value of at least one of X, Y, and Z.

[0250] In another example, the first terminal 101 may determine X according to the period of the CSI-RS, and obtain the predicted CSI according to X.

[0251] Optionally, the frequency domain characteristics are frequency domain characteristics of a reference signal such as a CSI-RS, or frequency domain characteristics corresponding to the channel state information CSI. Frequency domain characteristics include but are not limited to carrier frequency or bandwidth. For example, CSI is the channel state information of each RB in 13 resource blocks (RBs) with a center carrier frequency of 2 GHz, and the bandwidth corresponding to each RB is 180 kHz. For example, the frequency domain position of the CSI-RS configured by the base station is bandwidth part 1 (Bandwidth Part, BWP#1) of cell 1 (cell#1). The frequency domain characteristics of the CSI measured by the first terminal 101 based on the CSI-RS include: the carrier frequency corresponding to cell#1, the bandwidth corresponding to BWP#1, and the start and end positions of the frequency domain.

[0252] Optionally, the moving speed may refer to the moving speed of the first terminal 101, or the moving speed may include the moving speeds of multiple different terminals (including the first terminal 101). It is understandable that a model trained to convergence based on a data set of a certain moving speed can enable the model to have a better processing effect on the data set of the moving speed.

[0253] Optionally, the scene may include at least one of the following: an urban scene, a rural scene, an indoor scene, and an outdoor scene. The channel information complexity of the urban scene is greater than that of the rural scene; and the channel information complexity of the indoor scene is greater than that of the outdoor scene.

[0254] In some embodiments, taking configuring the auxiliary information through configuration information as an example, the configuration information may include multiple items of information, each item corresponding to a type of auxiliary information, and each item indicating a specific value of the corresponding auxiliary information.

[0255] In one example, the configuration information includes two items of information. The first item of information corresponds to the time interval between adjacent CSIs, and the first item of information may indicate that the time interval between adjacent CSIs is 5 milliseconds; the second item of information corresponds to the number of CSIs in the observation window, and the second item of information may indicate that the number of CSIs in the observation window is 5.

[0256] In some embodiments, during the model training phase, the auxiliary information may further include the number of CSIs in a group of CSIs.

[0257] Optionally, the set of CSIs may be CSIs in one simulation (one drop).

[0258] Optionally, the group of CSIs may be CSIs from a terminal at different times. During model training, the first M CSIs in the group may be used as measured CSIs, and the last N CSIs may be used as predicted CSIs for model training.

[0259] Optionally, the set of CSI corresponds to a deployment. The deployment object includes at least one of the following: a terminal, a base station device, and a neutral node (Neutral site). The neutral node can be a third-party node other than the terminal, the base station device, and the server.

[0260] In some other embodiments, in addition to the network device configuration auxiliary information, the auxiliary information may also be predefined. For example, when the first terminal 101 obtains a data set for model training through a server, the auxiliary information of the data set may be predefined.

[0261] In some embodiments, the first terminal 101 receives configuration information.

[0262] Step S2102: The first terminal 101 sends second information to the first device 102.

[0263] In some embodiments, the second information is used to indicate a first identifier corresponding to the second data set requested by the first terminal 101 .

[0264] In some embodiments, the first device 102 in this step may be one of the following:

[0265] Network equipment;

[0266] Second terminal;

[0267] server.

[0268] In some embodiments, the second information may be request information, such as dataset request information.

[0269] Optionally, during the model training phase, the first terminal 101 obtains a data set for model training from the first device 102 . The first terminal 101 may request the relevant data set from the first device 102 by sending a second message carrying the first identifier.

[0270] In some embodiments, during the model training phase, the first terminal 101 sends the second information.

[0271] In some embodiments, the first device 102 receives the second information.

[0272] Step S2103: The first device 102 sends first information to the first terminal 101.

[0273] In some embodiments, the first device 102 may directly send the first information to the first terminal 101, where the first information includes the first data set, auxiliary information corresponding to the first data set, and / or the first identifier corresponding to the first data set. In this case, the method may not include step S2102.

[0274] In some embodiments, the first device 102 may send first information to the first terminal 101 according to a request of the first terminal 101 , where the first information includes the second data set and / or auxiliary information corresponding to the second data set.

[0275] In some embodiments, the first terminal 101 receives the first information.

[0276] In some embodiments, during the model training phase, the first device 102 sends first information to the first terminal 101.

[0277] Step S2104: The first terminal 101 determines a data set corresponding to the first identifier according to the first identifier.

