Channel prediction method and apparatus

Abstract

Disclosed in the embodiments of the present disclosure are a channel prediction method and apparatus. The method comprises: inputting a random noise vector into a first model to obtain output first channel data; inputting the first channel data into a second model to obtain output second channel data; and if it is determined that a first condition is met, determining the second channel data as a channel prediction result, such that the models as a whole can better learn channel sequence features, can significantly eliminate redundant information in channel data, and can generate a large amount of accurate channel data on the basis of actual measurement results, thereby implementing spatial-domain channel prediction.

Description

Channel prediction method and device TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a channel prediction method and device. BACKGROUND

[0002] With the development and maturity of the 5th generation mobile communication technology (5G), the industry begins to research the 6th generation mobile communication technology (6G).

[0003] In recent years, the artificial intelligence (AI) technology is continuously developed, applied in many fields, and will empower various fields of social life in the future. An important innovation of 6G technology is to integrate artificial intelligence (AI) technology and communication technology.

[0004] SUMMARY

[0005] The present disclosure provides a channel prediction method and device.

[0006] The first aspect of the present disclosure provides a channel prediction method, and the method comprises the following steps:

[0007] inputting a random noise vector into a first model to obtain output first channel data;

[0008] inputting the first channel data into a second model to obtain output second channel data;

[0009] determining that a first condition is met, and determining the second channel data as a result of channel prediction.

[0010] The second aspect of the present disclosure provides a channel prediction method, and the method comprises the following steps:

[0011] inputting a random noise vector into a first model to obtain output first channel data;

[0012] inputting the first channel data into a second model to obtain output second channel data;

[0013] determining that a first condition is met, and determining the second channel data as a result of channel prediction.

[0014] The third aspect of the present disclosure provides a terminal, and the terminal comprises:

[0015] The processing module is configured to input the random noise vector into the first model to obtain first channel data as output.

[0016] The processing module is further configured to input the first channel data into the second model to obtain second channel data as output.

[0017] The processing module is further configured to determine that a first condition is met, and determine the second channel data as a result of channel prediction.

[0018] The fourth aspect of the present disclosure provides a network device, which comprises:

[0019] The processing module is configured to input the random noise vector into the first model to obtain first channel data as output.

[0020] The processing module is further configured to input the first channel data into the second model to obtain second channel data as output.

[0021] The processing module is further configured to determine that a first condition is met, and determine the second channel data as a result of channel prediction.

[0022] The scheme provided by the embodiments of the present disclosure can make the model as a whole better learn the channel sequence characteristics, and can greatly eliminate the redundant information in the channel data, can generate a large number of accurate channel data based on the real measurement results, and can realize the spatial channel prediction. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the background art, the drawings needed to be used in the embodiments of the present disclosure or the background art will be described below.

[0024] FIG. 1 is a schematic diagram of the architecture of a communication system provided by an embodiment of the present disclosure;

[0025] FIG. 2A is an interaction schematic diagram of a channel prediction method provided by an embodiment of the present disclosure;

[0026] FIG. 2B is a schematic diagram of a model structure provided by an embodiment of the present disclosure;

[0027] FIG. 2C is a schematic diagram of a channel prediction provided by an embodiment of the present disclosure;

[0028] FIGS. 3A-3B are flow schematic diagrams of a channel prediction method provided by an embodiment of the present disclosure;

[0029] FIGS. 4A-4B are flow diagrams of a channel prediction method according to an embodiment of the present disclosure;

[0030] FIG. 5 is a schematic diagram of an overall architecture of a channel prediction method according to an embodiment of the present disclosure;

[0031] FIG. 6A is a schematic diagram of a structure of a terminal according to an embodiment of the present disclosure;

[0032] FIG. 6B is a schematic diagram of a structure of a network device according to an embodiment of the present disclosure;

[0033] FIG. 7A is a schematic diagram of a structure of a communication device according to an embodiment of the present disclosure;

[0034] FIG. 7B is a schematic diagram of a structure of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] Embodiments of the present disclosure provide a channel prediction method and device.

[0036] In a first aspect, embodiments of the present disclosure provide a channel prediction method, which includes:

[0037] inputting a random noise vector into a first model to obtain output first channel data;

[0038] inputting the first channel data into a second model to obtain output second channel data;

[0039] determining that a first condition is met, and determining that the second channel data is a result of channel prediction.

[0040] In the above embodiments, the model as a whole can better learn the channel sequence features, can greatly eliminate the redundant information in the channel data, can generate a large amount of accurate channel data based on real measurement results, and can achieve spatial channel prediction.

[0041] In combination with some embodiments of the first aspect, in some embodiments, the method further includes:

[0042] obtaining third channel data, inputting the third channel data and the second channel data into a third model to obtain a first output value;

[0043] the first condition is that the first output value converges;

[0044] The third channel data is real data obtained based on channel measurement, and the third model is used to determine the difference between the second channel data and the third channel data.

[0045] In some embodiments of the first aspect, the first output value is a ratio between a distribution of the third channel data and a first sum, wherein the first sum is a sum of a distribution of the second channel data and the distribution of the third channel data.

[0046] In some embodiments of the first aspect, the second model and the third model are iteratively trained alternately.

[0047] In some embodiments of the first aspect, the first model is a long short-term memory network model, and the second model and the third model are generative adversarial network models, wherein the second model is a generative model in the generative adversarial network models, and the third model is a discriminative model in the generative adversarial network models.

[0048] In a second aspect, the embodiments of the present disclosure provide a channel prediction method, and the method comprises:

[0049] inputting a random noise vector into the first model to obtain first channel data as an output;

[0050] inputting the first channel data into the second model to obtain second channel data as an output;

[0051] determining that the second channel data is the result of channel prediction when a first condition is met.

[0052] In the above embodiments, the model as a whole can better learn the characteristics of the channel sequence, can greatly eliminate the redundant information in the channel data, can generate a large amount of accurate channel data based on the real measurement results, and can realize the spatial channel prediction.

