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

Abstract

The present disclosure relates to a communication method, a terminal, a network device, a communication system, and a storage medium. The communication method comprises: a terminal sends a first report to a network device, the first report comprising a report quantity of at least one reference signal resource corresponding to at least one port. According to the embodiments of the present disclosure, the terminal sends the first report to the network device, the first report comprising the report quantity of the at least one reference signal resource corresponding to the at least one port, so that an AI model trained and obtained by the network device on the basis of the first report can separately predict report quantities of reference signal resources for different ports, thereby improving the accuracy of model training.

Description

Communication method, terminal, network device, communication system and storage medium TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a communication method, a terminal, a network device, a communication system and a storage medium. BACKGROUND

[0002] In New Radio (NR), especially in frequency range 2, in order to guarantee coverage, beam-based transmission and reception are needed.

[0003] In the beam management process, the network device configures a reference signal resource set for beam measurement, the terminal measures the reference signal resources in the reference signal resource set, the terminal reports the measurement result to the network device, and the network device can train an Artificial Intelligence (AI) model based on the data reported by the terminal.

[0004] SUMMARY

[0005] For the AI model in the near field communication scenario, how to collect the training data is a problem to be solved.

[0006] Embodiments of the present disclosure provide a communication method, a terminal, a network device, a communication system and a storage medium.

[0007] According to a first aspect of the embodiments of the present disclosure, a communication method is provided, and the method comprises: a terminal sending a first report to a network device, wherein the first report comprises a report quantity of at least one reference signal resource corresponding to at least one port.

[0008] According to a second aspect of the embodiments of the present disclosure, a communication method is provided, and the method comprises: a network device receiving a first report sent by a terminal, wherein the first report comprises a report quantity of at least one reference signal resource corresponding to at least one port.

[0009] According to a third aspect of the embodiments of the present disclosure, a terminal is provided, comprising: a transceiver module, configured to send a first report to a network device, wherein the first report comprises a report quantity of at least one reference signal resource corresponding to at least one port.

[0010] According to a fourth aspect of the embodiments of the present disclosure, a network device is provided, comprising: a transceiver module, configured to receive a first report sent by a terminal, wherein the first report comprises a report quantity of at least one reference signal resource corresponding to at least one port.

[0011] According to a fifth aspect of the embodiments of the present disclosure, a terminal is provided, comprising: one or more processors; wherein the processor is configured to execute the communication method of the first aspect.

[0012] According to a sixth aspect of the embodiments of the present disclosure, a network device is provided, comprising: one or more processors; wherein the processor is configured to execute the communication method of the second aspect.

[0013] According to a seventh aspect of the embodiments of the present disclosure, a communication system is provided, comprising a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and the network device is configured to implement the communication method of the second aspect.

[0014] According to an eighth aspect of the embodiments of the present disclosure, a storage medium is provided, and the storage medium stores instructions, and the instructions, when executed on a communication device, cause the communication device to execute the method of the first aspect or the second aspect.

[0015] According to a ninth aspect of the embodiments of the present disclosure, a computer program is provided, and the computer program, when executed on a communication device, causes the communication device to execute the communication method of the first aspect or the second aspect.

[0016] Through the embodiments of the present disclosure, the terminal sends a first report to the network device, and the first report includes a report quantity of at least one reference signal resource corresponding to at least one port, so that the network device can respectively predict the report quantity of the reference signal resource for different ports based on the AI model obtained by training the first report, thereby improving the accuracy of model training. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following describes the drawings required for the embodiments, and the following drawings are only some embodiments of the present disclosure, and do not specifically limit the protection scope of the present disclosure.

[0018] FIG. 1A is a schematic diagram of a communication scenario of a far-field terminal.

[0019] FIG. 1B is a schematic diagram of a communication scenario of a near-field terminal.

[0020] FIG. 1C is an exemplary schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0021] FIG. 2 is an interaction schematic diagram of a communication method according to an embodiment of the present disclosure.

[0022] FIG. 3A is a flow schematic diagram of a communication method according to an embodiment of the present disclosure.

[0023] FIG. 3B is a flow schematic diagram of a communication method according to an embodiment of the present disclosure.

[0024] FIG. 4A is a flow diagram of a communication method according to an embodiment of the present disclosure.

[0025] FIG. 4B is a flow diagram of a communication method according to an embodiment of the present disclosure.

[0026] FIG. 5 is an interaction diagram of a communication method according to an embodiment of the present disclosure.

[0027] FIG. 6A is a structural diagram of a terminal according to an embodiment of the present disclosure.

[0028] FIG. 6B is a structural diagram of a network device according to an embodiment of the present disclosure.

[0029] FIG. 7A is a structural diagram of a communication device according to an embodiment of the present disclosure.

[0030] FIG. 7B is a structural diagram of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The present disclosure provides a communication method, a terminal, a network device, a communication system, and a storage medium.

[0032] In a first aspect, the present disclosure provides a communication method, which includes: a terminal sending a first report to a network device, the first report including a report quantity of at least one reference signal resource corresponding to at least one port.

[0033] In the above embodiment, the terminal sends the first report to the network device, and the first report includes the report quantity of the at least one reference signal resource corresponding to the at least one port, so that the AI model obtained by the network device based on the first report can predict the report quantity of the reference signal resource for different ports respectively, thereby improving the accuracy of model training.

[0034] In some embodiments of the first aspect, the first report includes a report quantity corresponding to at least one of a first set and a second set; the first set includes M reference signal resources corresponding to each of L ports; the second set includes N reference signal resources corresponding to each of the L ports; where L, M, and N are positive integers, and M is less than or equal to N.

[0035] In the above embodiment, the first set includes M reference signal resources corresponding to each of the L ports, and the second set includes M reference signal resources corresponding to each of the L ports, so that the AI model obtained by the network device based on the first set and the second set can predict the report quantity of the at least one reference signal resource or the best reference signal resource of the at least one port according to the input report quantity of the at least one reference information resource of the at least one port.

[0036] In some embodiments of the first aspect, in some embodiments, the first report comprises a report quantity corresponding to at least one of the first set and the second set; the first set comprises M reference signal resources corresponding to X ports of the L ports; and the second set comprises a second report quantity corresponding to N reference signal resources corresponding to each port of the L ports; wherein L, M and X are positive integers, M is less than or equal to N, and X is less than or equal to L.

[0037] In the above embodiments, the first set comprises M reference signal resources corresponding to part of the L ports, and the second set comprises N reference signal resources corresponding to each port of the L ports, so that the AI model trained by the network device based on the first set and the second set can predict the report quantity of at least one reference signal resource or the best reference signal resource of at least one port according to the report quantity of at least one reference information resource of the part of ports.

[0038] In some embodiments of the first aspect, in some embodiments, the first report comprises a report quantity corresponding to at least one of the first set and the second set; the first set comprises a first report quantity corresponding to at least one best reference signal resource corresponding to each port of X ports of the L ports; and the second set comprises N reference signal resources corresponding to each port of the L ports except the X ports; wherein L, N and X are positive integers, and X is less than or equal to L.

[0039] In the above embodiments, the first set comprises at least one best reference signal resource corresponding to part of the L ports, and the second set comprises N reference signal resources corresponding to other ports of the L ports, so that the AI model trained by the network device based on the first set and the second set can predict the report quantity of at least one reference signal resource or the best reference signal resource of the other ports according to the report quantity of the best reference information resource of the part of ports.

[0040] In some embodiments of the first aspect, in some embodiments, the report quantity corresponding to the first set comprises L1-RSRP of at least one best reference signal resource corresponding to each port of X ports of the L ports.

[0041] In some embodiments of the first aspect, in some embodiments, the report quantity corresponding to the first set comprises an identifier of at least one best reference signal resource corresponding to each port of X ports of the L ports.

[0042] In some embodiments of the first aspect, in some embodiments, the first report comprises a report quantity corresponding to at least one of the first set and the second set; the first set comprises at least one best reference signal resource corresponding to each of X ports of L ports; and the second set comprises at least one best reference signal resource corresponding to each of L ports; wherein L and X are positive integers, and X is less than or equal to L.

[0043] In the above embodiments, the first set comprises at least one best reference signal resource corresponding to part of the L ports, and the second set comprises N reference signal resources corresponding to each of the L ports, so that the AI model trained by the network device based on the first set and the second set can predict the report quantity of at least one reference signal resource or the best reference signal resource of all ports according to the report quantity of at least one best reference signal resource of part of the ports.

[0044] In some embodiments of the first aspect, in some embodiments, the M reference signal resources corresponding to different ports are different.

[0045] In the above embodiments, different ports can correspond to different M reference signal resources, which can improve the flexibility and accuracy of obtaining the best reference signal resource.

[0046] In some embodiments of the first aspect, in some embodiments, a time period corresponding to the first set is greater than or equal to a time period corresponding to the second set.

[0047] In the above embodiments, the time period corresponding to the first set is greater than or equal to the time period corresponding to the second set, so that the reported first set and the second set can be used for AI models for spatial domain prediction and time domain prediction.

[0048] In some embodiments of the first aspect, in some embodiments, the first report is used for a training process of an artificial intelligence (AI) model, the first set is used to determine an input of the training process, and the second set is used to determine a label of the training process.

[0049] In the above embodiments, in the first report sent by the terminal to the network device, the first set is used to determine the input of the training process, and the second set is used to determine the label of the training process, so that the AI model trained by the network device based on the first report can predict the report quantity of the reference signal resource for different ports respectively, thereby improving the accuracy of model training.

[0050] In some embodiments of the first aspect, in some embodiments, the method further comprises: receiving, by the terminal, configuration information of the reference signal resource set sent by the network device, wherein the configuration information is used by the terminal to determine the report quantity.

[0051] In the above embodiment, the terminal can determine the report quantity according to the configuration information sent by the network device, and the efficiency of determining the report quantity by the terminal can be improved.