[0278] In some embodiments, the first terminal 101 may obtain a data set from the first device 102 and may learn the classification or auxiliary information of the data set based on the first identifier.

[0279] In step S2105 , the first terminal 101 performs model training based on the data set.

[0280] In some embodiments, the first terminal 101 trains the model based on a data set with known characteristics, so that after a converged model is obtained, the capabilities or applicability of the model can be defined.

[0281] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", and "field" can be used interchangeably.

[0282] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0283] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0284] In some embodiments, terms such as "uplink", "uplink", and "physical uplink" can be interchangeable, and terms such as "downlink", "downlink", and "physical downlink" can be interchangeable, and terms such as "side", "sidelink", "side communication", and "sidelink communication" can be interchangeable.

[0285] In some embodiments, the terms "downlink control information (DCI)", "downlink (DL) assignment", "DL DCI", "uplink (UL) grant", "UL DCI" and the like may be used interchangeably.

[0286] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, and terms such as "physical uplink shared channel (PUSCH)" and "UL data" can be used interchangeably.

[0287] In some embodiments, the terms "component carrier (CC)", "cell", "frequency carrier", "carrier frequency" and the like can be used interchangeably.

[0288] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0289] In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values ​​(for example, comparison with a predetermined value), but is not limited thereto.

[0290] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the recipient to respond to the content sent.

[0291] The communication processing method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2105. For example, step S2104 may be implemented as an independent embodiment, steps S2103 and S2104 may be implemented as independent embodiments, steps S2102 to S2104 may be implemented as independent embodiments, steps S2101 and S2104 may be implemented as independent embodiments, and steps S2104 and S2105 may be implemented as independent embodiments, but are not limited thereto.

[0292] In some embodiments, steps S2101 , S2102 , S2103 , and S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0293] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2 a .

[0294] FIG2b is an interactive diagram illustrating a communication processing method according to an embodiment of the present disclosure. As shown in FIG2b , the present disclosure embodiment relates to a communication processing method, the method comprising:

[0295] Step S2201: The first device 102 sends configuration information to the first terminal 101.

[0296] In some embodiments, the implementation of step S2201 can refer to the relevant implementation of step S2101 in Figure 2a, and will not be repeated here.

[0297] Step S2202 : The first terminal 101 sends capability information to the first device 102 .

[0298] In some embodiments, the capability information includes auxiliary information corresponding to a third data set to which the model is applicable or a second identifier of the corresponding auxiliary information.

[0299] Optionally, the capability information may include part or all of the auxiliary information.

[0300] In some embodiments, during the model inference phase and / or the model supervision phase, the first terminal 101 sends capability information, such as a UE capability report.

[0301] In some embodiments, the second identifier includes at least one of the following:

[0302] a first identifier (dataset ID) corresponding to the third dataset;

[0303] Model ID

[0304] The functional ID of the model;

[0305] The model identifier and / or function identifier corresponds to the first identifier.

[0306] Step S2203: The first device 102 sends third information to the first terminal 101.

[0307] In some embodiments, the third information includes a first identifier corresponding to the third data set, and the third information is used to request auxiliary information corresponding to the third data set.

[0308] In some embodiments, the first device 102 may send the third information when the auxiliary information carried in the capability information lacks required information.

[0309] In some embodiments, the first terminal 101 receives third information.

[0310] Step S2204: The first terminal 101 sends auxiliary information corresponding to the third data set to the first device 102.

[0311] In some embodiments, the first terminal 101 may further send the information or content requested by the first device 102 in step S2203 by sending auxiliary information.

[0312] In step S2205 , the first terminal 101 determines a data set corresponding to the first identifier according to the first identifier.

[0313] In some embodiments, the first terminal 101 may obtain the classification of the data set according to the first identifier.

[0314] In some embodiments, the first terminal 101 may determine the data set corresponding to the auxiliary information based on the corresponding first identifier. Alternatively, the first terminal 101 obtains the data set of predicted CSI based on measured CSI.

[0315] In one example, the first identifier of data set A is A, and the auxiliary information of data set A includes the number of CSIs in the prediction window and the moving speed of the first terminal 101. The first terminal 101 collects data in which the number of CSIs in the prediction window is a set number and the moving speed of the first terminal 101 is within a first range as data set A.

[0316] In some embodiments, the first terminal 101 may further send a third data set to the first device 102. For example, during the model inference or supervision phase, the first terminal 101 sends the third data set and / or its auxiliary information to the first device 102.