[0053] In some embodiments of the second aspect, the method further comprises:

[0054] obtaining third channel data, inputting the third channel data and the second channel data into a third model to obtain a first output value;

[0055] the first condition is that the first output value converges;

[0056] The third channel data is real data obtained based on channel measurement, and the third model is used to determine the difference between the second channel data and the third channel data.

[0057] In some embodiments of the second aspect, the first output value is a ratio between a distribution of the third channel data and a first sum, wherein the first sum is a sum of a distribution of the second channel data and the distribution of the third channel data.

[0058] In some embodiments of the second aspect, the second model and the third model are alternately iteratively trained.

[0059] In some embodiments of the second aspect, the first model is a long short-term memory network model, and the second model and the third model are generative adversarial network models, wherein the second model is a generative model in the generative adversarial network model, and the third model is a discriminative model in the generative adversarial network model.

[0060] In a third aspect, the embodiments of the present disclosure provide a channel prediction method, and the method comprises:

[0061] inputting a random noise vector into the first model to obtain output first channel data;

[0062] inputting the first channel data into the second model to obtain output second channel data;

[0063] determining that the first condition is met, and determining the second channel data as a result of channel prediction.

[0064] In the above embodiments, the model as a whole can better learn the channel sequence features, can greatly eliminate the redundant information in the channel data, can generate a large amount of accurate channel data based on the real measurement results, and can realize the spatial channel prediction.

[0065] In a fourth aspect, the embodiments of the present disclosure provide a terminal, and the terminal comprises a transceiver module and a processing module; wherein the terminal is configured to perform the first aspect and the optional implementation manners of the first aspect.

[0066] In a fifth aspect, the embodiments of the present disclosure provide a network device, and the network device comprises a transceiver module and a processing module; wherein the network device is configured to perform the second aspect and the optional implementation manners of the second aspect.

[0067] In a sixth aspect, the embodiments of the present disclosure provide a terminal, and the terminal comprises one or more processors; wherein the terminal is configured to perform the first aspect and the optional implementation manners of the first aspect.

[0068] In a seventh aspect, the embodiments of the present disclosure provide a network device, and the network device comprises one or more processors; wherein the network device is configured to perform the second aspect and the optional implementation manners of the second aspect.

[0069] In an eighth aspect, the embodiments of the present disclosure provide a communication system, the communication system comprising: a terminal and a network device; wherein the terminal is configured to perform the method described in the first aspect and the optional implementation manners of the first aspect, and the network device is configured to perform the method described in the second aspect and the optional implementation manners of the second aspect.

[0070] In a ninth aspect, the embodiments of the present disclosure provide a storage medium, the storage medium storing instructions which, when executed on a communication device, cause the communication device to perform the method described in the first aspect and the optional implementation manners of the first aspect.

[0071] In a tenth aspect, the embodiments of the present disclosure provide a program product, the program product, when executed by a communication device, causing the communication device to perform the method described in the first aspect and the optional implementation manners of the first aspect, the second aspect and the optional implementation manners of the second aspect.

[0072] In an eleventh aspect, the embodiments of the present disclosure provide a computer program, when executed on a computer, causing the computer to perform the method described in the first aspect and the optional implementation manners of the first aspect, the second aspect and the optional implementation manners of the second aspect.

[0073] In a twelfth aspect, the embodiments of the present disclosure provide a chip or a chip system. The chip or the chip system comprises processing circuitry configured to perform the method described in the first aspect and the optional implementation manners of the first aspect, the second aspect and the optional implementation manners of the second aspect.

[0074] It can be understood that the terminal, the network device, the communication system, the storage medium, the program product, the computer program, the chip or the chip system are all used to perform the method proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be described here.

[0075] The embodiments of the present disclosure provide a channel prediction method and device. In some embodiments, the terms of channel prediction method, information processing method and communication method can be replaced with each other, the terms of channel prediction device, information processing device and communication device can be replaced with each other, and the terms of information processing system and communication system can be replaced with each other.

[0076] 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 part of the 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, part or all steps of different embodiments can be combined arbitrarily, an embodiment can be combined with optional implementation manners of other embodiments arbitrarily.

[0077] In each embodiment of the present disclosure, the terms and / or descriptions between the embodiments are consistent if there is no special description and logical conflict, and can be referred to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0078] 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.

[0079] 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", and can also represent "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, and can also be understood as plural expression.

[0080] In the embodiments of the present disclosure, "a plurality of" means two or more.

[0081] In some embodiments, the terms "at least one of", "one or more", "a plurality of", "multiple" and the like can be replaced with each other.

[0082] In some embodiments, the description of "at least one of A, B", "A and / or B", "in a 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 selectively executed (A and B are selected from A and B); in some embodiments, A and B (A and B are executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0083] In some embodiments, the description of "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 selectively executed (A and B are selected from A and B). When there are more branches such as A, B, C, and the like, the above is similar.

[0084] In the embodiments of the present disclosure, the prefix words "first", "second", and the like, are only 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 object is "field", and the ordinal words before "field" in "first field" and "second field" do not limit the position or order between "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of "first field" and "second field". For another example, the description object is "level", and the ordinal words before "level" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description object is not limited by the ordinal words, and can be one or more. For example, "first device", where the quantity of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description object is "device", 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 object is "information", and "first information" and "second information" can be the same information or different information, and their contents can be the same or different.

[0085] In some embodiments, "including A", "containing A", "for indicating A", "carrying A", can be interpreted as directly carrying A, or indirectly indicating A.

[0086] In some embodiments, the terms "time / frequency", "time / frequency domain", and the like, refer to the time domain and / or the frequency domain.

[0087] In some embodiments, the terms "in response to", "in response to determining", "in the case of", "when", "if", "if" and the like can be replaced with each other.

[0088] 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 lower 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.