[0052] In some embodiments of the first aspect, the report quantity includes at least one of the following: a synchronization signal block (SSB) index; a channel state information reference signal resource index (CRI); a port index; and L1-RSRP.

[0053] In the above embodiment, the report quantity includes the above content, which can enable the AI model obtained by training to predict more specific parameter values and improve the accuracy of the AI model.

[0054] In some embodiments of the first aspect, the configuration information includes port information corresponding to the reference signal resource.

[0055] In the above embodiment, the network device sends the terminal the configuration information including the port information corresponding to the reference signal resource, so that the terminal can obtain the report quantity of the reference signal resource corresponding to the port according to the port information, and the efficiency of determining the report quantity by the terminal can be improved.

[0056] In some embodiments of the first aspect, the terminal is a near-field terminal.

[0057] In the above embodiment, the near-field terminal reports the network device the report quantity of at least one reference signal resource corresponding to at least one port, so that the AI model obtained by the network device based on the first report can predict the report quantity of the reference signal resource for different ports respectively, thereby improving the accuracy of model training.

[0058] In a second aspect, the embodiments of the present disclosure provide a communication method, which includes: a network device receiving a first report sent by a terminal, the first report including a report quantity of at least one reference signal resource corresponding to at least one port.

[0059] In some embodiments of the second aspect, the first report includes a report quantity corresponding to at least one of a first set and a second set; the first set includes M reference signal resources corresponding to each of L ports; the second set includes N reference signal resources corresponding to each of the L ports; and L, M, and N are positive integers, and M is less than or equal to N.

[0060] In some embodiments of the second aspect, in some embodiments, the first report comprises a report quantity corresponding to at least one of the first set and the second set; the first set comprises M reference signal resources corresponding to X ports of the L ports; and the second set comprises N reference signal resources corresponding to each of the L ports except the X ports; wherein L, M, and X are positive integers, and M is less than or equal to N.

[0061] In some embodiments of the second aspect, in some embodiments, the first report comprises a report quantity corresponding to at least one of the first set and the second set; the first set comprises at least one best reference signal resource corresponding to each of X ports of the L ports; and the second set comprises N reference signal resources corresponding to each of the L ports except the X ports; wherein L, N, and X are positive integers, and X is less than or equal to L.

[0062] In some embodiments of the second aspect, in some embodiments, the report quantity corresponding to the first set comprises L1-RSRP of at least one best reference signal resource corresponding to each of X ports of the L ports.

[0063] In some embodiments of the second aspect, in some embodiments, the report quantity corresponding to the first set comprises an identity of at least one best reference signal resource corresponding to each of X ports of the L ports.

[0064] In some embodiments of the second aspect, in some embodiments, the M reference signal resources corresponding to different ports are different.

[0065] In some embodiments of the second aspect, in some embodiments, a time period corresponding to the first set is greater than or equal to a time period corresponding to the second set.

[0066] In some embodiments of the second aspect, in some embodiments, the first report is used in a training process of an artificial intelligence (AI) model, the first set is used to determine an input of the training process, and the second set is used to determine a label of the training process.

[0067] In some embodiments of the second aspect, in some embodiments, the method further comprises: sending, by the network device and to the terminal, configuration information of a reference signal resource set, the configuration information being used by the terminal to determine the report quantity.

[0068] In some embodiments of the second aspect, in some embodiments, the report quantity comprises at least one of: a synchronization signal block (SSB) index; a channel state information reference signal resource index (CRI); a port index; and L1-RSRP.

[0069] In some embodiments of the second aspect, in some embodiments, the configuration information comprises port information corresponding to the reference signal resource.

[0070] In some embodiments of the second aspect, in some embodiments, the terminal is a near field terminal.

[0071] In a third aspect, the embodiments of the present disclosure provide a terminal, comprising: a transceiver module, configured to send a first report to a network device, wherein the first report comprises a report quantity of at least one reference signal resource corresponding to at least one port.

[0072] In a fourth aspect, the embodiments of the present disclosure provide a network device, comprising: a transceiver module, configured to receive a first report sent by a terminal, wherein the first report comprises a report quantity of at least one reference signal resource corresponding to at least one port.

[0073] In a fifth aspect, the embodiments of the present disclosure provide a terminal, comprising: one or more processors; wherein the processor is configured to execute the communication method of the first aspect.

[0074] In a sixth aspect, the embodiments of the present disclosure provide a network device, comprising: one or more processors; wherein the processor is configured to execute the communication method of the second aspect.

[0075] In a seventh aspect, the embodiments of the present disclosure provide a communication system, comprising a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and the network device is configured to implement the communication method of the second aspect.

[0076] In an eighth aspect, the embodiments of the present disclosure provide a storage medium, wherein the storage medium stores instructions, and the instructions, when executed on a communication device, cause the communication device to execute the method of the first aspect or the second aspect.

[0077] In a ninth aspect, the embodiments of the present disclosure provide a program product, and the program product, when executed on a communication device, causes the communication device to execute the method described in the optional implementation manner of the first aspect or the second aspect.

[0078] In a tenth aspect, the embodiments of the present disclosure provide a computer program, and the computer program, when executed on a communication device, causes the communication device to execute any of the above communication methods.

[0079] In an eleventh aspect, the embodiments of the present disclosure provide a chip or chip system. The chip or chip system comprises processing circuitry configured to execute the method described in the optional implementation manner of the first aspect or the second aspect.

[0080] It can be understood that the network function, the terminal, the communication system, the storage medium, the program product, the computer program, the chip or the chip system are used to execute the method proposed in the embodiments of the present disclosure. Therefore, the beneficial effects achieved thereby can refer to the beneficial effects in the corresponding method, which will not be described here.

[0081] The embodiments of the present disclosure propose a communication method, a terminal, a network device, a communication system and a storage medium. In some embodiments, the communication method and the information reporting method, the information receiving method and the like can be replaced with each other.

[0082] The embodiments of the present disclosure are not exhaustive, but only illustrate some embodiments, and are not specific limitations on the protection scope of the present disclosure. In the case of no contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily, for example, the scheme after removing some steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily, in addition, the optional implementation manners in an embodiment can be combined arbitrarily; in addition, the embodiments can be combined arbitrarily, for example, the steps of different embodiments or part or all of the steps of different embodiments can be combined arbitrarily, an embodiment can be combined with the optional implementation manners of other embodiments.

[0083] In the embodiments of the present disclosure, the terms and / or descriptions between the embodiments are consistent and can be referred to each other if there is no special description and logical conflict, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0084] The terms used in the embodiments of the present disclosure are only for the purpose of describing the specific embodiments, and not as a limitation on the present disclosure.

[0085] In the embodiments of the present disclosure, unless otherwise specified, the elements expressed in singular form, such as "one", "a", "the", "above", "said", "preceding", "this" and the like, can represent "one and only one", or "one or more", "at least one" and the like. For example, in the case of using articles such as "a", "an", "the" and the like in English, the noun after the article can be understood as singular expression, or can be understood as plural expression.

[0086] In the embodiments of the present disclosure, "a plurality of" means two or more.

[0087] In some embodiments, the terms "at least one of," "one or more of," "a plurality of," "multiple," and the like can be used interchangeably.

[0088] In some embodiments, the recitations "at least one of A, B," "A and / or B," "in one case A, in another case B," "in response to a case A, in response to a case B," and the like can include the following technical solutions according to the case: in some embodiments A (A is executed regardless of B); in some embodiments B (B is executed regardless of A); in some embodiments A and B are selected from A and B (A and B are selectively executed); in some embodiments A and B (A and B are both executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0089] In some embodiments, the recitations "A or B" and the like can include the following technical solutions according to the case: in some embodiments A (A is executed regardless of B); in some embodiments B (B is executed regardless of A); in some embodiments A and B are selected from A and B (A and B are selectively executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0090] The prefix words "first", "second", and the like in the embodiments of the present disclosure are merely used to distinguish different description objects, and do not constitute a limitation on the position, order, priority, quantity, or content of the description objects. The description of the description objects should refer to the description in the context of the claims or embodiments, and should not constitute an additional limitation because of the use of the prefix words. For example, the description objects are "fields", and the ordinal words before "fields" in "first field" and "second field" do not limit the position or order between "fields", and "first" and "second" do not limit whether the "fields" modified thereby are in the same message or not, nor do they limit the order of "first field" and "second field". For another example, the description objects are "levels", and the ordinal words before "levels" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description objects is not limited by the ordinal words, and can be one or more. For example, "first device", wherein the quantity of "devices" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description objects are "devices", and "first device" and "second device" can be the same device or different devices, and their types can be the same or different; for another example, the description objects are "information", and "first information" and "second information" can be the same information or different information, and their contents can be the same or different.

[0091] In some embodiments, "comprising", "including", "to indicate", "carrying", can be interpreted as directly carrying A, or indirectly indicating A.

[0092] In some embodiments, the terms "in response to", "in response to determining", "in the case of", "when", "when", "if", "if" and the like can be replaced with each other.

[0093] In some embodiments, the terms "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", "above" and the like can be replaced with each other, and the terms "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", "below" and the like can be replaced with each other.

[0094] In some embodiments, the device and the like can be interpreted as physical or virtual, and the name is not limited to the name described in the embodiments. The terms "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and the like can be replaced with each other.

[0095] In some embodiments, "network" can be interpreted as a device (for example, access network device, core network device, etc.) contained in the network.

[0096] In some embodiments, the terms “access network device (AN device),” “radio access network device (RAN device),” “base station (BS),” “radio base station,” “fixed station,” “node,” “access point,” “transmission point (TP),” “reception point (RP),” “transmission / 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),” and the like can be used interchangeably.

[0097] In some embodiments, the terms "terminal," "terminal device," "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," and so on can be replaced with each other.

