Channel prediction methods, devices, network-side equipment and terminals

Abstract

This application discloses a channel prediction method, apparatus, network-side device, and terminal, belonging to the field of mobile communications. The channel prediction method of this application includes: the network-side device acquiring channel prediction information from the terminal; the network-side device selecting a channel prediction configuration based on the channel prediction information; wherein, the channel prediction configuration includes the configuration of an artificial intelligence network for performing channel prediction.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of mobile communication, and particularly relates to a channel prediction method and device, a network side equipment and a terminal. BACKGROUND

[0002] In downlink communication, a base station needs to obtain as accurate channel information as possible, such as channel state information (CSI), so as to perform reasonable precoding construction and multi-user scheduling. In a high-speed scenario, the channel changes rapidly, and a conventional CSI reporting period is insufficient to meet the rapid channel change. The base station needs to predict the channel state after a certain period of time according to the obtained CSI information, so as to perform real-time precoding calculation and multi-user scheduling.

[0003] With the wide application of artificial intelligence (AI) in various fields, AI is also used for channel prediction. The AI module has various implementation manners, such as neural network, decision tree, support vector machine, and Bayesian classifier.

[0004] However, when the AI is used for channel prediction, the calculation amount may be wasted and the CSI overhead may be increased in a good channel condition, and the prediction accuracy may be affected in a poor channel condition. SUMMARY

[0005] Embodiments of the present application provide a channel prediction method and device, a network side equipment and a terminal, which can solve the problem that the calculation amount may be wasted and the CSI overhead may be increased in a good channel condition, and the prediction accuracy may be affected in a poor channel condition.

[0006] In a first aspect, a channel prediction method is provided, applied to a network side equipment, comprising:

[0007] The network side equipment obtains channel prediction information from a terminal;

[0008] The network side equipment determines a channel prediction configuration according to the channel prediction information; wherein the channel prediction configuration comprises a configuration of an artificial intelligence network used for performing channel prediction.

[0009] In a second aspect, a channel prediction device is provided, comprising:

[0010] A transceiver module is configured to obtain channel prediction information from a terminal;

[0011] The configuration module is configured to determine a configuration of channel prediction according to the channel prediction information, wherein the configuration of channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction.

[0012] In a third aspect, a channel prediction method is provided, which is applied to a terminal and comprises the following steps:

[0013] The terminal determines channel prediction information.

[0014] The terminal reports the channel prediction information to a network side device, wherein the channel prediction information is used to determine a configuration of channel prediction of the network side device, and the configuration of channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction.

[0015] In a fourth aspect, a channel prediction device is provided, which comprises the following modules:

[0016] The calculation module is configured to determine channel prediction information.

[0017] The reporting module is configured to report the channel prediction information to a network side device, wherein the channel prediction information is used to determine a configuration of channel prediction of the network side device, and the configuration of channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction.

[0018] In a fifth aspect, a network side device is provided, which comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and when the program or instruction is executed by the processor, the steps of the method according to the first aspect are implemented.

[0019] In a sixth aspect, a terminal is provided, which comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and when the program or instruction is executed by the processor, the steps of the method according to the third aspect are implemented.

[0020] In a seventh aspect, a readable storage medium is provided, which stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the method according to the first aspect are implemented, or the steps of the method according to the third aspect are implemented.

[0021] In an eighth aspect, a chip is provided, which comprises a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or instruction to implement the method according to the first aspect or the method according to the third aspect.

[0022] In a ninth aspect, a computer program / program product is provided, which is stored in a non-transitory storage medium, and the program / program product is executed by at least one processor to implement the method according to the first aspect, or implement the steps of the method according to the third aspect.

[0023] In the embodiments of the present application, the network side device obtains channel prediction information from the terminal, and determines a configuration for channel prediction according to the channel prediction information, wherein the configuration for channel prediction includes a configuration of an artificial intelligence network used for performing channel prediction, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction, a complex network is used for tracking the channel when the channel changes fast, the accuracy of channel prediction is improved, a relatively simple network is used for channel prediction when the channel changes slowly, the complexity of the network is reduced, and the RS overhead and the CSI overhead can also be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A structural schematic diagram of a wireless communication system to which the embodiments of the present application can be applied is shown;

[0025] Figure 2 A flowchart of a channel prediction method according to the embodiments of the present application is shown;

[0026] Figure 3 Another flowchart of a channel prediction method according to the embodiments of the present application is shown;

[0027] Figure 4 A structural schematic diagram of a channel prediction device according to the embodiments of the present application is shown;