[0317] Optionally, the third data set can be a data set used for model inference or supervision stage. The network device can obtain relevant capabilities of the model on the first terminal 101 side based on the auxiliary information corresponding to the third data set, so that the model on the first terminal 101 side can be reasonably used for CSI prediction.

[0318] The communication processing method involved in the embodiments of the present disclosure may include at least one of steps S2201 to S2205. For example, step S2205 may be implemented as an independent embodiment, steps S2202 and S2205 may be implemented as independent embodiments, and steps S2203 to S2205 may be implemented as independent embodiments, but are not limited thereto.

[0319] In some embodiments, steps S2201 , S2202 , S2203 , and S2204 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0320] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2 b .

[0321] FIG3a is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG3a, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first terminal 101. The above method includes:

[0322] Step S3101: The first terminal 101 obtains configuration information.

[0323] In some embodiments, the implementation of step S3101 can refer to the relevant implementation of step S2101 in Figure 2a, which will not be repeated here.

[0324] In some embodiments, the first terminal 101 may obtain configuration information from a network device or other entity.

[0325] Step S3102: The first terminal 101 sends second information.

[0326] In some embodiments, the implementation of step S3102 can refer to the relevant implementation of step S2102 in Figure 2a, which will not be repeated here.

[0327] In some embodiments, the first terminal 101 may send the second information to a network device or other entity.

[0328] Step S3103: The first terminal 101 obtains first information.

[0329] In some embodiments, the implementation of step S3103 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0330] In some embodiments, the first terminal 101 may obtain the first information from a network device or other entity.

[0331] Step S3104: The first terminal 101 sends capability information.

[0332] In some embodiments, the implementation of step S3104 can refer to the relevant implementation of step S2202 in Figure 2b, which will not be repeated here.

[0333] In some embodiments, the first terminal 101 may send capability information to a network device or other entity.

[0334] Step S3105: The first terminal 101 obtains third information.

[0335] In some embodiments, the implementation of step S3105 can refer to the relevant implementation of step S2203 in Figure 2b, which will not be repeated here.

[0336] In some embodiments, the first terminal 101 may obtain the third information from a network device or other entity.

[0337] Step S3106: The first terminal 101 sends auxiliary information corresponding to the third data set.

[0338] In some embodiments, the implementation of step S3106 can refer to the relevant implementation of step S2204 in Figure 2b, which will not be repeated here.

[0339] In some embodiments, the first terminal 101 may send auxiliary information to a network device or other entity.

[0340] Step S3107: The first terminal 101 determines the data set corresponding to the first identifier according to the first identifier.

[0341] In some embodiments, the implementation of step S3107 can refer to the relevant implementation of step S2104 in Figure 2a and the relevant implementation of step S2205 in Figure 2b, which will not be repeated here.

[0342] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3 a .

[0343] FIG3b is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG3b, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first terminal 101. The method includes:

[0344] Step S3201: The first terminal 101 determines a data set corresponding to the first identifier according to the first identifier.

[0345] In some embodiments, the implementation of step S3201 can refer to the relevant implementation of step S2104 in Figure 2a and the relevant implementation of step S2205 in Figure 2b, which will not be repeated here.

[0346] In some embodiments, the auxiliary information may be configured by a network device, such as the first terminal receiving configuration information sent by the network device, where the configuration information is used to configure the auxiliary information.

[0347] In some embodiments, the auxiliary information is predefined.

[0348] In some embodiments, the dataset includes at least one of the following:

[0349] Channel state information CSI data set;

[0350] Beam identification dataset;

[0351] Beam reference signal received power RSRP dataset;

[0352] Positioning information dataset.

[0353] Optionally, the dataset is a CSI dataset, and the dataset is used for at least one of a model training stage, a model inference stage, and a model supervision stage of CSI prediction based on AI or ML.

[0354] In some embodiments, the auxiliary information includes at least one of the following:

[0355] The time interval between adjacent CSIs;

[0356] The number of CSIs;

[0357] Frequency domain characteristics;

[0358] Movement speed;

[0359] scene.

[0360] Optionally, the time interval between adjacent CSIs includes at least one of the following:

[0361] The time interval between adjacent CSI measurements in the observation window;

[0362] The time interval between adjacent predicted CSIs in the prediction window;

[0363] The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window;

[0364] The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

[0365] Optionally, the number of CSIs includes at least one of the following:

[0366] The number of measured CSIs in the observation window;

[0367] The number of predicted CSIs in the prediction window.