[0089] In some embodiments, the apparatuses and devices can be interpreted as physical or virtual, and their names are not limited to the names described in the embodiments, and in some cases can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and the like.

[0090] In some embodiments, "network" can be interpreted as an apparatus included in the network, such as an access network device, a core network device, and the like.

[0091] In some embodiments, an “access network device (AN device)” can also be referred to as a “radio access network device (RAN device),” a “base station (BS),” a “radio base station,” a “fixed station,” and in some embodiments can also be understood as a “node,” an “access point,” a “transmission point (TP),” a “reception point (RP),” a “transmission / reception point (TRP),” a “panel,” an “antenna panel,” an “antenna array,” a “cell,” a “macro cell,” a “small cell,” a “femto cell,” a “pico cell,” a “sector,” a “cell group,” a “serving cell,” a “carrier,” a “component carrier,” a “bandwidth part (BWP),” and the like.

[0092] In some embodiments, a "terminal" or "terminal device" can be referred to as a "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0093] In some embodiments, an access network device, a core network device, or a 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 an access network device, a core network device, or a network device and a 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", etc. can also be replaced with terms corresponding to the inter-terminal communication (e.g., "side"). For example, an uplink channel, a downlink channel, etc. can be replaced with a side channel, and an uplink, a downlink, etc. can be replaced with a side link.

[0094] In some embodiments, a terminal can be replaced with an access network device, a core network device, or a 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.

[0095] In some embodiments, obtaining data, information, and the like can comply with laws and regulations of the country where the location is.

[0096] In some embodiments, data, information, and the like can be obtained after obtaining the consent of the user.

[0097] 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.

[0098] FIG. 1 is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0099] As shown in FIG. 1, the communication system 100 includes a terminal 101 and a network device 102.

[0100] In some embodiments, the terminal 101 includes at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a 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, a RedCap terminal, and the like, but is not limited thereto.

[0101] In some embodiments, the network device 102 is, for example, a node or device that accesses a terminal to a wireless network, and the network device can include at least one of a satellite or a drone in a channel prediction network, an evolved NodeB (eNB) in a 5G communication system, a next generation eNB (ng-eNB), a next generation NodeB (gNB), a next generation RAN node (NG-RAN node), 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 RAN, a Cloud RAN, a base station in other communication systems, an access node in a Wi-Fi system, but is not limited thereto.

[0102] 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.

[0103] In some embodiments, the 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), and the CU-DU structure can split the protocol layers of the access network device, and some of the protocol layers are controlled by the CU, and the remaining or all of the protocol layers are distributed in the DU and controlled by the CU, but are not limited thereto.

[0104] 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 in the embodiments of the present disclosure, and 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 in the embodiments of the present disclosure are also applicable to similar technical problems.

[0105] The following embodiments of the present disclosure can be applied to the communication system 100 shown in FIG. 1 or part of the subjects, but are not limited thereto. The subjects shown in FIG. 1 are illustrative, and the communication system can include all or part of the subjects in FIG. 1, or other subjects other than FIG. 1, the number and form of the subjects are arbitrary, each subject can be real or virtual, the connection relationship between the subjects is illustrative, the subjects 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.

[0106] Embodiments of the present disclosure can be applied to a Non-terrestrial Network (NTN), 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. Further, a plurality of systems can be applied in combination (for example, combination of LTE or LTE-A and 5G, and the like).

[0107] With the development and maturity of the 5th generation mobile communication technology (5G), the industry has begun to study the 6th generation mobile communication technology (6G).

[0108] In recent years, Artificial Intelligence (AI) technology has been developing rapidly and has been applied in many fields, and will empower various fields of social life in the future. One of the important innovations of 6G technology is to integrate Artificial Intelligence (AI) technology and communication technology.

[0109] However, the development of 6G technology brings many new problems and challenges to traditional wireless channel modeling. The global coverage, full spectrum, full application and strong security in the 6G vision lead to complex channel measurement activities, time-consuming channel parameter processing and more general channel model expression. The complex and diverse scenarios require expensive high-performance channel sounding instruments, and it is impossible to perform full-channel measurement in all frequency bands or scenarios. In addition, the use of high-resolution parameter estimation algorithms is required to process a large amount of measurement data, which brings extremely high complexity. The non-predictive channel model cannot predict the channel in the future time, unknown frequency band or scenario.

[0110] Therefore, a new model is needed to predict the channel.

[0111] The channel prediction method and device provided by the present disclosure will be described in detail below in conjunction with the accompanying drawings.

[0112] FIG. 2A is an interaction diagram of a channel prediction method according to an embodiment of the present disclosure. As shown in FIG. 2A, the present embodiment relates to a channel prediction method, and the method comprises:

[0113] In step S2101, the terminal 101 or the network device 102 obtains first channel data based on a first model.

[0114] In some embodiments, the steps in the present embodiment can be performed by the terminal 101 or the network device 102.

[0115] In some embodiments, the terminal 101 can input a random noise vector into the first model to obtain the output first channel data.

[0116] In some embodiments, the network device 102 can input a random noise vector into the first model to obtain the output first channel data.

[0117] In some embodiments, the first model described above is used to learn long-term dependencies in sequence-related modeling.

[0118] In some embodiments, the first model described above is a Long Short-Term Memory (LSTM) network model.

[0119] In some embodiments, the first model described above is composed of at least two LSTM blocks.

[0120] In some embodiments, the structure of the first model described above can be as shown in FIG. 2B.

[0121] It should be noted that FIG. 2B only illustrates the structure of LSTM and does not constitute a specific limitation on the first model.

[0122] Optionally, as an example, each repeating module in the LSTM can include four interacting layers, three sigmoid (σ) and one tanh layer.

[0123] In some embodiments, channel prediction can be represented as a time step sequence prediction problem, which predicts the next channel state in the spatial domain using the first j states obtained by continuous measurement.