[0028] Figure 5 Another flowchart of a channel prediction method according to the embodiments of the present application is shown;

[0029] Figure 6 A structural schematic diagram of a channel prediction device according to the embodiments of the present application is shown;

[0030] Figure 7 A structural schematic diagram of a communication device according to the embodiments of the present application is shown;

[0031] Figure 8 A structural schematic diagram of a network side device according to the embodiments of the present application is shown;

[0032] Figure 9 A structural schematic diagram of a terminal according to the embodiments of the present application is shown. DETAILED DESCRIPTION

[0033] With reference to the drawings and the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly described. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0034] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than that illustrated or described herein, and the objects distinguished by "first", "second" are generally a category, not limited to the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally means that the front and rear associated objects are in an "or" relationship.

[0035] It is worth noting that the technology described in the embodiments of the present application is not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the described technology can be used in the above mentioned systems and radio technologies, and also in other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in most of the following description, but these technologies can also be applied outside the NR system application, such as 6th Generation (6G) communication systems. th

[0036] Figure 1 ​This diagram illustrates the structure of a wireless communication system applicable to embodiments of this application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can also be referred to as a terminal device or user equipment (UE). The terminal 11 can be a mobile phone, tablet computer, laptop computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), wearable device, vehicle-mounted device (VUE), pedestrian terminal (PUE), etc. Wearable devices include smartwatches, wristbands, headphones, glasses, etc. It should be noted that this application does not limit the specific type of terminal 11. Network-side device 12 can be a base station or a core network. The base station can be referred to as a node B, evolved node B, access point, base transceiver station (BTS), radio base station, radio transceiver, basic service set (BSS), extended service set (ESS), B node, evolved B node (eNB), home B node, home evolved B node, WLAN access point, WiFi node, transmitting and receiving point (TRP), or any other suitable term in the field, as long as the same technical effect is achieved. The base station is not limited to specific technical terms. It should be noted that in this application embodiment, only the base station in the NR system is used as an example, but the specific type of base station is not limited.

[0037] The channel prediction method provided in this application will be described in detail below with reference to the accompanying drawings, through some embodiments and application scenarios.

[0038] like Figure 2 As shown in the figure, this application provides a channel prediction method. The execution subject of this method can be a network-side device; in other words, the method can be executed by software or hardware installed on the network-side device. The execution steps of the method are as follows.

[0039] Step S210: The network-side device obtains channel prediction information from the terminal;

[0040] The terminal can determine the channel prediction information according to actual needs. In an embodiment, the terminal can determine the channel prediction information based on a protocol agreement, or determine the channel prediction information according to a period configured by a network side.

[0041] In an embodiment, the terminal determining the channel prediction information includes performing channel estimation, receiving channel state information reference signals (CSI Reference Signals, CSI-RSs) at a plurality of time domain positions, performing channel estimation to obtain downlink channels H0, H1, H2, …, H N-1 , where N is the number of CSI-RSs received in the time domain, and each H i = [h i0 ,h i1 ,h i2 …h i,M-1 represents channel estimation results in different frequency domains at the i-th moment, and M is the number of CSI-RSs in the frequency domain.

[0042] The channel prediction information can be various. In an embodiment, the channel prediction information includes at least one of the following:

[0043] A channel variation rate;

[0044] A level of the channel variation rate, which can be obtained after quantization of the channel variation rate.

[0045] It should be understood that the channel prediction information can also be other expression information for indicating a channel variation state.

[0046] The channel variation rate includes at least one of the following:

[0047] A time domain correlation variation rate;

[0048] A specific path amplitude variation rate;

[0049] A specific path phase variation rate;

[0050] A specific path delay variation rate;

[0051] A specific port amplitude variation rate;

[0052] A specific port phase variation rate;

[0053] A terminal moving speed;

[0054] A terminal rotating speed;

[0055] A beam variation rate.

[0056] Further, the specific path includes at least one of:

[0057] a path with the maximum power;

[0058] a plurality of paths with the maximum power;

[0059] a path with power concentrated in a Line of Sight (LOS) propagation direction;

[0060] a path with power exceeding a first threshold.