[0368] In some embodiments, the data set includes one or more data. When the data set is a CSI data set, the data is a channel state information reference signal CSI-RS or CSI.

[0369] Optionally, the CSI is at least one of the following:

[0370] Channel matrix information;

[0371] Channel feature vector information.

[0372] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3 b .

[0373] FIG3c is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG3c, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first terminal 101. The method includes:

[0374] Step S3301: The first terminal 101 receives the first information sent by the first device 102.

[0375] In some embodiments, the implementation of step S3301 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0376] In some embodiments, the first information includes a first data set, and auxiliary information corresponding to the first data set and / or a first identifier corresponding to the first data set.

[0377] In some embodiments, the first device is one of the following:

[0378] Network equipment;

[0379] Second terminal;

[0380] server.

[0381] Step S3302: The first terminal 101 determines a data set corresponding to the first identifier according to the first identifier.

[0382] In some embodiments, the implementation of step S3202 can refer to the relevant implementation of step S2104 in Figure 2a and the relevant implementation of step S2205 in Figure 2b, which will not be repeated here.

[0383] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3c.

[0384] FIG3d is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG3d, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first terminal 101. The above method includes:

[0385] Step S3401: The first terminal 101 sends second information to the first device 102.

[0386] In some embodiments, the implementation of step S3401 can refer to the relevant implementation of step S2102 in Figure 2a, and will not be repeated here.

[0387] In step S3402 , the first terminal 101 receives a second data set and / or auxiliary information corresponding to the second data set sent by the first device 102 .

[0388] In some embodiments, the implementation of step S3402 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0389] Step S3403: The first terminal 101 determines the data set corresponding to the first identifier according to the first identifier.

[0390] In some embodiments, the implementation of step S3403 can refer to the relevant implementation of step S2104 in Figure 2a and the relevant implementation of step S2205 in Figure 2b, which will not be repeated here.

[0391] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3 d .

[0392] FIG3e is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG3e, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first terminal 101. The method includes:

[0393] Step S3501: The first terminal 101 sends capability information to the first device 102.

[0394] In some embodiments, the implementation of step S3501 can refer to the relevant implementation of step S2202 in Figure 2b, which will not be repeated here.

[0395] In some embodiments, the second identifier includes at least one of the following:

[0396] a first identifier corresponding to the third data set;

[0397] The identification of the model;

[0398] Functional identification of the model;

[0399] The model identifier and / or function identifier corresponds to the first identifier.

[0400] Step S3502: The first terminal 101 receives the third information sent by the first device 102.

[0401] In some embodiments, the implementation of step S3502 can refer to the relevant implementation of step S2203 in Figure 2b, which will not be repeated here.

[0402] Step S3503 : The first terminal 101 sends auxiliary information corresponding to the third data set to the first device 102 .

[0403] In some embodiments, the implementation of step S3503 can refer to the relevant implementation of step S2204 in Figure 2b, which will not be repeated here.

[0404] Step S3504: The first terminal 101 determines the data set corresponding to the first identifier according to the first identifier.

[0405] In some embodiments, the implementation of step S3504 can refer to the relevant implementation of step S2205 in Figure 2b, which will not be repeated here.

[0406] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3e.

[0407] FIG4a is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG4a, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first device 102. The method includes:

[0408] Step S4101: The first device 102 sends configuration information.

[0409] In some embodiments, the implementation of step S4101 can refer to the relevant implementation of step S2101 in Figure 2a, which will not be repeated here.

[0410] Step S4102: The first device 102 obtains second information.

[0411] In some embodiments, the implementation of step S4102 can refer to the relevant implementation of step S2102 in Figure 2a, which will not be repeated here.

[0412] Step S4103: The first device 102 sends the first information.

[0413] In some embodiments, the implementation of step S4103 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0414] Step S4104: The first device 102 obtains capability information.

[0415] In some embodiments, the implementation of step S4104 can refer to the relevant implementation of step S2202 in Figure 2b, which will not be repeated here.

[0416] Step S4105: The first device 102 sends third information.

[0417] In some embodiments, the implementation of step S4105 can refer to the relevant implementation of step S2203 in Figure 2b, which will not be repeated here.

[0418] Step S4106: The first device 102 obtains auxiliary information corresponding to the third data set.

[0419] In some embodiments, the implementation of step S4106 can refer to the relevant implementation of step S2204 in Figure 2b, which will not be repeated here.

[0420] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4 a .

[0421] FIG4b is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG4b, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first device 102. The method includes:

[0422] Step S4201: The first device 102 sends first information to the first terminal 101.