[0124] As an example, the LSTM-based spatial domain channel prediction can be as shown in FIG. 2C. The output h t of the first model contains time step update information obtained from the sequence h t , which can be represented as:

[0125] h t = f LSTM (h(n-j+1),h(n-j+2),…,h(n)),

[0126] wherein,

[0127] Step S2102, the terminal 101 or the network device 102 obtains second channel data based on the second model.

[0128] In some embodiments, the steps in the embodiments of the present application can be performed by the terminal 101 or the network device 102.

[0129] In some embodiments, the terminal 101 can input the first channel data described above into the second model to obtain the output second channel data.

[0130] In some embodiments, the network device 102 can input the first channel data described above into the second model to obtain the output second channel data.

[0131] In some embodiments, the second model described above is used to generate channel data.

[0132] In some embodiments, the second model described above is used for channel prediction.

[0133] In some embodiments, the trained second model described above is used to obtain the result of channel prediction.

[0134] In some embodiments, the second model described above is a generative model (G) in a generative adversarial network (GAN) model.

[0135] In some embodiments, the input of the second model described above is the output of the first model described above (i.e., the first channel data).

[0136] As an example, the output of the second model described above (i.e., the second channel data) can be represented as G(·).

[0137] In some embodiments, the second model can use three layers of convolution (Conv) layers as the main structure because they have good recognition performance for high-dimensional matrices and complex features. The Batch Normalization (BN) layer added after the Conv layer normalizes the output of each layer node and enhances the generalization and robustness of the model. The Parametric Rectified Linear Unit (PReLU) activation function is used for reprocessing to improve the accuracy and efficiency of training.

[0138] In step S2103, the terminal 101 or the network device 102 obtains third channel data.

[0139] In some embodiments, the steps in the embodiments of the present application can be performed by the terminal 101 or the network device 102.

[0140] In some embodiments, the terminal 101 is capable of obtaining the third channel data, wherein the third channel data described above is real data obtained based on channel measurement.

[0141] In some embodiments, the network device 102 is capable of obtaining the third channel data, wherein the third channel data described above is real data obtained based on channel measurement.

[0142] In some embodiments, the third channel data described above is used for the third model to determine the difference between the second channel data generated by the second model described above and the real channel data.

[0143] In some embodiments, the third channel data can include at least one of the following information: channel delay information, received power information, frequency band information where the channel is located, scene information where the channel is located, physical environment information, etc.

[0144] In some embodiments, the third channel data can be used for feature extraction and learning training of the model in the embodiments of the present application.

[0145] In step S2104, the terminal 101 or the network device 102 obtains the first output value.

[0146] In some embodiments, the steps in the embodiments of the present application can be performed by the terminal 101 or the network device 102.

[0147] In some embodiments, the terminal 101 can obtain the first output value based on the third channel data, the second channel data, and the third model.

[0148] In some embodiments, the network device 102 can obtain the first output value based on the third channel data, the second channel data, and the third model.

[0149] In some embodiments, the terminal 101 can input the third channel data and the second channel data into the third model to obtain the output first output value.

[0150] In some embodiments, the network device 102 can input the third channel data and the second channel data into the third model to obtain the output first output value.

[0151] In some embodiments, the third model is a discriminative model (D) in a generative adversarial network (GAN) model.

[0152] In some embodiments, the third model is used to judge the difference between the second channel data and the third channel data.

[0153] In some embodiments, the first output value can be used to represent the difference between the second channel data and the third channel data.

[0154] In some embodiments, the first output value is the ratio of the distribution of the third channel data to the first sum, where the first sum is the sum of the distribution of the second channel data and the distribution of the third channel data.

[0155] As an example, the distribution of the third channel data can be represented as P data (x), and the distribution of the second channel data can be represented as P G(x) represents, the output result of the third model (i.e. the first output value) can be represented as:

[0156] In some embodiments, the third model uses four convolutional Conv layers and one fully connected (FC) layer. In addition to the BN layer and PReLU used before, a Dropout layer is added in the third model, which can improve the anti-overfitting ability of the neural network and enhance its generality. In order to improve the learning efficiency, an additional FC layer is added.

[0157] In step S2105, the terminal 101 or the network device 102 determines the second channel data as the result of channel prediction.

[0158] In some embodiments, the steps in the embodiments of the present application can be performed by the terminal 101 or the network device 102.

[0159] In some embodiments, the terminal 101 determines that the first condition is met, and can determine the second channel data as the result of channel prediction.

[0160] In some embodiments, the network device 102 determines that the first condition is met, and can determine the second channel data as the result of channel prediction.

[0161] In some embodiments, the first condition is that the first output value converges.

[0162] In some embodiments, first, the measured third channel data is sent to the third model for feature extraction. Then, the second channel data generated by the second model is learned for the first time by using the third model, and compared with the real third channel data. When the second channel data generated by the second model is input into the third model again, these networks need to be reused in multiple training iterations, and the weights of these layers are dynamically adjusted to minimize the discriminant loss value. With the increase of the number of training iterations, the discriminant ability of the third model will tend to a convergence value, reaching the optimal is the optimal value of D.

[0163] It can be understood that when P G (x) approaches P data (x), will approach 0.5.

[0164] In some embodiments, the second model and the third model are alternately iteratively trained.

[0165] In some embodiments, the alternating iterative training can include: first fixing the third model, training the second model, inputting the channel data generated by the second model into the third model for judgment, obtaining the difference between the generated channel data and the real channel data, and through iterative training of the second model, the result output by the third model is close to 0.5. Further, fix the second model, and iteratively train the third model so that the third model can identify the generated channel data and the real channel data. Further, the second model is iteratively trained again, and the above process is repeatedly cycled, and the second model and the third model are alternately iteratively trained, so that the second channel data generated by the second model and the real third channel data have perfect comparability. At this point, the GAN model including the above-mentioned second model and the third model can effectively learn and predict the channel characteristics.

[0166] In some embodiments, the terms "carrier", "band", "frequency", and the like can be replaced with each other.