[0098] In some embodiments, the access network device, the core network device, or the network device can be replaced with a terminal. For example, the embodiments of the present disclosure can also be applied to a structure in which communication between the access network device, the core network device, or the network device and the terminal is replaced with communication between a plurality of terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the terminal can also be configured to have all or part of the functions of the access network device. In addition, the terms "uplink," "downlink," and the like can also be replaced with terms corresponding to the inter-terminal communication (e.g., "side"). For example, the uplink channel, the downlink channel, and the like can be replaced with the side channel, and the uplink, the downlink, and the like can be replaced with the sidelink.

[0099] In some embodiments, the terminal can be replaced with the access network device, the core network device, or the network device. In this case, the access network device, the core network device, or the network device can also be configured to have all or part of the functions of the terminal.

[0100] In some embodiments, obtaining data, information, and the like can comply with the laws and regulations of the country in which the location is situated.

[0101] In some embodiments, data, information, and the like can be obtained after obtaining the consent of the user.

[0102] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0103] In NR, especially when the communication frequency band is in frequency range 2, due to fast attenuation of high frequency channels, in order to ensure coverage, beam-based transmission and reception need to be used.

[0104] In the beam management process, the network device configures a reference signal resource set for beam measurement. The terminal measures the reference signal resources in the reference signal resource set, and the terminal reports some of the strongest reference signal resource IDs and corresponding layer 1 reference signal received power (L1-RSRP) and / or layer 1 signal to interference plus noise ratio (L1-SINR) to the network device.

[0105] In the related art, it is assumed that the reference signal resource set configured by the network device includes X reference signal resources, and each reference signal resource corresponds to a different transmission beam of the network device. For each reference signal resource, the terminal needs to use all receive beams to measure the reference signal and obtain the beam measurement quality corresponding to all receive beams, and determine one or more best beam measurement qualities, so the terminal needs to measure M*N beam pairs. Wherein, M represents the number of network device transmission beams, and N is the number of terminal receive beams.

[0106] In some embodiments, an implementation process of beam prediction based on an AI (Artificial Intelligence) model and / or AI function is provided. Wherein, the AI function can be considered as one or more AI models that implement the same function or purpose.

[0107] In some embodiments, the AI model for beam prediction can be referred to as a beam prediction model. Of course, it can also be referred to as a beam prediction AI model, a prediction AI model, a prediction beam model, and the like. The present disclosure does not limit the name of such AI model.

[0108] In some embodiments, for the case that the beam prediction model is for spatial domain prediction, the terminal measures the L1-RSRP (possibly including the beam or beam pair ID) of set B, inputs to the beam prediction model, and the beam prediction model can predict the L1-RSRP of the best beam and / or beam pair in set A, and / or the identification of the best beam and / or beam pair in set A.

[0109] Wherein, the relationship between set B and set A can include the following two:

[0110] The first relationship is that set B is a subset of set A. For example, set A contains 32 reference signal resources (each reference signal resource corresponds to a beam direction), and set B contains N partial reference signal resources, such as set B contains 8 reference signal resources out of the 32 reference signal resources, i.e. N = 8.

[0111] The second relationship is that set B is a wide beam and set A is a narrow beam. For example, set A contains 32 reference signal resources (each reference signal resource corresponds to a beam direction, and the 32 reference signal resources cover a direction of 120 degrees). And set B contains another Y reference signal resources, such as Y = 8. These Y reference signal resources also cover a direction of 120 degrees, i.e. the beam direction of each reference signal resource in set B covers the beam direction of multiple reference signal resources in set A. It can be understood that 32 / Y reference signal resources in set A and one reference signal resource in set B are in quasi co location (QCL) Type D relationship.

[0112] It can be understood that for the examples of the above first relationship and second relationship, only the case of transmitting beams is described. When considering beam pairs including transmitting beams and receiving beams, the receiving beams of the terminal also need to be considered. For example, 32 transmitting beams and 4 receiving beams, then set A is 32*4 beam pairs, and set B can be 32 beam pairs, 16 beam pairs, etc.

[0113] If the performance monitoring of the AI model is not required, and the AI model has been trained in advance, then during the derivation based on the AI model, the network device only needs to periodically send the reference signals on the reference signal resources in set B (such as based on the first period). Then the terminal measures the L1-RSRP of the reference signals on the reference signal resources in set B, inputs to the beam prediction model, and outputs the L1-RSRP corresponding to the reference signal resources in set A or outputs the strongest one or more reference signal resource IDs or beam IDs in the 32 reference signal resources in set A.

[0114] If the performance of the AI model needs to be monitored, the network device periodically transmits the reference signals in set A (for example, based on a second period, where the second period is greater than the first period) in addition to set B. The terminal measures the reference signals on the reference signal resources in set B, inputs the measurement results into the AI model, obtains predicted beam information, and reports the predicted beam information to the network device. Meanwhile, the terminal also measures the L1-RSRP of the reference signals on all reference signal resources in set A, and reports the measurement results of set A or the reference signal resource ID corresponding to the best beam in set A obtained based on the measurement results of set A as the beam information obtained by the traditional method to the network device.

[0115] It can be understood that if set B is a subset of set A, it is equivalent to the terminal only needing to measure all beams or beam pairs in set A.

[0116] In the embodiments of the present disclosure, the "terminal-side model" and the "AI model deployed on the terminal" can be used interchangeably. The "network device-side model" and the "AI model deployed on the network device" can be used interchangeably.

[0117] In some embodiments, for the case where the beam prediction model is a time-domain prediction, the terminal measures the L1-RSRP of the historical time set B, inputs it into the AI model, and predicts the L1-RSRP of the future time set A or the ID of the best beam in set A. In addition to the above two relationships, there is another relationship between set B and set A, which is that set B and set A are the same.

[0118] If the AI model is used for beam prediction, the reference signals at the future time can not be transmitted; that is, the beam information can be obtained based on the output of the AI model and reported to the network device.

[0119] If the performance of the AI model is monitored based on the method in the related art, the reference signals at the future time also need to be transmitted, and the terminal measures the reference signals at the future time and obtains the beam information and reports it to the base station. Therefore, when monitoring the performance of the model, the network device needs to periodically transmit the transmission beams in set B and set A, and the terminal needs to measure all beams or beam pairs in set B and set A.

[0120] FIG. 1A is a schematic diagram of a communication scenario of a far-field terminal. FIG. 1B is a schematic diagram of a communication scenario of a near-field terminal.

[0121] FIG. 1A shows a schematic diagram of far-field beam steering, and FIG. 1B shows a schematic diagram of near-field beam focusing. As shown in FIG. 1A, in a communication scenario of a far-field terminal, the beam is like a “flashlight”, and the wavefront of the beam is a planar wavefront. As shown in FIG. 1B, in a communication scenario of a near-field terminal, the beam is like a “spotlight”, and the wavefront of the beam is a spherical wavefront. There is a case of same direction different distances for the beam.

[0122] As shown in FIG. 1A, the related art considers the beam of a far-field terminal, that is, for any antenna element of an antenna panel, the best beam direction reaching the terminal is the same. Therefore, for each terminal, the antenna panel only needs to find one best beam.

[0123] However, for a near-field terminal as shown in FIG. 1B, the best beam direction reaching the terminal is different for different antenna elements of the same antenna panel. Therefore, based on the beam measurement of the related art, it is equivalent to measuring the best beam for each antenna element (or each element group or each port or each port group or each subarray) of the antenna panel of the base station. Then the number of beam measurements is increased by a multiple of the number of ports included in the antenna panel based on the measurement method of the related art.

[0124] In order to reduce the measurement, the best beam can be predicted based on an AI model. The AI model in the related art can predict one best beam for one terminal and one antenna panel. However, in the near-field case, each port needs to predict its corresponding best beam, so the input and output of the AI model will be different.

[0125] For the AI model in the near-field communication scenario, how to collect training data is a problem to be solved.

[0126] The embodiment of the present disclosure provides a communication method, a terminal sends a first report to a network device, the first report includes a report quantity of at least one reference signal resource corresponding to at least one port, so that the AI model obtained by the network device based on the first report can predict the report quantity of the reference signal resource for different ports respectively, thereby improving the accuracy of model training.

[0127] FIG. 1C is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0128] As shown in FIG. 1C, the communication system 100 includes a terminal 101 and a network device 102.

[0129] In some embodiments, the terminal 101 can be a user equipment (UE), for example, at least one of a mobile phone, a wearable device, an Internet of Things (IoT) device, a communication-capable automobile, a smart automobile, a tablet computer (Pad), 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 smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, a wireless terminal device in smart home, but is not limited thereto.

[0130] In the embodiments of the present disclosure, the terminal 101 can be a near-field terminal, where the near-field terminal refers to a terminal where beams emitted by different ports of the same antenna panel arrive at different directions of the terminal.

[0131] In some embodiments, the network device 102 can be one functional network element in a core network device, which can be one device including all or part of the first network element, the second network element, etc., or can be multiple devices or device groups including all or part of the first network element, the second network element, etc. The network element can be virtual or physical. The core network includes at least one of an evolved packet core (EPC), a 5G core network (5GCN), a next-generation core (NGC), for example.

[0132] In some embodiments, the network device 102 can include at least one of an access network device and a core network device.

[0133] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network, and the access network device can include at least one of an evolved NodeB (eNB) in a 5G communication system, a next generation eNB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (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, an access node in a Wi-Fi system, but is not limited thereto.

[0134] In some embodiments, the technical solutions of the present disclosure can be applied to an Open RAN architecture, at this time, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be realized through software or programs.

[0135] In some embodiments, the access network device can be composed of a central unit (CU) and a distributed unit (DU), wherein the CU can also be referred to as a control unit (control unit). The CU-DU structure can split the protocol layers of the access network device, and part of the functions of the protocol layers are controlled by the CU, and the remaining part or all of the functions of the protocol layers are distributed in the DU and controlled by the CU, but the present disclosure is not limited thereto.