[0061] The method for determining the time-domain correlation by the terminal can be determined by the terminal or a network-side device according to actual conditions. For example, the time-domain correlation of two channels with a time-domain interval of k can be expressed as:

[0062] The method for determining the change rate of the time-domain correlation by the terminal can be various. In an embodiment, if the time-domain correlation at a time t0 is K0=[k0, k1, … kN-1], and the time-domain correlation at a next time t1 is K1, the terminal can calculate the change of the time-domain correlation at adjacent times, and calculate sum(|K1-K0|) as the change rate of the time-domain correlation, and directly report or report after quantization to the network-side device. N-1

[0063] In another embodiment, the change rate of the time-domain correlation can include the change rate of a target parameter of a U-shaped spectrum. The terminal can calculate the Discrete Fourier Transform (DFT) change of the time-domain correlation K0 and K1, i.e., the U-shaped spectrum in the frequency domain, and use the change rate of the 3dB width of the U-shaped spectrum as the change rate of the time-domain correlation, and directly report or report after quantization to the network-side device.

[0064] In an embodiment, the terminal can determine the change rate of the amplitude or phase of a specific path or a specific port of a channel in a period of time by performing channel estimation on the specific path or the specific port, and directly report or report after quantization to the network-side device.

[0065] In an embodiment, the terminal can obtain the time delay position of a specific path searched in the time-domain channel by performing estimation on the channel of the same specific port in a plurality of symbols, calculate the change rate of the time-domain position of the specific path with time, and directly report or report after quantization to the network-side device.

[0066] ​In an embodiment, the change rate of the beam can include a change rate of a target parameter of a specific beam; wherein the target parameter can include a Reference Signal Received Power (RSRP), a Reference Signal Received Quality (RSRQ), a Signal-to-Noise and Interference Ratio (SINR), etc. The terminal calculates the change rate of the RSRP or RSRQ information by tracking the channel quality of the same specific beam within a beam switching period, and directly reports or reports after quantization to the network side device.

[0067] In another embodiment, the change rate of the beam can include a rate of switching the beam. The terminal determines the rate of switching the beam by calculating the number of times of switching the beam within a long period of time, and directly reports or reports after quantization to the network side device.

[0068] Further, the specific beam includes at least one of the following:

[0069] a beam corresponding to a minimum control resource set ID (CORESET ID);

[0070] a beam corresponding to a control resource set CORESET 0;

[0071] a beam indicated by the network side device or the base station.

[0072] Step S220, the network side device determines a configuration for channel prediction according to the channel prediction information; wherein the configuration for channel prediction includes a configuration of an AI network for performing channel prediction. The network side device performs channel prediction by the AI network after configuration according to the determined configuration.

[0073] The network side device finds the configuration of the corresponding AI network and the configuration of other parameters according to the channel prediction information reported by the terminal. In an embodiment, the configuration for channel prediction includes at least one of the following:

[0074] a structure of the AI network;

[0075] a parameter of the AI network;

[0076] input data of the AI network;

[0077] a time span that can be predicted by the AI network;

[0078] configuration of RS;

[0079] configuration of CSI;

[0080] configuration of reporting of the CSI;

[0081] period of reporting CSI;

[0082] complexity of non-AI prediction algorithm, which can include: number of iterations, polynomial exponent, etc.

[0083] As can be seen from the technical solutions of the above embodiments, the application embodiments acquire channel prediction information from a terminal by a network side device; the network side device determines configuration for channel prediction according to the channel prediction information; wherein the configuration for channel prediction includes configuration of an artificial intelligence network for performing channel prediction, so that the AI network can be reasonably configured or switched, a more suitable AI network is used for channel prediction, a complex network is used for tracking a channel when the channel changes fast, the accuracy of channel prediction is improved, a relatively simple network is used for channel prediction when the channel changes slowly, the complexity of the network is reduced, and RS overhead and CSI overhead can also be reduced.

[0084] Based on the above embodiments, further, as shown in the step S210, the method further includes: Figure 3

[0085] Step S200, the network side device sends a first indication to the terminal, and the first indication is used to instruct the terminal to determine channel prediction information.

[0086] The terminal determines channel prediction information according to the first indication in the case of receiving the first indication; and the terminal can determine channel prediction information according to a configured period and / or according to a protocol agreement in the case of not receiving the first indication.

[0087] The content indicated by the first indication can include at least one of the following:

[0088] indicating whether the terminal reports or does not report channel prediction information;

[0089] corresponding relationship between the channel prediction information and the configuration of channel prediction;

[0090] calculation method of the channel prediction information;

[0091] time-frequency resource position of the terminal for channel estimation, so that the terminal receives CSI-RS for channel estimation at the time-frequency resource position.

[0092] ​In an embodiment, the form of the first indication comprises at least one of the following:

[0093] Radio Resource Control (RRC) signaling;

[0094] Medium Access Control Control Element (MAC CE) signaling;

[0095] Downlink Control Information (DCI).