[0167] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and the terms "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "code point", "bit", "data", "program", "chip", and the like can be replaced with each other.

[0168] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", "chip", and the like can be replaced with each other.

[0169] In some embodiments, terms such as "physical downlink shared channel (PDSCH)", "DL data", and the like can be replaced with each other, and terms such as "physical uplink shared channel (PUSCH)", "UL data", and the like can be replaced with each other.

[0170] In some embodiments, terms such as "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based", and the like can be replaced with each other.

[0171] In some embodiments, terms such as "resource block (RB)", "physical resource block (PRB)", "sub-carrier group (SCG)", "resource element group (REG)", "PRB pair", "RB pair", "resource element (RE)", "sub-carrier", and the like can be replaced with each other.

[0172] In some embodiments, "acquire", "obtain", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be replaced by each other, which can be interpreted as receiving from other subjects, acquiring from protocols, acquiring from higher layers, obtaining by self-processing, implementing autonomously, and the like.

[0173] In some embodiments, the terms "send", "transmit", "report", "issue", "transmit", "bidirectional transmission", "send and / or receive" can be replaced by each other.

[0174] In some embodiments, the terms "certain", "preset", "preset", "set", "indicated", "certain", "arbitrary", "first" and the like can be replaced by each other. "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 protocols and the like, A obtained by setting, configuration, or indication, and the like, A specific, certain, arbitrary, or first A, and the like, but not limited thereto.

[0175] The communication method related to the embodiments of the present disclosure can include at least one of steps S2101-S2105. For example, step 2101 can be implemented as an independent embodiment, steps 2101+2102 can be implemented as an independent embodiment, step 2102 can be implemented as an independent embodiment, step 2103 can be implemented as an independent embodiment, step 2104 can be implemented as an independent embodiment, steps 2102+2103+2104 can be implemented as an independent embodiment, steps 2101+2102+2103+2104 can be implemented as an independent embodiment, step 2105 can be implemented as an independent embodiment, steps 2101+2102+2105 can be implemented as an independent embodiment, steps 2101+2102+2103+2104+2105 can be implemented as an independent embodiment, and the like, but not limited thereto.

[0176] In some embodiments, steps S2101, S2102, S2103 can be exchanged in order or executed simultaneously.

[0177] In some embodiments, other optional implementations described before or after the corresponding description of FIG. 2A can be referred to.

[0178] FIG. 3A is a flow diagram of a channel prediction method according to an embodiment of the present disclosure. As shown in FIG. 3A, the embodiments of the present disclosure relate to a channel prediction method, and the above method is executed by the terminal 101, and the above method comprises:

[0179] At step S3101, first channel data is acquired based on a first model.

[0180] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0181] At step S3102, second channel data is acquired based on a second model.

[0182] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0183] At step S3103, third channel data is acquired.

[0184] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0185] At step S3104, a first output value is acquired.

[0186] The optional implementation of step S3104 can refer to the optional implementation of step S2104 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0187] At step S3105, the second channel data is determined as a result of channel prediction.

[0188] The optional implementation of step S3105 can refer to the optional implementation of step S2105 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0189] The communication method involved in the embodiments of the present disclosure can include at least one of steps S3101-S3105. For example, step 3101 can be implemented as an independent embodiment, steps 3101+3102 can be implemented as an independent embodiment, step 3102 can be implemented as an independent embodiment, step 3103 can be implemented as an independent embodiment, step 3104 can be implemented as an independent embodiment, steps 3102+3103+3104 can be implemented as an independent embodiment, steps 3101+3102+3103+3104 can be implemented as an independent embodiment, step 3105 can be implemented as an independent embodiment, steps 3101+3102+3105 can be implemented as an independent embodiment, steps 3101+3102+3103+3104+3105 can be implemented as an independent embodiment, and the like, but are not limited thereto.

[0190] In some embodiments, steps S3101, S3102, S3103 can be exchanged in order or executed simultaneously.

[0191] FIG. 3B is a flow diagram of a channel prediction method according to an embodiment of the present disclosure. As shown in FIG. 3B, the embodiment of the present disclosure relates to a channel prediction method, the method is performed by the terminal 101, and the method comprises:

[0192] In step S3201, first channel data is obtained based on a first model.

[0193] The optional implementation of step S3201 can refer to the optional implementation of step S2101 in FIG. 2A, the optional implementation of step S3101 in FIG. 3A, and other associated parts in the embodiments related to FIG. 2A and FIG. 3A, which will not be described here.

[0194] In step S3202, second channel data is obtained based on a second model.

[0195] The optional implementation of step S3202 can refer to the optional implementation of step S2102 in FIG. 2A, the optional implementation of step S3102 in FIG. 3A, and other associated parts in the embodiments related to FIG. 2A and FIG. 3A, which will not be described here.

[0196] In step S3203, the second channel data is determined as the result of channel prediction.

[0197] The optional implementation of step S3203 can refer to the optional implementation of step S2105 in FIG. 2A, the optional implementation of step S3105 in FIG. 3A, and other associated parts in the embodiments related to FIG. 2A and FIG. 3A, which will not be described here.

[0198] The communication method related to the embodiments of the present disclosure can comprise at least one of steps S3201-S3203. For example, step 3201 can be implemented as an independent embodiment, step 3202 can be implemented as an independent embodiment, step 3203 can be implemented as an independent embodiment, steps 3201+3202 can be implemented as an independent embodiment, steps 3202+3203 can be implemented as an independent embodiment, steps 3201+3202+3203 can be implemented as an independent embodiment, and the like, but are not limited thereto.

[0199] FIG. 4A is a flow diagram of a channel prediction method according to an embodiment of the present disclosure. As shown in FIG. 4A, the embodiment of the present disclosure relates to a channel prediction method, the method is performed by the network device 102, and the method comprises:

[0200] In step S4101, first channel data is obtained based on a first model.

[0201] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in FIG. 2A and other associated parts in the embodiments related to FIG. 2A, which will not be repeated here.