[0136] In some embodiments, the core network device can be one device including one or more network elements, or can be multiple devices or device groups including all or part of the above-mentioned one or more network elements. The network element can be virtual or physical. The core network can include at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0137] It can be understood that the communication system described in the embodiments of the present disclosure is for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions proposed by the embodiments of the present disclosure. Those skilled in the art can know that, with the evolution of system architecture and the appearance of new business scenarios, the technical solutions proposed by the embodiments of the present disclosure are also applicable to similar technical problems.

[0138] The following embodiments of the present disclosure can be applied to the communication system 100 shown in FIG. 1C or part of the subjects, but are not limited thereto. The subjects shown in FIG. 1C are exemplary, and the communication system can include all or part of the subjects in FIG. 1C, or other subjects other than FIG. 1, the number and form of each subject is arbitrary, each subject can be real or virtual, the connection relationship between each subject is exemplary, each subject can not be connected or can be connected, the connection can be in any way, can be direct connection or indirect connection, can be wired connection or wireless connection.

[0139] 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 (Bluetooth (registered trademark)), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), system using other communication methods, next-generation system expanded based thereon, and the like. In addition, a plurality of systems can be combined (for example, combination of LTE or LTE-A and 5G, and the like).

[0140] FIG. 2 is an interaction diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 2, the embodiment of the present disclosure relates to a communication method, and the method includes:

[0141] In step S2101, the network device sends configuration information of a reference signal resource set to the terminal.

[0142] In some embodiments, the terminal receives configuration information of a set of reference signal resources sent by the network device.

[0143] In some embodiments, the terminal can be a near-field terminal. Wherein, different antenna elements (or element groups or ports or port groups or subarrays) of the same antenna panel have different best beams to the near-field terminal.

[0144] In some embodiments, the set of reference signal resources includes a plurality of reference signal resources.

[0145] In some embodiments, the configuration information can include port information corresponding to the reference signal resources.

[0146] In some embodiments, the port information can be a port number and / or a port identifier.

[0147] In some embodiments, the configuration information can further include at least one of the following: a synchronization signal block (SSB) index; a channel state information reference signal resource index (CRI); a layer 1 reference signal receiving power (L1-RSRP).

[0148] In step S2102, the terminal determines a report quantity based on the configuration information.

[0149] In some embodiments, the terminal can determine a report quantity of at least one reference signal resource corresponding to at least one port (or antenna element or element group or port group or subarray) based on the configuration information sent by the network device.

[0150] In some embodiments, the report quantity can include at least one of the following: an SSB index; a CRI; a port index; an L1-RSRP.

[0151] For example, the report quantity can include a port index. For another example, the report quantity can include an L1-RSRP. For another example, the report quantity can include a port index and an L1-RSRP. For another example, the report quantity can include a reference signal resource identifier, a port index and an L1-RSRP.

[0152] In the following illustration, the port is taken as an example for illustration, and it can be understood that the port can be replaced by an antenna element or element group or port group or subarray.

[0153] In some embodiments, the terminal can measure the reference signals based on the configuration information sent by the network device to obtain the reporting quantity of at least one reference signal resource corresponding to at least one port.

[0154] For example, the configuration information includes a port index, an SSB index and an L1-RSRP. The terminal can determine the to-be-measured port according to the port index, and determine the to-be-measured reference signal resource according to the SSB index. The terminal measures the value of the L1-RSRP of each to-be-measured reference signal resource corresponding to each to-be-measured port.

[0155] It can be understood that the configuration information sent by the network device to the terminal includes the parameters of the reporting quantity, and the terminal determines the specific value of the reporting quantity corresponding to the parameters of the reporting quantity based on the configuration information.

[0156] In step S2103, the terminal sends the first report to the network device.

[0157] In some embodiments, the network device receives the first report sent by the terminal.

[0158] In some embodiments, the first report can include the reporting quantity of at least one reference signal resource corresponding to at least one port (or antenna array element or array element group or port group or subarray).

[0159] In some embodiments, the first report can include the reporting quantity corresponding to at least one of the first set and the second set. For example, the first report can include the reporting quantity corresponding to the first set; or, the first report can include the reporting quantity corresponding to the second set; or, the first report can include the reporting quantity corresponding to the first set and the second set. The reporting quantity corresponding to the first set can be the reporting quantity corresponding to at least one reference signal resource included in the first set, and the reporting quantity corresponding to the second set can be the reporting quantity corresponding to at least one reference signal resource included in the second set.

[0160] In some embodiments, the first set can be the set B described above, and the second set can be the set A described above.

[0161] In some embodiments, the first report is used in the training process of the AI model, the first set is used to determine the input of the training process, and the second set is used to determine the label of the training process.

[0162] For example, the reporting quantity corresponding to the first set can be used as the input in the training process of the AI model, and the reporting quantity corresponding to the second set can be used as the label in the training process of the AI model.

[0163] In some embodiments, the AI model can be a beam prediction model.

[0164] In some embodiments, the terminal sends a first report to the network device, and the network device trains the AI model based on the first report sent by the terminal.

[0165] In some embodiments, the network device inputs the report quantity corresponding to the first set in the first report into the AI model to be trained to obtain a prediction result; the report quantity corresponding to the second set in the first report can be used as a label in the training process, that is, a loss value can be determined according to the prediction result and the report quantity corresponding to the second set, and the model parameters of the AI model are adjusted according to the loss value, so that the loss value between the output prediction result and the report quantity corresponding to the second set is less than a preset value, and the training of the AI model is completed.

[0166] In some embodiments, after the AI model training is completed, the report quantity corresponding to the first set can be input to obtain a prediction value.

[0167] In some embodiments, different AI models (hereinafter referred to as first AI model, second AI model, third AI model and fourth AI model, but the present disclosure is not limited thereto) correspond to different first sets and second sets, that is, different AI models can be trained and obtained according to different first sets and second sets.

[0168] In some embodiments, the report quantity corresponding to the first set can be referred to as a first report quantity, and the report quantity corresponding to the second set can be referred to as a second report quantity.

[0169] In some embodiments, the first report quantity can include at least one of the following: SSB index; CRI; port index; L1-RSRP.

[0170] In some embodiments, the second report quantity can include at least one of the following: SSB index; CRI; port index; L1-RSRP.

[0171] In some embodiments, the first report quantity and the second report quantity corresponding to different AI models can be different. For example, in the first AI model, the first report quantity can include L1-RSRP, and the second report quantity can include L1-RSRP and / or reference signal resource identifier. In the second AI model, the first report quantity can include L1-RSRP, and the second report quantity can include reference signal resource identifier. In the third AI model, the first report quantity can include L1-RSRP and / or reference signal resource identifier, and the second report quantity can include reference signal resource identifier. In the fourth AI model, the first report quantity can include L1-RSRP and / or reference signal resource identifier, and the second report quantity can include reference signal resource identifier.

[0172] The following will be described in detail for different AI models (a first AI model, a second AI model, a third AI model, and a fourth AI model, but the present disclosure is not limited thereto).

[0173] The following will be described for a first set and a second set corresponding to the first AI model.

[0174] In some embodiments, the first set can include M reference signal resources corresponding to each of the L ports; and the second set can include N reference signal resources corresponding to each of the L ports. Wherein L, M, and N are all positive integers, and M is less than or equal to N.

[0175] In some embodiments, the first report quantity can include L1-RSRP, and the second report quantity can include L1-RSRP and / or reference signal resource identification.

[0176] In some embodiments, the first report sent by the terminal to the network device can include L1-RSRP corresponding to the reference signal resources (or beams or beam pairs) in the first set, and the second report sent by the terminal to the network device can include L1-RSRP and / or reference signal resource identification corresponding to the reference signal resources (or beams or beam pairs) in the second set.

[0177] In some embodiments, the M reference signal resources corresponding to different ports are different.

[0178] For example, the first AI model can predict L1-RSRP of N reference signal resources corresponding to each of the L ports through L1-RSRP of M reference signal resources corresponding to each of the L ports. For another example, the first AI model can predict L1-RSRP and / or reference signal resource identification of at least one best reference signal resource in N reference signal resources corresponding to each of the L ports through L1-RSRP of M reference signal resources corresponding to each of the L ports. Wherein L, M, and N are all positive integers.

[0179] Then, the terminal can measure M (for example, M = 8) reference signal resources corresponding to each of the L (for example, L = 10) ports to obtain L1-RSRP of L*M reference signal resources, and the L1-RSRP of the L*M reference signal resources can be used as a report quantity corresponding to the first set.

[0180] The M reference signal resources corresponding to each of the L ports can be the same or different.

[0181] For example, the M reference signal resources corresponding to the first port are 0, 4, 8, 12, and 16, the M reference signal resources corresponding to the second port are 1, 5, 9, 13, and 17, the M reference signal resources corresponding to the third port are 2, 6, 10, 14, and 18, and the M reference signal resources corresponding to the fourth port are 3, 7, 11, 15, and 19.

[0182] It can be understood that the above is an example of 5 reference signal resources corresponding to each of the 4 ports. The number of ports and the number of reference signal resources corresponding to each port are not limited in the disclosure.

[0183] The terminal can measure the N reference signal resources corresponding to each of the L ports to obtain L*N L1-RSRPs of the N reference signal resources, and the L*N L1-RSRPs of the N reference signal resources can be used as the reporting quantity corresponding to the second set. Alternatively, the terminal can determine at least one best reference signal resource corresponding to each of the L ports according to the L1-RSRP of the N reference signal resources corresponding to each of the L ports, and the L1-RSRP and / or reference signal resource identifier of the at least one best reference signal resource corresponding to each of the L ports can be used as the reporting quantity corresponding to the second set.

[0184] For example, in the training process of the first AI model, the L1-RSRPs of the L*M reference signal resources can be used as input data of the first AI model, and the L1-RSRP and / or reference signal resource identifier of the reference signal resource or the best reference signal resource corresponding to each of the L ports can be used as a training label. The first AI model is trained to obtain a trained first AI model.