[0423] In some embodiments, the implementation of step S4201 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0424] In some embodiments, the first terminal is used to determine a data set corresponding to the first identifier based on the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information of the corresponding data set, and the data set is used for prediction based on AI or ML.

[0425] In some embodiments, the first device is one of the following:

[0426] Network equipment;

[0427] Second terminal;

[0428] server.

[0429] In some embodiments, the first device may send configuration information to the first terminal, where the configuration information is used to configure the auxiliary information, and the first device is a network device. Alternatively, the auxiliary information is predefined.

[0430] In some embodiments, the dataset includes at least one of the following:

[0431] CSI dataset;

[0432] Beam identification dataset;

[0433] Beam RSRP dataset;

[0434] Positioning information dataset.

[0435] Optionally, the dataset is a CSI dataset, and the dataset is used for at least one of a model training stage, a model inference stage, and a model supervision stage of CSI prediction based on AI or ML.

[0436] In some embodiments, the auxiliary information includes at least one of the following:

[0437] The time interval between adjacent CSIs;

[0438] The number of CSIs;

[0439] Frequency domain characteristics;

[0440] Movement speed;

[0441] scene.

[0442] Optionally, the time interval between adjacent CSIs includes at least one of the following:

[0443] The time interval between adjacent CSI measurements in the observation window;

[0444] The time interval between adjacent predicted CSIs in the prediction window;

[0445] The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window;

[0446] The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

[0447] Optionally, the number of CSIs includes at least one of the following:

[0448] The number of measured CSIs in the observation window;

[0449] The number of predicted CSIs in the prediction window.

[0450] In some embodiments, the data set includes one or more data. When the data set is a CSI data set, the data is a channel state information reference signal CSI-RS or CSI.

[0451] Optionally, the CSI is at least one of the following:

[0452] Channel matrix information;

[0453] Channel feature vector information.

[0454] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4 b .

[0455] FIG4c is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG4c, the embodiment of the present disclosure relates to a communication processing method, which is executed by the first device 102. The method includes:

[0456] Step S4301: The first device 102 receives second information sent by the first terminal 101.

[0457] In some embodiments, the implementation of step S4301 can refer to the relevant implementation of step S2102 in Figure 2a, and will not be repeated here.

[0458] Step S4302: The first device 102 sends first information to the first terminal 101.

[0459] In some embodiments, the implementation of step S4302 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0460] The first information includes the second data set and / or auxiliary information corresponding to the second data set.

[0461] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4c.

[0462] FIG4d is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG4d , the embodiment of the present disclosure relates to a communication processing method, which is executed by the first device 102. The method includes:

[0463] Step S4401: The first device 102 sends first information to the first terminal 101.

[0464] In some embodiments, the implementation of step S4401 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0465] Step S4402 : The first device 102 receives capability information sent by the first terminal 101 .

[0466] In some embodiments, the implementation of step S4401 can refer to the relevant implementation of step S2202 in Figure 2b, which will not be repeated here.

[0467] In some embodiments, the second identifier includes at least one of the following:

[0468] a first identifier corresponding to the third data set;

[0469] The identification of the model;

[0470] Functional identification of the model;

[0471] The model identifier and / or function identifier corresponds to the first identifier.

[0472] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4 d .

[0473] FIG4e is a schematic diagram of a communication processing method according to an embodiment of the present disclosure. As shown in FIG4e , the embodiment of the present disclosure relates to a communication processing method, which is executed by the first device 102. The method includes:

[0474] Step S4501: The first device 102 sends first information to the first terminal 101.

[0475] In some embodiments, the implementation of step S4501 can refer to the relevant implementation of step S2103 in Figure 2a, which will not be repeated here.

[0476] Step S4502: The first device 102 sends third information to the first terminal 101.

[0477] In some embodiments, the implementation of step S4502 can refer to the relevant implementation of step S2203 in Figure 2b, which will not be repeated here.

[0478] Step S4503 : The first device 102 receives auxiliary information corresponding to the third data set sent by the first terminal 101 .

[0479] In some embodiments, the implementation of step S4503 can refer to the relevant implementation of step S2204 in Figure 2b, which will not be repeated here.

[0480] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4e.

[0481] To facilitate understanding of the embodiments of the present disclosure, some specific examples are listed below:

[0482] Example 1:

[0483] Datasets used for AI-based CSI prediction, with different datasets distinguished by ID.

[0484] Optionally, each dataset is defined by auxiliary information.