[0202] In step S4102, the second channel data is obtained based on the second model.

[0203] The optional implementation of step S4102 can refer to the optional implementation of step S2102 in FIG. 2A and other associated parts in the embodiments related to FIG. 2A, which will not be repeated here.

[0204] In step S4103, the third channel data is obtained.

[0205] The optional implementation of step S4103 can refer to the optional implementation of step S2103 in FIG. 2A and other associated parts in the embodiments related to FIG. 2A, which will not be repeated here.

[0206] In step S4104, the first output value is obtained.

[0207] The optional implementation of step S4104 can refer to the optional implementation of step S2104 in FIG. 2A and other associated parts in the embodiments related to FIG. 2A, which will not be repeated here.

[0208] In step S4105, the second channel data is determined as the result of channel prediction.

[0209] The optional implementation of step S4105 can refer to the optional implementation of step S2105 in FIG. 2A and other associated parts in the embodiments related to FIG. 2A, which will not be repeated here.

[0210] The communication method related to the embodiments of the present disclosure can include at least one of steps S4101-S4105. For example, step 4101 can be implemented as an independent embodiment, steps 4101+4102 can be implemented as an independent embodiment, step 4102 can be implemented as an independent embodiment, step 4103 can be implemented as an independent embodiment, step 4104 can be implemented as an independent embodiment, steps 4102+4103+4104 can be implemented as an independent embodiment, steps 4101+4102+4103+4104 can be implemented as an independent embodiment, step 4105 can be implemented as an independent embodiment, steps 4101+4102+4105 can be implemented as an independent embodiment, steps 4101+4102+4103+4104+4105 can be implemented as an independent embodiment, and the like, but are not limited thereto.

[0211] In some embodiments, steps S4101, S4102, S4103 can be exchanged in order or executed simultaneously.

[0212] FIG. 4B is a flow diagram illustrating a channel prediction method according to an embodiment of the present disclosure. As shown in FIG. 4B, the embodiment of the present disclosure relates to a channel prediction method, which is performed by the network device 102, and the method comprises the following steps:

[0213] In step S4201, first channel data is obtained based on a first model.

[0214] The optional implementation of step S4201 can refer to the optional implementation of step S2101 in FIG. 2A, the optional implementation of step S4101 in FIG. 4A, and other associated parts in the embodiments related to FIG. 2A and FIG. 4A, which will not be repeated here.

[0215] In step S4202, second channel data is obtained based on a second model.

[0216] The optional implementation of step S4202 can refer to the optional implementation of step S2102 in FIG. 2A, the optional implementation of step S4102 in FIG. 4A, and other associated parts in the embodiments related to FIG. 2A and FIG. 4A, which will not be repeated here.

[0217] In step S4203, the second channel data is determined as the result of channel prediction.

[0218] The optional implementation of step S4203 can refer to the optional implementation of step S2105 in FIG. 2A, the optional implementation of step S4105 in FIG. 4A, and other associated parts in the embodiments related to FIG. 2A and FIG. 4A, which will not be repeated here.

[0219] The communication method related to the embodiments of the present disclosure can include at least one of steps S4201-S4203. For example, step 4201 can be implemented as an independent embodiment, step 4202 can be implemented as an independent embodiment, step 4203 can be implemented as an independent embodiment, steps 4201+4202 can be implemented as an independent embodiment, steps 4202+4203 can be implemented as an independent embodiment, steps 4201+4202+4203 can be implemented as an independent embodiment, and the like, but not limited thereto.

[0220] The following is an exemplary introduction to the method described in the above embodiments.

[0221] The embodiment of the present application proposes a new channel prediction framework based on GAN-LSTM.

[0222] In some embodiments, first, the combination of a generative adversarial network (GAN) and a long short-term memory network (LSTM) effectively solves the challenge of insufficient data sets, can effectively improve the quality and diversity of the required channel data, and is superior to traditional channel modeling in parameter generation efficiency. Further, by extracting and learning the features of channel measurement data, the unknown channel characteristics can be predicted using known information.

[0223] As shown in FIG. 5, FIG. 5 is a schematic diagram of a channel prediction framework based on GAN-LSTM provided by an embodiment of the present application. Using channel measurement data of known frequency bands and scenes, unknown channel characteristics can be predicted.

[0224] GAN stands out due to its powerful ability to generate samples very similar to real data. It uses an adversarial structure to learn real samples, and once training is complete, it can generate new data samples.

[0225] GAN generates output data comparable to real data by adversarial learning of two sub-models in the model: a generation model G and a discrimination model D. The role of G is to generate synthetic data G(z) from a noise vector z with probability distribution P z (z), which fits the real data with probability distribution P data (x) as much as possible. The input of D is the real sample x, and the output is D(x) representing the probability that the vector x is real data.

[0226] The value function V(D; G) of GAN can be as follows:

[0227] In the formula, E[·] represents the expected value, and D(G(z)) represents the probability that D judges the data generated by G to be true or false.

[0228] In the above formula, given G, the measure is the "difference" between P data and P z , so the final optimization goal is to find the optimal G * that makes the "difference" between P data and P z minimum.

[0229] Generative Model:

[0230] In some embodiments, in the GAN generation model architecture design, we use three layers of convolution (Conv) as the main structure because they have good recognition performance for high-dimensional matrices and complex features. The role of the batch normalization (BN) layer added after the Conv layer is to normalize the output of each layer node, enhancing the generalization and robustness of the model. The parameter rectified linear unit (PReLU) activation function is used for reprocessing to improve the accuracy and efficiency of training.

[0231] In some embodiments, LSTM has been proven to be effective in learning long-term dependencies in sequence-related modeling. In order to accurately and timely predict the channel data within a certain time period, channel prediction is represented as a time step sequence prediction problem. Therefore, an LSTM model is composed of several LSTM blocks (as shown in FIG. 2B), and the output of the LSTM model is used as the input of the generation model G to build a GAN-LSTM framework, which is better than the original separate GAN and LSTM.