[0185] For example, after the first AI model is trained, the L1-RSRP of at least one reference signal resource corresponding to at least one port can be input to predict the L1-RSRP and / or reference signal resource identifier of at least one reference signal resource or at least one best reference signal resource corresponding to each of the at least one port.

[0186] The first set and the second set corresponding to the second AI model are described below.

[0187] In some embodiments, the first set includes M reference signal resources corresponding to X ports of L ports, respectively; the second set includes N reference signal resources corresponding to each of the L ports; wherein L, M, X, and N are positive integers, X is less than or equal to L, and M is less than or equal to N.

[0188] In some embodiments, the X ports are every first interval of the L ports, or the X ports are randomly determined from the L ports. The first interval may, for example, be one port, two ports, etc., and the disclosure does not limit this.

[0189] In some embodiments, the first report quantity can include L1-RSRP, and the second report quantity can include reference signal resource identification.

[0190] In some embodiments, the first report sent by the terminal to the network device can include L1-RSRP corresponding to the reference signal resources (or beams or beam pairs) in the first set, and the second report sent by the terminal to the network device can include reference signal resource identification corresponding to the reference signal resources (or beams or beam pairs) in the second set.

[0191] For example, the second AI model can predict, through L1-RSRP of M reference signal resources respectively corresponding to part of the L ports (for example, X ports), L1-RSRP of N reference signal resources respectively corresponding to each of the L ports, or L1-RSRP and / or reference signal resource identification of the best reference signal resource. Wherein, L, M, X and N are positive integers, and X is less than or equal to L.

[0192] Then, the terminal can measure M (for example, M = 5) reference signal resources respectively corresponding to part of the L (for example, L = 10) ports, wherein the part of the L ports may, for example, be every other port, every two ports, or may, for example, be X ports randomly determined from the L ports. Taking every other port as an example, the part of the L ports may, for example, be port 1, port 3, port 5, port 7 and port 9, and then the terminal can measure L*M / 2 reference signal resources. L1-RSRP of the L*M / 2 reference signal resources can be used as the report quantity corresponding to the first set.

[0193] The M reference signal resources corresponding to each of the part of the L ports may, for example, be the same or different.

[0194] For example, the M reference signal resources corresponding to the first port are 0, 4, 8, 12 and 16, the M reference signal resources corresponding to the third port are 1, 5, 9, 13 and 17, and the M reference signal resources corresponding to the fifth port are 2, 6, 10, 14 and 18.

[0195] It can be understood that the above is an example of taking 5 reference signal resources corresponding to each of the 3 ports of the L ports, and the disclosure does not limit the number of ports and the number of reference signal resources corresponding to each port.

[0196] The terminal can perform measurement on N (e.g., N = 32) reference signal resources corresponding to each of the L (e.g., L = 10) ports to obtain L1-RSRP of the L*N reference signal resources, determine the best reference signal resource corresponding to each of the L ports according to the L1-RSRP of the N reference signal resources corresponding to each of the L ports, and the reference signal resource identifier of the N reference signal resources or at least one best reference signal resource corresponding to each of the L ports can be used as the reporting quantity corresponding to the second set.

[0197] For example, in the training process of the second AI model, the L1-RSRP of the L*M / 2 reference signal resources can be used as the input data of the second AI model, and the L1-RSRP and / or the reference signal resource identifier of at least one reference signal resource corresponding to each of the L ports or at least one best reference signal resource can be used as the training label to train the second AI model to obtain the trained second AI model.

[0198] For example, after the training of the second AI model is completed, the L1-RSRP of at least one reference signal resource corresponding to at least one port can be input to predict the L1-RSRP and / or the reference signal resource identifier of at least one reference signal resource corresponding to each of the at least one port or at least one best reference signal resource.

[0199] The first set and the second set corresponding to the third AI model are described below.

[0200] In some embodiments, the first set includes at least one best reference signal resource corresponding to each of X ports in the L ports; and the second set includes N reference signal resources corresponding to each of the L ports except the X ports; wherein L, X and N are positive integers, and X is less than or equal to L.

[0201] In some embodiments, the X ports are every first interval of the L ports, or the X ports are randomly determined from the L ports. The first interval may, for example, be one port, two ports, etc., and the disclosure does not limit the same.

[0202] In some embodiments, the at least one best reference signal resource can be one best reference signal resource, or a plurality (e.g., two, three) of best reference signal resources, and the disclosure does not limit the number of best reference signal resources.

[0203] In some embodiments, the first report quantity can comprise L1-RSRP and / or reference signal resource identification, for example, the first report quantity comprises L1-RSRP, and for another example, the first report quantity comprises reference signal resource identification (or beam identification or beam pair identification). The second report quantity can comprise L1-RSRP and / or reference signal resource identification, for example, the second report quantity can comprise reference signal resource identification, and for another example, the second report quantity can comprise reference signal resource identification and L1-RSRP.

[0204] In some embodiments, the first report quantity can comprise L1-RSRP and / or reference signal resource identification, for example, the first report quantity comprises L1-RSRP, and for another example, the first report quantity comprises reference signal resource identification (or beam identification or beam pair identification). The second report quantity can comprise L1-RSRP and / or reference signal resource identification, for example, the second report quantity can comprise reference signal resource identification, and for another example, the second report quantity can comprise reference signal resource identification and L1-RSRP.

[0205] For example, the third AI model can predict, through the L1-RSRP and / or reference signal resource identification of at least one best reference signal resource corresponding to part of ports (e.g., X ports) in L ports, the L1-RSRP and / or reference signal resource identification of N reference signal resources or at least one best reference signal resource in N reference signal resources corresponding to each of the other ports (e.g., (L-X) ports) in L ports. Wherein, L, X and N are positive integers, and X is less than or equal to L.

[0206] Then, the terminal can measure N (e.g., N=32) reference signal resources corresponding to X (e.g., X=5) ports in L (e.g., L=10) ports respectively, wherein part of the L ports are, for example, every other port, every two ports, or for another example, X ports randomly determined from the L ports. Taking every other port as an example, the X ports in the L ports are, for example, port 1, port 3, port 5, port 7 and port 9, and then the terminal can measure the L1-RSRP of X*N reference signal resources. The best reference signal resource corresponding to each of the X ports is determined according to the L1-RSRP of N reference signal resources corresponding to each of the X ports, and the L1-RSRP and / or reference signal resource identification of the best reference signal resource corresponding to each of the X ports can be used as the report quantity corresponding to the first set.

[0207] The terminal can perform measurement on N (e.g., N = 32) reference signal resources corresponding to other ports (e.g., other (L-X) ports) of the L (e.g., L = 10) ports, respectively. The other (L-X) ports may, for example, be: port 2, port 4, port 6, port 8, and port 10. The terminal performs measurement on the N reference signal resources corresponding to the (L-X) ports to obtain L1-RSRP of the (L-X)*N reference signal resources, and the L1-RSRP of the (L-X)*N reference signal resources can be used as the reporting quantity corresponding to the second set. Alternatively, the terminal determines the best reference signal resource corresponding to each of the (L-X) ports according to the L1-RSRP of the N reference signal resources corresponding to each of the (L-X) ports, and the L1-RSRP and / or reference signal resource identifier of at least one best reference signal resource corresponding to each of the (L-X) ports can be used as the reporting quantity corresponding to the second set.

[0208] For example, in the training process of the third AI model, the L1-RSRP and / or reference signal resource identifier of at least one best reference signal resource corresponding to each of the X ports can be used as input data of the third AI model, and the L1-RSRP and / or reference signal resource identifier of at least one best reference signal resource corresponding to each of the (L-X) ports can be used as a training label to train the third AI model to obtain the trained third AI model.

[0209] For example, after the training of the third AI model is completed, the L1-RSRP and / or reference signal resource identifier of at least one best reference signal resource corresponding to each of the X ports is input, and the L1-RSRP and / or reference signal resource identifier of at least one best reference signal resource corresponding to each of the other ports can be predicted.

[0210] The first set and the second set corresponding to the fourth AI model are described below.

[0211] In some embodiments, the first set includes at least one best reference signal resource corresponding to each of the X ports of the L ports; the second set includes N reference signal resources corresponding to each of the L ports; wherein L, X, and N are positive integers, and X is less than or equal to L.

[0212] In some embodiments, the X ports are every first interval of the L ports, or the X ports are randomly determined from the L ports. The first interval may, for example, be one port, two ports, etc., and the disclosure does not limit the same.

[0213] In some embodiments, the first report quantity can comprise L1-RSRP and / or reference signal resource identification, and the second report quantity can comprise L1-RSRP and / or reference signal resource identification.

[0214] In some embodiments, the first report sent by the terminal to the network device can comprise L1-RSRP and / or reference signal resource identification corresponding to the reference signal resources (or beams or beam pairs) in the first set, and the second report sent by the terminal to the network device can comprise L1-RSRP and / or reference signal resource identification corresponding to the reference signal resources (or beams or beam pairs) in the second set.

[0215] For example, the fourth AI model can predict, through L1-RSRP and / or reference signal resource identification of N reference signal resources corresponding to each of the X ports in the L ports, L1-RSRP and / or reference signal resource identification of the best reference signal resource in the N reference signal resources corresponding to each of the X ports in the L ports. Wherein, L, X and N are positive integers, and X is less than or equal to L.

[0216] Then, the terminal can measure N (for example, N = 32) reference signal resources corresponding to X (for example, X = 5) ports in L (for example, L = 10) ports, wherein, for example, every other port, every two ports, or for example, X ports randomly determined from the L ports, in the L ports. Taking every other port as an example, the X ports in the L ports are, for example, port 1, port 3, port 5, port 7 and port 9, and the terminal can measure L1-RSRP of X*N reference signal resources. The L1-RSRP and / or reference signal resource identification of the best reference signal resource corresponding to each of the X ports is determined according to the L1-RSRP of the N reference signal resources corresponding to each of the X ports, and the L1-RSRP and / or reference signal resource identification of the best reference signal resource corresponding to each of the X ports can be used as the report quantity corresponding to the first set.