[0485] Optionally, the definition of the auxiliary information of the data set may be network configured or predefined.

[0486] Optionally, the auxiliary information includes at least one of the following:

[0487] The time interval between samples, for example, the time interval is the number of slots under a certain SCS;

[0488] The number of samples in a group. A group of samples can be all samples from a UE in a drop;

[0489] The forecast period;

[0490] The distance between the starting prediction point and the last observation point;

[0491] The number of predictions;

[0492] Frequency characteristics, such as carrier frequency and bandwidth;

[0493] User speed;

[0494] scene.

[0495] Example 2:

[0496] During the model training phase, if the UE obtains the dataset for model training through other entities, data set interaction will be involved. The other entities can be servers, gNBs, other UEs, etc.

[0497] Method 1: When an entity transmits data to a UE, it transmits auxiliary information corresponding to the data set, such as a data set identifier, at the same time as the data set.

[0498] Method 2: The UE sends a dataset request to the entity, and the request includes a dataset identifier.

[0499] Optionally, if the entity is a gNB or UE, the auxiliary information of the data set is configured by the network device, such as through RRC signaling.

[0500] Optionally, if the entity is a server, auxiliary information of the dataset is predefined.

[0501] Example 3:

[0502] During the model inference phase or the supervision phase, auxiliary information of at least a portion of the data set used for the inference phase or the supervision phase may be reported through a capability report.

[0503] Optionally, in the capability report, the UE may directly report the dataset ID. Alternatively, the UE may also report the model ID and / or functionality ID, where the model ID and / or functionality ID have a corresponding relationship with the dataset ID.

[0504] Optionally, for the content not included in the dataset ID, the base station may request the terminal to report it.

[0505] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0506] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0507] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0508] Figure 5a is a schematic diagram of the structure of a terminal 101 according to an embodiment of the present disclosure. As shown in Figure 5a, terminal 5100 may include at least one of a transceiver module 5101 and a processing module 5102. In some embodiments, transceiver module 5101 is configured to determine, based on a first identifier, a dataset corresponding to the first identifier, and to associate corresponding auxiliary information with different datasets; the auxiliary information indicates feature information corresponding to the dataset, and the dataset is used for prediction based on artificial intelligence (AI) or machine learning (ML).

[0509] Optionally, the transceiver module 5101 is configured to execute at least one of the communication steps of sending and / or receiving executed by the terminal 101 in any of the above methods. Optionally, the processing module 5102 is configured to execute at least one of the other steps executed by the terminal 101 in any of the above methods.

[0510] Figure 5b is a schematic diagram of the structure of the first device 102 proposed in an embodiment of the present disclosure. As shown in Figure 5b, the first device 5200 may include at least one of a transceiver module 5201 and a processing module 5202. In some embodiments, the transceiver module 5201 is configured to send first information to a first terminal, where the first information includes a first data set, the auxiliary information corresponding to the first data set, and / or a first identifier corresponding to the first data set; wherein the first terminal is configured to determine the data set corresponding to the first identifier based on the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is configured to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

[0511] Optionally, the transceiver module 5201 is configured to execute at least one of the communication steps of sending and / or receiving performed by the first device 102 in any of the above methods. Optionally, the processing module 5202 is configured to execute at least one of the other steps performed by the first device 102 in any of the above methods.

[0512] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0513] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0514] Figure 6a is a schematic diagram of the structure of a communication device 6100 proposed in an embodiment of the present disclosure. Communication device 6100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device implementing any of the above methods, or a chip, a chip system, or a processor that supports a terminal implementing any of the above methods. Communication device 6100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0515] As shown in Figure 6a, the communication device 6100 includes one or more processors 6101. Processor 6101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, baseband chip, terminal device, terminal device chip, DU or CU, etc.), execute programs, and process program data. The communication device 6100 is used to perform any of the above methods.

[0516] In some embodiments, the communication device 6100 further includes one or more memories 6102 for storing instructions. Optionally, all or part of the memories 6102 may be located outside the communication device 6100.

[0517] In some embodiments, the communication device 6100 further includes one or more transceivers 6103. When the communication device 6100 includes one or more transceivers 6103, the transceiver 6103 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101 and step S2102, but not limited thereto), and the processor 6101 performs at least one of the other steps (for example, step S2103, step S2104, and step S2105, but not limited thereto).