[0232] The prediction problem is to use the first j states obtained by continuous measurement to predict the next channel state in the spatial domain, and the LSTM-based spatial domain channel prediction can be as shown in FIG. 2C.

[0233] The output h t of the LSTM model contains time step update information obtained from the sequence and is used as the input vector of the generation model:

[0234] h t = f LSTM (h(n-j+1),h(n-j+2),…,h(n)).

[0235] Discriminative Model:

[0236] In some embodiments, since the discriminative model needs to know the feature point distribution of the real channel data, the construction of the discriminative model is more complex than that of the generation model. First, the measurement data is sent to the discriminative model for feature extraction. Then, the synthetic data output by the generation model is learned for the first time by the discriminative model and compared with the real data. The data distribution P G (x) of the synthetic data is similar to the real data distribution P data (x). When the synthetic data is input into the discriminative model again, these networks need to be reused in multiple training iterations, and the weights of these layers are dynamically adjusted to minimize the discriminative loss value. As the number of training iterations increases, the discriminative ability of D tends to a convergence value, reaching the optimum:

[0237] is the optimal value of D. When P G (x) approaches P data (x), will approach 0.5, at which point D has failed to distinguish between real channel data and synthetic channel data. At this point, the channel data generated by G has perfect comparability with the real data, so the GAN model can effectively learn and predict the channel characteristics.

[0238] In some embodiments, in the design of the discriminant model architecture of the GAN, four Conv layers and one fully connected (FC) layer are used. In addition to the BN layer and PReLU used previously, a Dropout layer is added in the D model, which can improve the anti-overfitting ability of the neural network and enhance its versatility. In order to improve the learning efficiency, an additional FC layer is added.

[0239] The embodiments of the present disclosure also propose an apparatus for implementing any of the above methods, for example, an apparatus comprising units or modules for implementing the steps performed by the terminal in any of the above methods. For another example, another apparatus is also proposed, comprising units or modules for implementing the steps performed by the network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0240] 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 the functions of any of the above methods or the units or modules 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 the 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 between the 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 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.

[0241] 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 circuit, and the logical relationship of the hardware circuit 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, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), and the like.

[0242] 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 at least one of a transceiver module 6101, a processing module 6102, and the like. In some embodiments, the processing module 6102 is configured to input a random noise vector into a first model to obtain output first channel data; the processing module is further configured to input the first channel data into a second model to obtain output second channel data; and the processing module is further configured to determine that a first condition is met, and determine the second channel data as a result of channel prediction.

[0243] Optionally, the processing module 6102 is further configured to:

[0244] obtain third channel data, and input the third channel data and the second channel data into a third model to obtain a first output value;

[0245] The first condition is that the first output value converges;

[0246] The third channel data is real data obtained based on channel measurement, and the third model is used to determine the difference between the second channel data and the third channel data.

[0247] Optionally, the first output value is a ratio between a distribution of the third channel data and a first sum, where the first sum is a sum of a distribution of the second channel data and a distribution of the third channel data.

[0248] Optionally, the second model and the third model are iteratively trained alternately.

[0249] Optionally, the first model is a long short-term memory network model, and the second model and the third model are generative adversarial network models, where the second model is a generative model in the generative adversarial network model, and the third model is a discriminative model in the generative adversarial network model.

[0250] Optionally, the transceiver module is configured to perform at least one of the communication steps of sending and / or receiving performed by the terminal 101 in any of the above methods, which will not be described herein again.

[0251] Optionally, the processing module is configured to perform at least one of the other steps (for example, steps S2101, S2102, step S2103, step S2104, step S2105, but not limited thereto) performed by the terminal 101 in any of the above methods, which will not be described herein again.

[0252] 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 at least one of a transceiver module 6201, a processing module 6202, and the like. In some embodiments, the processing module 6202 is configured to input a random noise vector into a first model to obtain output first channel data, input the first channel data into a second model to obtain output second channel data, and determine the second channel data as a result of channel prediction when a first condition is met.

[0253] Optionally, the processing module 6202 is further configured to:

[0254] obtain third channel data, and input the third channel data and the second channel data into a third model to obtain a first output value;

[0255] The first condition is that the first output value converges.

[0256] The third channel data is real data obtained based on channel measurement, and the third model is used to determine the difference between the second channel data and the third channel data.

[0257] Optionally, the first output value is a ratio between a distribution of the third channel data and a first sum, where the first sum is a sum of a distribution of the second channel data and the distribution of the third channel data.

[0258] Optionally, the second model and the third model are iteratively trained alternately.

[0259] Optionally, the first model is a long short-term memory network model, and the second model and the third model are generative adversarial network models, where the second model is a generative model in the generative adversarial network model, and the third model is a discriminative model in the generative adversarial network model.

[0260] Optionally, the transceiver is configured to perform at least one of the communication steps, such as the sending and / or receiving, performed by the network device 102 in any of the above methods, which will not be repeated here.

[0261] Optionally, the processing module is configured to perform at least one of the other steps performed by the network device 102 in any of the above methods (for example, steps S2101, S2102, step S2103, step S2104, step S2105, but not limited thereto), which will not be repeated here.

[0262] FIG. 7A is a structural schematic diagram of a communication device 7100 according to the embodiments of the present disclosure. The communication device 7100 can be a network device (such as an access network device, a core network device, etc.), a terminal (such as 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 specific implementation can be referred to the descriptions in the above method embodiments.

[0263] 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 (such as 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. The communication device 7100 is configured to implement any of the above methods.

[0264] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memory 7102 can also be outside the communication device 7100.

[0265] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceiver 7103 performs at least one of the communication steps of transmitting and / or receiving in the above-described methods, and the processor 7101 performs at least one of the other steps.