[0217] The terminal can measure N (for example, N = 32) reference signal resources corresponding to each of the L (for example, L = 10) ports, obtain L1-RSRP of L*N reference signal resources, and determine the best reference signal resource corresponding to each of the L ports according to the L1-RSRP of the N reference signal resources corresponding to each of the L ports. The L1-RSRP and / or reference signal resource identification of the best reference signal resource corresponding to each of the L ports can be used as the report quantity corresponding to the second set.

[0218] For example, in the training process of the fourth AI model, the L1-RSRP and / or reference signal resource identifier of the best reference signal resource corresponding to each of the X ports can be used as input data of the fourth AI model, and the L1-RSRP and / or reference signal resource identifier of the best reference signal resource corresponding to each of the L ports can be used as a training label. The fourth AI model is trained to obtain the trained fourth AI model.

[0219] For example, after the training of the fourth AI model is completed, the L1-RSRP and / or reference signal resource identifier of the best reference signal resource corresponding to each of the partial ports can be input, and the L1-RSRP and / or reference signal resource identifier of the best reference signal resource corresponding to each of all the ports can be predicted.

[0220] In some embodiments, the time period corresponding to the first set is greater than or equal to the time period corresponding to the second set.

[0221] In some embodiments, if it is spatial domain beam prediction, the time periods of the first set and the second set can be the same, and the first report can include the first set and the second set measured for the same time period.

[0222] In some embodiments, if it is time domain beam prediction, the time period of the second set can be less than or equal to the time period of the first set, and the first report can include the first set measured for K first time periods and the second set measured for H second time periods, K and H are positive integers.

[0223] The communication method provided by the embodiments of the present disclosure is that the terminal sends a first report to the network device, and the first report includes the report quantity of at least one reference signal resource corresponding to at least one port, so that the AI model obtained by the network device based on the first report can predict the report quantity of the reference signal resource for different ports respectively, thereby improving the accuracy of model training.

[0224] In the embodiments of the present disclosure, the best reference signal resource is the reference signal resource with the maximum L1-RSRP.

[0225] In the embodiments of the present disclosure, the L1-RSRP can be replaced by L1-SINR.

[0226] The communication method related to the embodiments of the present disclosure can include at least one of steps S2101-S2103. For example, step S2101 can be implemented as an independent embodiment, step S2102 can be implemented as an independent embodiment, S2103 can be implemented as an independent embodiment, steps S2101+S2102 can be implemented as an independent embodiment, steps S2102+S2103 can be implemented as an independent embodiment, but not limited thereto.

[0227] In some embodiments, steps S2101 and S2102 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0228] In some embodiments, other optional implementations described before or after the corresponding description of FIG. 2 can be referred to.

[0229] In some embodiments, the terms such as “reference signal resource”, “beam” and “beam pair” can be replaced with each other.

[0230] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and the terms such as “information”, “message”, “signal”, “signaling”, “report”, “configuration”, “indication”, “instruction”, “command”, “channel”, “parameter”, “domain”, “field”, “symbol”, “symbol”, “codebook”, “codeword”, “codepoint”, “bit”, “data”, “program”, “chip” and the like can be replaced with each other.

[0231] In some embodiments, the terms such as “time”, “time point”, “time”, “time position” and the like can be replaced with each other, and the terms such as “duration”, “period”, “time window”, “window”, “time” and the like can be replaced with each other.

[0232] In some embodiments, “acquire”, “obtain”, “get”, “receive”, “transmit”, “bidirectional transmission”, “send and / or receive” can be replaced with each other, and can be interpreted as receiving from other subjects, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, and various meanings such as autonomous implementation.

[0233] In some embodiments, the terms “sending”, “transmitting”, “reporting”, “issuing”, “transferring”, “bidirectional transferring”, “sending and / or receiving”, and the like can be replaced by each other.

[0234] In some embodiments, the terms “certain”, “preseted”, “preset”, “set”, “indicated”, “certain”, “arbitrary”, “first”, and the like can be replaced by each other, and “certain A”, “preset A”, “preset A”, “set A”, “indicated A”, “certain A”, “arbitrary A”, “first A” can be interpreted as A specified in advance in a protocol or the like, A obtained by setting, configuration, or indication, or A specific, certain, arbitrary, or first, but not limited thereto.

[0235] In some embodiments, the determination or judgment can be made 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 not limited thereto.

[0236] In some embodiments, “not expecting to receive” can be interpreted as not receiving in the time domain resource and / or the frequency domain resource, or as not performing subsequent processing on the data or the like after receiving the data or the like; “not expecting to send” can be interpreted as not sending, or as sending but not expecting the receiver to respond to the content of the sending.

[0237] FIG. 3A is a flow diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 3A, the present disclosure relates to a communication method, and the above method comprises:

[0238] Step S3101, obtaining configuration information of a reference signal resource set.

[0239] The optional implementation of step S3101 can refer to the optional implementation of step S2101 of FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0240] In some embodiments, the terminal receives the configuration information of the reference signal resource set sent by the network device, but not limited thereto, and can also receive the configuration information of the reference signal resource set sent by other subjects.

[0241] Step S3102, determining a reporting quantity based on the configuration information.

[0242] The optional implementation of step S3102 can refer to the optional implementation of step S2102 of FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0243] In some embodiments, the terminal determines the reporting quantity based on the configuration information.

[0244] Step S3103: transmitting the first report.

[0245] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0246] In some embodiments, the terminal transmits the first report to the network device.

[0247] The communication method involved in the embodiments of the present disclosure can include at least one of steps S3101-S3103. For example, step S3101 can be implemented as an independent embodiment, step S3102 can be implemented as an independent embodiment, S3103 can be implemented as an independent embodiment, steps S3101+S3102 can be implemented as an independent embodiment, steps S3102+S3103 can be implemented as an independent embodiment, but is not limited thereto.

[0248] In some embodiments, steps S3101 and S3102 are optional, and one or more of the steps can be omitted or replaced in different embodiments.

[0249] FIG. 3B is a flow diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 3B, the embodiments of the present disclosure involve a communication method, and the above method includes:

[0250] Step S3201: transmitting the first report.

[0251] The optional implementation of step S3201 can refer to the optional implementation of step S2103 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0252] In some embodiments, the terminal transmits the first report to the network device.

[0253] FIG. 4A is a flow diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 4A, the embodiments of the present disclosure involve a communication method, and the above method includes:

[0254] Step S4101: transmitting configuration information of a reference signal resource set.

[0255] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0256] In some embodiments, the network device sends, to the terminal, configuration information of the set of reference signal resources.

[0257] At step S4102, the first report is acquired.

[0258] The optional implementation of step S4102 can refer to the optional implementation of step S2103 in FIG. 2 and other associated parts in the embodiments related to FIG. 2, which will not be repeated here.

[0259] In some embodiments, the network device receives the first report sent by the terminal.

[0260] The communication method related to the embodiments of the present disclosure can include at least one of steps S4101-S4102. For example, step S4101 can be implemented as an independent embodiment, and step S4102 can be implemented as an independent embodiment, but is not limited thereto.

[0261] In some embodiments, step S4101 is optional, and one or more of the steps can be omitted or replaced in different embodiments.

[0262] FIG. 4B is a flow diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 4B, the embodiments of the present disclosure relate to a communication method, and the above method includes:

[0263] At step S4201, the first report is acquired.

[0264] The optional implementation of step S4201 can refer to the optional implementation of step S2103 in FIG. 2 and other associated parts in the embodiments related to FIG. 2, which will not be repeated here.

[0265] In some embodiments, the network device receives the first report sent by the terminal.

[0266] FIG. 5 is an interaction diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 5, the embodiments of the present disclosure relate to a communication method, and the above method includes:

[0267] At step S5101, the terminal sends, to the network device, the first report.

[0268] The optional implementation of step S5101 can refer to step S2103 in FIG. 2, step S3103 in FIG. 3, step S4102 in FIG. 4, and other associated parts in the embodiments related to FIG. 2, FIG. 3, and FIG. 4, which will not be repeated here.

[0269] In some embodiments, the above method can include the method of the above embodiments of the communication system side, the terminal side, the network device side, and the like, which will not be repeated here.

[0270] The embodiment of the present disclosure proposes a method for network side AI model data collection for near-field beam prediction, which is used to determine the content corresponding to the input, output or label of the AI model.

[0271] In some embodiments, the terminal receives configuration information sent by the network device, which can include configuration information of the reference signal resource set, and can also include at least one or more report quantity parameters. The terminal measures the reference signal resource in the reference signal resource set to obtain the corresponding report quantity and sends it to the network device.

[0272] In some embodiments, each of the one or more report quantities can include at least one of the following:

[0273] SSB index;

[0274] CSI-RS resource index (CRI);

[0275] Port index;

[0276] L1-RSRP.

[0277] In some embodiments, different models can correspond to different report quantity parameters, and the input and output of the model can include at least one of the following. Wherein the input corresponds to the first set and the output corresponds to the second set.

[0278] For example, model 1 is the input (corresponding to the first set) and the output (corresponding to the second set) as follows:

[0279] The first set can include: for each port (for example, the number of ports is L), input L1-RSRP of M (for example, M is 8) base station transmission beams (pairs). If it is the L1-RSRP corresponding to the base station transmission beam, then the terminal reception beam corresponds to the best reception beam (Rx beam), or the quasi-optimal beam, or the random reception beam or a certain fixed reception beam (the following model is also applicable). That is, the first set needs to obtain L*M L1-RSRP.

[0280] Wherein, the M beams corresponding to each port can be different (or the same), such as:

[0281] The M beams corresponding to the first port are 0, 4, 8, 12, 16…

[0282] The M beams corresponding to the second port are 1, 5, 9, 13, 17…

[0283] The M beams corresponding to the third port are 2, 6, 10, 14, 18…

[0284] The M beams corresponding to the fourth port are 3, 7, 11, 15, 19, and so on.