[0518] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0519] In some embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6102. The interface circuit 6104 may be configured to receive signals from the memory 6102 or other devices, and may be configured to send signals to the memory 6102 or other devices. For example, the interface circuit 6104 may read instructions stored in the memory 6102 and send the instructions to the processor 6101.

[0520] The communication device 6100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited by FIG. 6a. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0521] FIG6b is a schematic diagram of the structure of a chip 6200 according to an embodiment of the present disclosure. If the communication device 6100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 6200 shown in FIG6b , but the present disclosure is not limited thereto.

[0522] The chip 6200 includes one or more processors 6201 , and the chip 6200 is configured to execute any of the above methods.

[0523] In some embodiments, the chip 6200 further includes one or more interface circuits 6202. Optionally, the interface circuit 6202 is connected to the memory 6203. The interface circuit 6202 can be used to receive signals from the memory 6203 or other devices, and can be used to send signals to the memory 6203 or other devices. For example, the interface circuit 6202 can read instructions stored in the memory 6203 and send the instructions to the processor 6201.

[0524] In some embodiments, the interface circuit 6202 executes at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, step S2102, but not limited to these), and the processor 6201 executes at least one of the other steps (for example, step S2103, step S2104, S2105, but not limited to these).

[0525] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0526] In some embodiments, the chip 6200 further includes one or more memories 6203 for storing instructions. Alternatively, all or part of the memories 6203 may be located outside the chip 6200.

[0527] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 6100, the communication device 6100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0528] The present disclosure also provides a program product, which, when executed by the communication device 6100, enables the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0529] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods. Industrial Applicability

[0530] In the method disclosed herein, the data set in AI or ML-based prediction can be classified by the first identifier, and the characteristic information of the data set can be referred to, so that the characteristics or uses of the data set can be combined in the classification, which is conducive to improving the rationality and efficiency of data set collection and application.

Claims

1. A communication processing method, comprising: The first terminal determines a data set corresponding to the first identifier according to the first identifier, and different data sets are associated with corresponding auxiliary information; The auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on artificial intelligence AI or machine learning ML.

2. The method of claim 1, wherein: The method further comprises: The first terminal receives first information sent by a first device, where the first information includes a first data set, and the auxiliary information corresponding to the first data set and / or a first identifier corresponding to the first data set.

3. The method of claim 1, wherein: The method further comprises: The first terminal sends second information to the first device, where the second information is used to indicate a first identifier corresponding to the second data set requested by the first terminal; The first terminal receives the second data set and / or the auxiliary information corresponding to the second data set sent by the first device.

4. The method of claim 1, wherein: The method further comprises: The first terminal sends capability information to the first device, where the capability information includes auxiliary information corresponding to a third data set to which the model is applicable or a second identifier of the corresponding auxiliary information.

5. The method of claim 4, wherein: The second identifier includes at least one of the following: a first identifier corresponding to the third data set; an identification of the model; functional identification of the model; The model identifier and / or the function identifier corresponds to the first identifier.

6. The method of claim 4, wherein: The method further comprises: The first terminal receives third information sent by the first device, where the third information includes a first identifier corresponding to the third data set, and the third information is used to request auxiliary information corresponding to the third data set; The first terminal sends auxiliary information corresponding to the third data set to the first device.

7. The method according to any one of claims 2 to 6, wherein: The first device is one of the following: Network equipment; Second terminal; server.

8. The method according to any one of claims 1 to 6, wherein: The method further comprises: The first terminal receives configuration information sent by a first device, where the configuration information is used to configure the auxiliary information, wherein the first device is a network device.

9. The method according to any one of claims 1 to 7, wherein: The auxiliary information is predefined.

10. The method according to any one of claims 1 to 9, wherein: The data set includes at least one of the following: Channel state information CSI data set; Beam identification dataset; The reference signal received power (RSRP) data set of the beam; Positioning information dataset.

11. The method of claim 10, wherein: The data set is a CSI data set, and the data set is used for at least one of a model training stage, a model reasoning stage, and a model supervision stage of CSI prediction based on AI or ML.

12. The method according to claim 10 or 11, wherein: The auxiliary information includes at least one of the following: The time interval between adjacent CSIs; The number of CSIs; Frequency domain characteristics; Movement speed; scene.

13. The method of claim 12, wherein: The time interval of adjacent CSIs includes at least one of the following: The time interval between adjacent CSI measurements in the observation window; The time interval between adjacent predicted CSIs in the prediction window; The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window; The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

14. The method of claim 12, wherein: The number of CSIs includes at least one of the following: The number of measured CSIs in the observation window; The number of predicted CSI in the prediction window.