[0096] In an embodiment, before the step S200, the method further comprises:

[0097] The network-side device sends first configuration information to the terminal, and the first configuration information comprises a configuration for determining the channel prediction information.

[0098] The first configuration information can comprise at least one of the following:

[0099] A correspondence between the channel prediction information and the configuration of channel prediction;

[0100] A calculation method of the channel prediction information;

[0101] A time-frequency resource position used by the terminal for channel estimation.

[0102] After obtaining the first configuration information, the terminal can determine channel prediction information based on the first configuration information in a case where the first indication sent by the network-side device is received.

[0103] As can be seen from the technical solutions of the above embodiments, the present application embodiment sends a first indication to the terminal by a network-side device, the first indication is used to instruct the terminal to determine channel prediction information, and determines the configuration of channel prediction according to the channel prediction information reported by the terminal, so that the terminal can be instructed to timely report channel prediction information, which is used to timely and reasonably configure or switch the AI network, and the more suitable AI network is used for channel prediction.

[0104] It should be noted that the channel prediction method provided by the present application embodiment can be executed by a channel prediction device, or a control module in the channel prediction device for executing the channel prediction method. In the present application embodiment, the channel prediction device executes the channel prediction method as an example to illustrate the channel prediction device provided by the present application embodiment.

[0105] As Figure 4As shown, the embodiment of the present application provides a channel prediction device, which comprises a transceiving module 401 and a configuration module 402.

[0106] The transceiving module 401 is configured to acquire channel prediction information from a terminal; and the configuration module 402 is configured to determine a configuration of channel prediction according to the channel prediction information; wherein the configuration of channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction.

[0107] Further, the channel prediction information comprises at least one of the following:

[0108] a channel variation rate;

[0109] a level of the channel variation rate.

[0110] Further, the channel variation rate comprises at least one of the following:

[0111] a variation rate of a time domain correlation;

[0112] a variation rate of an amplitude of a specific path;

[0113] a variation rate of a phase of the specific path;

[0114] a variation rate of a time delay of the specific path;

[0115] a variation rate of an amplitude of a specific port;

[0116] a variation rate of a phase of the specific port;

[0117] a moving speed of the terminal;

[0118] a rotating speed of the terminal;

[0119] a variation rate of a beam.

[0120] Further, the variation rate of the time domain correlation comprises a variation rate of a target parameter of a U-shaped spectrum.

[0121] Further, the specific path comprises at least one of the following:

[0122] a path with maximum power;

[0123] a plurality of paths with maximum power;

[0124] a path with power concentrated in a line-of-sight propagation direction;

[0125] a path with power exceeding a first threshold.

[0126] Further, the variation rate of the beam comprises at least one of the following:

[0127] a rate of change of a target parameter of a specific beam;

[0128] a rate of switching beams.

[0129] Further, the specific beam comprises at least one of:

[0130] a minimum control resource set identification corresponding to the beam;

[0131] a control resource set CORESET0 corresponding to the beam;

[0132] a beam indicated by a network side device or a base station.

[0133] Further, the configuration of the channel prediction comprises at least one of: a structure of the artificial intelligence network;

[0134] a parameter of the artificial intelligence network;

[0135] input data of the artificial intelligence network;

[0136] a time span of the prediction;

[0137] a configuration of a reference signal;

[0138] a configuration of channel state information;

[0139] a configuration of reporting of the channel state information;

[0140] a period of reporting of the channel state information;

[0141] a complexity of a non-artificial intelligence prediction algorithm.

[0142] As can be seen from the technical solutions of the above embodiments, the embodiments of the present application acquire channel prediction information from a terminal; and according to the channel prediction information, a configuration of channel prediction is determined; wherein the configuration of the channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction, in a case where a channel changes fast, a complex network is used to track the channel, and the accuracy of channel prediction is improved, in a case where a channel changes slowly, a relatively simple network is used for channel prediction, and the complexity of the network is reduced, and RS overhead and CSI overhead can also be reduced.

[0143] Based on the above embodiments, further, the transceiver module is further configured to send a first indication to the terminal, and the first indication is used to instruct the terminal to determine channel prediction information.

[0144] Further, the content indicated by the first indication comprises at least one of:

[0145] indicate the terminal to report or not to report channel prediction information;

[0146] a correspondence between the channel prediction information and a configuration of channel prediction;

[0147] a calculation method of the channel prediction information;

[0148] a time-frequency resource position for the terminal to perform channel estimation.