[0285] …

[0286] The second set can include: for each port (or one or more specified ports) of the at least one port, output the best 1 / 2 / 4 Tx beam (or beam pair) IDs in the N (for example, N is 32) Tx beams (pairs) and / or the L1-RSRP corresponding to at least one beam (or the L1-RSRP corresponding to each beam, respectively). If the second set is actually measured, if the model needs to output the best beam corresponding to each port, L*N L1-RSRPs need to be measured.

[0287] It can be understood that the model 1 can predict the L1-RSRP corresponding to the N beams of each port by the L1-RSRP corresponding to the M beams of each port input, or predict the ID and / or L1-RSRP of the best beam corresponding to each port.

[0288] For example, the model 2 inputs (corresponding to the first set) and outputs (corresponding to the second set) as follows:

[0289] The first set can include: for every other port (or every two, every three, …), input the L1-RSRP of the M beams in the first set. That is, the first set needs to obtain L*M / 2 (or L*M / 3, L*M / 4, …) L1-RSRPs.

[0290] Wherein, the M beams corresponding to each port can be different (or the same), such as:

[0291] The M beams corresponding to the first port are 0, 4, 8, 12, 16, and so on.

[0292] The M beams corresponding to the third port are 1, 5, 9, 13, 17, and so on.

[0293] The M beams corresponding to the fifth port are 2, 6, 10, 14, 18, and so on.

[0294] …

[0295] The second set can include: for each port (or one or more specified ports) of the at least one port, output the best 1 / 2 / 4 Tx beam (or beam pair) IDs in the N beams in the second set. If the second set is actually measured, if the model needs to output the best beam corresponding to each port, L*N L1-RSRPs need to be measured.

[0296] It can be understood that the model 2 can predict the L1-RSRP corresponding to the N beams of each port or predict the ID and / or L1-RSRP of the best beam of each port by inputting the L1-RSRP corresponding to every other (or every two, every three, and so on) beam pair of each port.

[0297] For example, the model 3 is input (corresponding to the first set) and output (corresponding to the second set) as follows:

[0298] The first set can include: for L (for example, L is 10) ports, input the L1-RSRP of the best transmission beam (pair) in the first set corresponding to X (for example, X is 5) ports (for example, ports 1, 3, 5, 7, 9). The first set needs to obtain L*N / 2 L1-RSRP.

[0299] The second set can include: the ID of the best 1 / 2 / 4 transmission beams (Tx beams) or beam pair ID in the second set corresponding to at least one of the other (L-X) ports (for example, ports 2, 4, 6, 8, 10) or all L ports. If the second set is actually measured, if the model needs to output the best beam corresponding to other ports or all ports, L*N / 2 L1-RSRP or L*N L1-RSRP needs to be measured.

[0300] It can be understood that the model 3 can predict the L1-RSRP corresponding to the best beam of other ports or all ports or predict the ID and / or L1-RSRP of the best beam of other ports or all ports by inputting the L1-RSRP corresponding to the best beam pair of part of the ports.

[0301] For example, the model 4 is input (corresponding to the first set) and output (corresponding to the second set) as follows:

[0302] The first set can include: for L (for example, L is 10) ports, input the ID of the best transmission beam or beam pair in the first set corresponding to X (for example, X is 5) ports (for example, ports 1, 3, 5, 7, 9). The first set needs to obtain L*N / 2 L1-RSRP.

[0303] The second set can include: the ID of the best 1 / 2 / 4 transmission beams (Tx beams) or beam pair ID in the second set corresponding to at least one of the other (L-X) ports (for example, ports 2, 4, 6, 8, 10) or all L ports. If the second set is actually measured, if the model needs to output the best beam corresponding to other ports or all ports, L*N / 2 L1-RSRP or L*N L1-RSRP needs to be measured.

[0304] It can be understood that the model 4 can predict the IDs and / or L1-RSRPs of the optimal beams of other ports or all ports through the beam IDs or beam pair IDs corresponding to the optimal beams of the input partial ports.

[0305] For the above model, different ports can correspond to different reference signal resources. In this case, the L1-RSRPs corresponding to different transmission beams are the L1-RSRPs corresponding to different reference signal resources. If the reception beams are also considered, each L1-RSRP can correspond to a reference signal resource and a reception beam of the terminal. Alternatively, the reference signal resources corresponding to different ports are the same, i.e., one reference signal resource is a multi-port reference signal resource. In this case, the L1-RSRPs corresponding to different transmission beams are the L1-RSRPs corresponding to reference signal resources and port identifiers. If the reception beams are also considered, each L1-RSRP corresponds to a reference signal resource, a port identifier, and a reception beam of the terminal.

[0306] In some embodiments, for the above model, the network device can send configuration information of the reference signal resource set to the terminal, and the terminal performs measurement on the reference signal based on the configuration information to obtain the first set and the second set. The data reported by the terminal includes the input of the model (corresponding to the first set) and the output of the model (the second set), and the network device can train the AI model based on the data reported by the terminal. The data included in the input of the model includes the reporting quantity corresponding to the first set, and the data included in the output of the model includes the reporting quantity corresponding to the second set. The output of the model included in the data reported by the terminal can be used as a training label of the AI model.

[0307] For the first set, in order to determine the port corresponding to the reference signal resource, the configuration information of the reference signal resource set can correspond to the configuration of the port index corresponding to the reference signal resource. One reference signal resource can correspond to one or more port indexes.

[0308] In some embodiments, the reporting of one time instance above can include the reporting quantities corresponding to the first set and the second set.

[0309] In some embodiments, if it is spatial domain beam prediction, the periods of the first set and the second set are the same. One time instance can be for measurement within one period.

[0310] In some embodiments, if it is time domain beam prediction, the period of the second set can be less than or equal to the period of the first set. One time instance includes a sample of one time domain beam prediction, such as measurement results of the input of the first set including N times and the output of the second set including M times corresponding to the label.

[0311] The embodiment of the present disclosure provides a method for collecting data for network side AI model training of near-field beam prediction, and realizes data collection for AI model training.

[0312] In the embodiments of the present disclosure, part or all of the steps, and optional implementation manners thereof, can be combined with part or all of the steps in other embodiments, or can be combined with optional implementation manners of other embodiments.

[0313] The embodiments of the present disclosure also provide a device for implementing any of the above methods, for example, a device including units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another device is provided, including units or modules for implementing each step performed by a network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0314] It should be understood that the division of each unit or module in the above apparatus is only a logical function division, and all or part of them can be integrated into a physical entity or physically separated in actual implementation. In addition, the units or modules in the apparatus can be implemented in the form of processor calling software: for example, the apparatus includes a processor, the processor is connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to realize any of the above methods or realize the functions of each unit or module of the above apparatus, wherein the processor is a general processor such as a central processing unit (CPU) or a microprocessor, and the memory is a memory in the apparatus or a memory outside the apparatus. Alternatively, the units or modules in the apparatus can be implemented in the form of hardware circuit, and the functions of part or all of the units or modules can be realized by the design of hardware circuit. The above hardware circuit can be understood as one or more processors; for example, in one implementation, the above hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the units or modules are realized by the design of the logical relationship of elements in the circuit; for another example, in another implementation, the above hardware circuit is a programmable logic device (PLD), and a field programmable gate array (FPGA) is taken as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of part or all of the above units or modules. All units or modules of the above apparatus can be all implemented in the form of processor calling software, or all implemented in the form of hardware circuit, or part implemented in the form of processor calling software and the remaining part implemented in the form of hardware circuit.

[0315] In the embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), and the like. In another implementation, the processor can implement certain functions through a logical relationship of hardware circuits, and the logical relationship of the hardware circuits is fixed or can be reconfigured. 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 the reconfigurable hardware circuit, the processor loads a configuration document to implement the configuration of the hardware circuit. It can be understood that the processor loads instructions to implement the functions of the above part or all units or modules. In addition, the hardware circuit can also be 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), and the like.

[0316] FIG. 6A is a structural schematic diagram of a terminal according to an embodiment of the present disclosure. As shown in FIG. 6A, the terminal 6100 can include a transceiver module 6101. In some embodiments, the transceiver module 6101 is configured to send the first report to the network device. Optionally, the transceiver module is configured to perform at least one of the steps (for example, steps S2101 and S2103, but not limited to) in the processing performed by the network device in any of the above methods, and details are not repeated here.

[0317] FIG. 6B is a structural schematic diagram of a network device according to an embodiment of the present disclosure. As shown in FIG. 6B, the network device 6200 can include a transceiver module 6201. In some embodiments, the transceiver module 6201 is configured to receive the first report sent by the terminal. Optionally, the transceiver module is configured to perform at least one of the steps (for example, steps S2101 and S2103, but not limited to) in the processing performed by the terminal in any of the above methods, and details are not repeated here.

[0318] In some embodiments, the processing module can be one module, or can include multiple sub-modules. Optionally, the multiple sub-modules perform all or part of the steps required to be performed by the processing module, respectively. Optionally, the processing module can be mutually replaced with the processor.

[0319] FIG. 7A is a structural schematic diagram of a communication device 7100 according to an embodiment of the present disclosure. The communication device 7100 can be a network device (for example, an access network device, a core network device, etc.), a terminal (for example, a user equipment, etc.), a chip, a chip system, or a processor supporting the network device to implement any of the above methods, or a chip, a chip system, or a processor supporting the terminal to implement any of the above methods. The communication device 7100 can be used to implement the methods described in the above method embodiments, and details can be referred to the descriptions in the above method embodiments.

[0320] As shown in FIG. 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a special-purpose 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 (for example, a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process data of the programs. Optionally, the communication device 7100 is used to execute any of the above methods. Optionally, the one or more processors 7101 are used to call instructions to enable the communication device 7100 to execute any of the above methods.