15. The method according to any one of claims 1 to 14, wherein: The data set includes one or more data. When the data set is a CSI data set, the data is a channel state information reference signal CSI-RS or CSI.

16. The method of claim 15, wherein: The CSI is at least one of the following: Channel matrix information; Channel feature vector information.

17. A communication processing method, comprising: The first device sends first information to the first terminal, where the first information includes a first data set, and auxiliary information corresponding to the first data set and / or a first identifier corresponding to the first data set; The first terminal is used to determine a data set corresponding to the first identifier according to the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

18. The method of claim 17, wherein: The method further comprises: The first device receives second information sent by the first terminal, where the second information is used to indicate a first identifier corresponding to a second data set requested by the first terminal; The first device sends the second data set and / or the auxiliary information corresponding to the second data set to the first terminal.

19. The method of claim 17, wherein: The method further comprises: The first device receives capability information sent by the first terminal, where the capability information includes auxiliary information corresponding to a third data set to which the model is applicable or a second identifier of the corresponding auxiliary information.

20. The method of claim 19, wherein: The second identifier includes at least one of the following: a first identifier corresponding to the third data set; an identification of the model; functional identification of the model; The model identifier and / or the function identifier corresponds to the first identifier.

21. The method of claim 19, wherein: The method further comprises: The first device sends third information to the first terminal, where the third information includes a first identifier corresponding to the third data set, and the third information is used to request auxiliary information corresponding to the third data set; The first device receives auxiliary information corresponding to the third data set sent by the first terminal.

22. The method according to any one of claims 17 to 21, wherein: The first device is one of the following: Network equipment; Second terminal; server.

23. The method according to any one of claims 17 to 21, wherein: The method further comprises: The first device sends configuration information to the first terminal, where the configuration information is used to configure the auxiliary information, and the first device is a network device.

24. The method according to any one of claims 17 to 23, wherein: The data set includes at least one of the following: CSI dataset; Beam identification dataset; Beam RSRP dataset; Positioning information dataset.

25. The method of claim 24, wherein: The data set is a CSI data set, and the data set is used for at least one of a model training stage, a model reasoning stage, and a model supervision stage of CSI prediction based on AI or ML.

26. The method according to any one of claims 17 to 23, wherein: The auxiliary information includes at least one of the following: The time interval between adjacent CSIs; The number of CSIs; Frequency domain characteristics; Movement speed; scene.

27. The method of claim 26, wherein: The time interval of adjacent CSIs includes at least one of the following: The time interval between adjacent CSI measurements in the observation window; The time interval between adjacent predicted CSIs in the prediction window; The time interval between the first predicted CSI in the prediction window and the last measured CSI in the observation window; The observation window is used to obtain the measured CSI input to the AI ​​or ML model, and the prediction window is used to obtain the predicted CSI output by the model.

28. The method of claim 26, wherein: The number of CSIs includes at least one of the following: The number of measured CSIs in the observation window; The number of predicted CSI in the prediction window.

29. The method according to any one of claims 17 to 28, wherein: The data set includes one or more data. When the data set is a CSI data set, the data is a channel state information reference signal CSI-RS or CSI.

30. The method of claim 29, wherein: The CSI is at least one of the following: Channel matrix information; Channel feature vector information.

31. A terminal, comprising: A transceiver module, used to determine a data set corresponding to the first identifier according to the first identifier, and to associate corresponding auxiliary information with different data sets; The auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on artificial intelligence AI or machine learning ML.

32. A device comprising: A transceiver module, configured to send first information to a first terminal, where the first information includes a first data set, and auxiliary information corresponding to the first data set and / or a first identifier corresponding to the first data set; The first terminal is used to determine a data set corresponding to the first identifier according to the first identifier, and different data sets are associated with corresponding auxiliary information, wherein the auxiliary information is used to indicate feature information corresponding to the data set, and the data set is used for prediction based on AI or ML.

33. A terminal, comprising: one or more processors; The terminal is used to execute the method according to any one of claims 1 to 16.

34. An apparatus comprising: one or more processors; Wherein, the device is used to execute the method described in any one of claims 17 to 30.

35. A communication system, comprising a first terminal and a first device, wherein: The first terminal is configured to execute the method according to any one of claims 1 to 16; The first device is configured to perform the method according to any one of claims 17 to 30.

36. A storage medium storing instructions, which, when executed on a communication device, causes the communication device to execute the method of any one of claims 1 to 16 or any one of claims 17 to 30.

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