[0266] In some embodiments, 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, etc. can be replaced by each other, the terms of transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be replaced by each other, and the terms of receiver, receiving unit, receiver, receiving circuit, etc. can be replaced by each other.

[0267] In some embodiments, the communication device 7100 can include one or more interface circuits 7104. Optionally, the interface circuit 7104 is connected with the memory 7102, and the interface circuit 7104 can be used to receive signals from the memory 7102 or other devices, and can be used to send signals to the memory 7102 or other devices. For example, the interface circuit 7104 can read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0268] 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 FIG. 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 also include storage components 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, etc.

[0269] FIG. 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 FIG. 7B can be referred to, but is not limited thereto.

[0270] The chip 7200 includes one or more processors 7201, and the chip 7200 is configured to execute any of the above methods.

[0271] In some embodiments, the chip 7200 further includes one or more interface circuits 7202. Optionally, the interface circuit 7202 is connected with the memory 7203, and the interface circuit 7202 can be configured to receive signals from the memory 7203 or other devices, and the interface circuit 7202 can be configured to send signals to the memory 7203 or other devices. For example, the interface circuit 7202 can read instructions stored in the memory 7203 and send the instructions to the processor 7201.

[0272] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (such as transmitting and / or receiving) in the above method, and the processor 7201 performs at least one of the other steps (such as steps S2101, S2102, step S2103, step S2104, step S2105, but not limited thereto).

[0273] In some embodiments, the terms of interface circuit, interface, transceiver pin, transceiver, etc. can be replaced with each other.

[0274] In some embodiments, the chip 7200 further includes one or more memories 7203 for storing instructions. Optionally, all or part of the memory 7203 can be outside the chip 7200.

[0275] The present disclosure further proposes a storage medium, and the above storage medium stores instructions, and when the above instructions run on the communication device 7100, the communication device 7100 performs any one of the above methods. Optionally, the above storage medium is an electronic storage medium. Optionally, the above storage medium is a computer readable storage medium, but not limited thereto, and it can also be a storage medium readable by other devices. Optionally, the above storage medium can be a non-transitory storage medium, but not limited thereto, and it can also be a transitory storage medium.

[0276] The present disclosure further proposes a program product, and the above program product is executed by the communication device 7100, so that the communication device 7100 performs any one of the above methods. Optionally, the above program product is a computer program product.

[0277] The present disclosure further proposes a computer program, and when the computer program runs on a computer, the computer executes any one of the above methods.

[0278] In the embodiments described above, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer programs are loaded on a computer and executed, all or part of the processes or functions described in the embodiments of the present disclosure are produced. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer programs can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another computer readable storage medium, for example, the computer programs can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as high-density digital video disc (digital video disc, DVD)), or semiconductor media (such as solid state disk (solid state disk, SSD)) and the like.

[0279] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. A skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present disclosure.

[0280] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0281] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present disclosure, which should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A channel prediction method characterized by, The method is performed by a terminal, and the method comprises: inputting a random noise vector into a first model to obtain output first channel data; inputting the first channel data into a second model to obtain output second channel data; determining that a first condition is met, and determining the second channel data as a result of channel prediction.

2. The method of claim 1, wherein, The method further comprises: obtaining third channel data, inputting the third channel data and the second channel data into a third model to obtain a first output value; the first condition is that the first output value converges; wherein the third channel data is real data obtained based on channel measurement, and the third model is used to judge the difference between the second channel data and the third channel data.

3. The method of claim 2, wherein, The first output value is a ratio between the distribution of the third channel data and a first sum, wherein the first sum is the sum of the distribution of the second channel data and the distribution of the third channel data.

4. The method according to claim 2 or 3, characterized in that, The second model and the third model are alternately iteratively trained.

5. The method according to any one of claims 1 to 4, characterized in that, The first model is a long short-term memory network model, and the second model and the third model are generative adversarial network models, wherein the second model is a generative model in the generative adversarial network model, and the third model is a discriminative model in the generative adversarial network model.

6. A channel prediction method characterized by, The method is performed by a network device, and the method comprises: inputting a random noise vector into a first model to obtain output first channel data; inputting the first channel data into a second model to obtain output second channel data; determining that a first condition is met, and determining the second channel data as a result of channel prediction.

7. The method of claim 6, wherein, The method further comprises: obtaining third channel data, inputting the third channel data and the second channel data into a third model to obtain a first output value; the first condition is that the first output value converges; wherein the third channel data is real data obtained based on channel measurement, and the third model is used to judge the difference between the second channel data and the third channel data.

8. The method of claim 7, wherein, The first output value is a ratio between the distribution of the third channel data and a first sum, wherein the first sum is the sum of the distribution of the second channel data and the distribution of the third channel data.

9. The method according to claim 7 or 8, characterized in that, The second model and the third model are alternately iteratively trained.

10. The method according to any one of claims 6-9, characterized in that, The first model is a long short-term memory network model, and the second model and the third model are generative adversarial network models, wherein the second model is a generative model in the generative adversarial network model, and the third model is a discriminative model in the generative adversarial network model.

11. A terminal, characterized by comprising: The terminal comprises: a processing module configured to input a random noise vector into a first model to obtain output first channel data; the processing module is further configured to input the first channel data into a second model to obtain output second channel data; the processing module is further configured to determine that a first condition is met, and determine the second channel data as a result of channel prediction.

12. A network device, comprising: The network device comprises: a processing module configured to input a random noise vector into a first model to obtain output first channel data; The processing module is further configured to input the first channel data into a second model to obtain output second channel data. The processing module is further configured to determine that a first condition is satisfied, and determine the second channel data as a result of channel prediction.

13. A communications device, characterized by The apparatus comprises: one or more processors; The apparatus is configured to perform the channel prediction method of any one of claims 1-5, or perform the channel prediction method of any one of claims 6-10.

14. A storage medium, the storage medium storing instructions, wherein, The instructions, when executed on a communications device, cause the communications device to perform the channel prediction method of any one of claims 1-5 or 6-10.

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