[0321] In some embodiments, the communication device 7100 further includes one or more transceivers 7102. When the communication device 7100 includes one or more transceivers 7102, the transceiver 7102 performs at least one of the communication steps (for example, steps S2101 and S2103, but not limited thereto) of the above methods, and the processor 7101 performs at least one of the other steps (for example, step S2102, but not limited thereto). In an optional embodiment, the transceiver can include a receiver and / or a transmitter, which can be separate or integrated together. Optionally, the terms of transceiver, transceiving unit, transceiver, transceiving circuit, interface circuit, interface, etc. can be mutually replaced, and the terms of transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be mutually replaced, and the terms of receiver, receiving unit, receiver, receiving circuit, etc. can be mutually replaced.

[0322] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Alternatively, all or part of the memories 7103 can be external to the communication device 7100. In optional embodiments, the communication device 7100 can include one or more interface circuits 7104. Optionally, the interface circuit 7104 is connected to the memory 7103, and the interface circuit 7104 can be used to receive data from the memory 7103 or other devices, and can be used to send data to the memory 7103 or other devices. For example, the interface circuit 7104 can read data stored in the memory 7103 and send the data to the processor 7101.

[0323] The communication device 7100 described in the above embodiments can be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 can not be limited by Figure 7A. The communication device can be a standalone device or can be part of a larger device. For example, the communication device can be: 1) a standalone integrated circuit (IC), or a chip, or a chip system or subsystem; (2) a set of one or more ICs, which can optionally include a storage component for storing data, programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, a smart terminal device, a cellular phone, a wireless device, a handset, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) other devices, etc.

[0324] Figure 7B is a structural schematic diagram of a chip 7200 according to an embodiment of the present disclosure. For the case where the communication device 7100 is a chip or a chip system, the structural schematic diagram of the chip 7200 shown in Figure 7B can be referred to, but is not limited thereto.

[0325] The chip 7200 includes one or more processors 7201. The chip 7200 is configured to execute any of the above methods.

[0326] In some embodiments, the chip 7200 further includes one or more interface circuits 7202. Optionally, the terms interface circuit, interface, transceiver pin, etc. can be replaced by each other. In some embodiments, the chip 7200 further includes one or more memories 7203 for storing data. Optionally, all or part of the memories 7203 can be external to the chip 7200. Optionally, the interface circuit 7202 is connected to the memory 7203, and the interface circuit 7202 can be used to receive data from the memory 7203 or other devices, and the interface circuit 7202 can be used to send data to the memory 7203 or other devices. For example, the interface circuit 7202 can read data stored in the memory 7203 and send the data to the processor 7201.

[0327] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (for example, step S2101, step S2103, but not limited thereto) of transmitting and / or receiving and / or the like in the above-described method. The interface circuit 7202 performing the communication steps of transmitting and / or receiving and / or the like in the above-described method refers to, for example, the interface circuit 7202 performing data interaction between the processor 7201, the chip 7200, the memory 7203, or a transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (for example, step S2102, but not limited thereto).

[0328] The various modules and / or devices described in each of the embodiments of the virtual device, the physical device, the chip, and / or the like can be combined or separated as the case can be. Alternatively, part or all of the steps can also be performed by a plurality of modules and / or devices in cooperation, which is not limited here.

[0329] The disclosure also proposes a storage medium, and the above-mentioned storage medium stores instructions, which, when executed on the communication device 7100, cause the communication device 7100 to perform any one of the above methods. Alternatively, the above-mentioned storage medium is an electronic storage medium. Alternatively, the above-mentioned storage medium is a computer-readable storage medium, but is not limited thereto, and it can also be a storage medium readable by other devices. Alternatively, the above-mentioned storage medium can be a non-transitory storage medium, but is not limited thereto, and it can also be a transitory storage medium.

[0330] The disclosure also proposes a program product, and the above-mentioned program product is executed by the communication device 7100, so that the communication device 7100 performs any one of the above methods. Alternatively, the above-mentioned program product is a computer program product.

[0331] The disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A communication method characterized by comprising: The method comprises: The terminal sends a first report to the network device, the first report comprising a report quantity of at least one reference signal resource corresponding to at least one port.

2. The method of claim 1, wherein, The first report comprises a report quantity corresponding to at least one of a first set and a second set; The first set comprises M reference signal resources corresponding to each of L ports; The second set comprises N reference signal resources corresponding to each of L ports; Wherein, L, M and N are positive integers, and M is less than or equal to N.

3. The method of claim 1, wherein, The first report comprises a report quantity corresponding to at least one of a first set and a second set; The first set comprises M reference signal resources corresponding to X ports of L ports; The second set comprises N reference signal resources corresponding to each of L ports; Wherein, L, M and N are positive integers, M is less than or equal to N, and X is less than or equal to L.

4. The method of claim 1, wherein, The first report comprises a report quantity corresponding to at least one of a first set and a second set; The first set comprises at least one best reference signal resource corresponding to each of X ports of L ports; The second set comprises N reference signal resources corresponding to each of L ports except the X ports; Wherein, L, N and X are positive integers, and X is less than or equal to L.

5. The method of claim 4, wherein, The report quantity corresponding to the first set comprises L1-RSRP of at least one best reference signal resource corresponding to each of X ports of L ports.

6. The method of claim 4, wherein, The report quantity corresponding to the first set comprises an identifier of at least one best reference signal resource corresponding to each of X ports of L ports.

7. The method according to any one of claims 2 to 6, characterized in that, The M reference signal resources corresponding to different ports are different.

8. The method according to any one of claims 2 to 6, characterized in that, The time period corresponding to the first set is greater than or equal to the time period corresponding to the second set.

9. The method according to any one of claims 2 to 6, characterized in that, The first report is used in an artificial intelligence AI model training process, the first set is used to determine an input of the training process, and the second set is used to determine a label of the training process.

10. The method according to any one of claims 1 to 9, characterized in that, The method further comprises: The terminal receives configuration information of a reference signal resource set sent by the network device, and the configuration information is used by the terminal to determine the report quantity.

11. The method of claim 10, wherein, The report quantity comprises at least one of: A synchronization signal block SSB index; A channel state information reference signal resource index CRI; A port index; L1-RSRP.

12. The method of claim 10, wherein, The configuration information comprises port information corresponding to a reference signal resource.

13. The method according to any one of claims 1 to 12, characterized in that, The terminal is a near-field terminal.

14. A communication method, comprising: The method comprises: The network device receives a first report sent by a terminal, the first report comprising a report quantity of at least one reference signal resource corresponding to at least one port.

15. The method of claim 14, wherein, The first report comprises a report quantity corresponding to at least one of a first set and a second set; The first set comprises M reference signal resources corresponding to each of L ports; The second set comprises N reference signal resources corresponding to each of L ports; Wherein, L, M and N are positive integers, and M is less than or equal to N.

16. The method of claim 14, wherein, The first report comprises a report quantity corresponding to at least one of a first set and a second set; The first set comprises M reference signal resources corresponding to X ports of L ports; The second set comprises N reference signal resources corresponding to each of L ports; Wherein, L, M and N are positive integers, M is less than or equal to N, and X is less than or equal to L. The second set includes N reference signal resources corresponding to each of the L ports. L, M, and X are positive integers, and M is less than or equal to N.

17. The method of claim 14, wherein, The first report includes a report quantity corresponding to at least one of the first set and the second set. The first set includes at least one best reference signal resource corresponding to each of X ports of the L ports. The second set includes N reference signal resources corresponding to each of the L ports other than the X ports. L, N, and X are positive integers, and X is less than or equal to L.

18. The method of claim 17, wherein, The report quantity corresponding to the first set includes an L1-RSRP of at least one best reference signal resource corresponding to each of the X ports of the L ports.

19. The method of claim 17, wherein, The report quantity corresponding to the first set includes an identifier of at least one best reference signal resource corresponding to each of the X ports of the L ports.

20. The method according to any one of claims 15 to 18, characterized in that, The M reference signal resources corresponding to different ports are different.

21. The method according to any one of claims 15 to 18, characterized in that, The time period corresponding to the first set is greater than or equal to the time period corresponding to the second set.

22. The method according to any one of claims 15 to 18, characterized in that, The first report is used in a training process of an artificial intelligence (AI) model, the first set is used to determine an input of the training process, and the second set is used to determine a label of the training process.

23. The method according to any one of claims 14 to 22, characterized in that, The method further includes: The network device sends configuration information of a reference signal resource set to the terminal, and the configuration information is used by the terminal to determine the report quantity.

24. The method of claim 23, wherein, The report quantity includes at least one of the following: a synchronization signal block (SSB) index; a channel state information reference signal resource index (CRI); a port index; an L1-RSRP.

25. The method of claim 24, wherein, The configuration information includes port information corresponding to a reference signal resource.

26. The method of any one of claims 14 to 25, wherein, The terminal is a near-field terminal.

27. A terminal, characterized by Comprises: a transceiver module configured to send a first report to a network device, the first report including a report quantity of at least one reference signal resource corresponding to at least one port.

28. A network device, comprising: Comprises: a transceiver module configured to receive a first report sent by a terminal, the first report including a report quantity of at least one reference signal resource corresponding to at least one port.

29. A terminal, characterized by Comprises: one or more processors; The terminal is configured to perform the method of any one of claims 1-13.

30. A network device, comprising: Comprises: one or more processors; The network device is configured to perform the method of any one of claims 14-26.

31. A communication system, characterized by The terminal and the network device are configured to perform the method of any one of claims 1-14 and the method of any one of claims 14-26.

32. A storage medium, the storage medium storing instructions, wherein, When the instructions are run on a communication device, the communication device is caused to perform the method of any one of claims 1-13 or the method of any one of claims 14-26.

33. A program product, characterized by Comprises: a computer program that, when executed by a communication device, causes the communication device to perform the method of any one of claims 1-13 or the method of any one of claims 14-26.

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