[0149] Further, the form of the first indication comprises at least one of:

[0150] radio resource control signaling;

[0151] medium access control element signaling;

[0152] downlink control information.

[0153] Further, the transceiver module is further configured to send first configuration information to the terminal, the first configuration information comprising a configuration for determining the channel prediction information.

[0154] Further, the first configuration information comprises at least one of:

[0155] a correspondence between the channel prediction information and a configuration of channel prediction;

[0156] a calculation method of the channel prediction information;

[0157] a time-frequency resource position for the terminal to perform channel estimation.

[0158] As can be seen from the technical solutions of the above embodiments, the embodiments of the present application send a first indication to a terminal, the first indication being used to instruct the terminal to determine channel prediction information, and according to the channel prediction information reported by the terminal, a configuration for performing channel prediction is determined, so that the terminal can be instructed to timely report channel prediction information, which is used to timely and reasonably configure or switch an AI network, and a more suitable AI network is used to perform channel prediction.

[0159] The channel prediction device in the embodiments of the present application can be a device, a device with an operating system, or an electronic device, and can also be a component in a terminal, an integrated circuit, or a chip. The device or electronic device can be a mobile terminal or a non-mobile terminal. Illustratively, the mobile terminal can include, but is not limited to, the types of terminal 11 listed above, and the non-mobile terminal can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., and the embodiments of the present application are not limited in this regard.

[0160] The channel prediction device provided by the embodiments of the present application can realize Figures 2-3 The method embodiments realize various processes and achieve the same technical effects. To avoid repetition, the details are not described here.

[0161] As shown in Figure 5 The embodiments of the present application also provide a channel prediction method. The execution subject of the method can be a terminal, in other words, the method can be executed by software or hardware installed in the terminal. The execution steps of the method are as follows.

[0162] In step S510, the terminal determines channel prediction information.

[0163] Further, the channel prediction information includes at least one of the following:

[0164] a channel variation rate;

[0165] a level of the channel variation rate.

[0166] Further, the channel variation rate includes at least one of the following:

[0167] a variation rate of a time domain correlation;

[0168] a variation rate of an amplitude of a specific path;

[0169] a variation rate of a phase of the specific path;

[0170] a variation rate of a time delay of the specific path;

[0171] a variation rate of an amplitude of a specific port;

[0172] a variation rate of a phase of the specific port;

[0173] a moving speed of the terminal;

[0174] a rotating speed of the terminal;

[0175] a variation rate of a beam.

[0176] Further, the variation rate of the time domain correlation includes:

[0177] a variation rate of a target parameter of a U-shaped spectrum.

[0178] Further, the specific path includes at least one of the following:

[0179] a path with the maximum power;

[0180] a plurality of paths with the maximum power;

[0181] a path with power concentrated in a line-of-sight propagation direction;

[0182] a path whose power exceeds a first threshold.

[0183] Further, the rate of change of the beam comprises at least one of:

[0184] a rate of change of a target parameter of a specific beam;

[0185] a rate of switching beams.

[0186] Further, the specific beam comprises at least one of:

[0187] a minimum control resource set identification corresponding to the beam;

[0188] a control resource set CORESET0 corresponding to the beam;

[0189] a beam indicated by a network side device or a base station.

[0190] Step S520, the terminal reports the channel prediction information to the network side device, and the channel prediction information is used to determine the configuration of the channel prediction of the network side device; wherein the configuration of the channel prediction comprises the configuration of an artificial intelligence network used to perform channel prediction.

[0191] Further, the configuration of the channel prediction comprises at least one of:

[0192] a structure of the artificial intelligence network;

[0193] a parameter of the artificial intelligence network;

[0194] input data of the artificial intelligence network;

[0195] a time span of prediction;

[0196] a configuration of a reference signal;

[0197] a configuration of channel state information;

[0198] a configuration of reporting of the channel state information;

[0199] a period of reporting the channel state information;

[0200] a complexity of a non-artificial intelligence prediction algorithm.

[0201] The steps S510-520 can implement the method embodiment of steps S210-S220 as shown in Figure 2 The same technical effects are obtained, and the repeated parts will not be described here.

[0202] From the technical solutions of the above-mentioned embodiments, it can be seen that the embodiments of the present application determine channel prediction information by a terminal; the terminal reports the channel prediction information to a network side device, and the channel prediction information is used to determine configuration of channel prediction of the network side device; wherein the configuration of channel prediction includes configuration of an artificial intelligence network used to perform channel prediction, so that the AI network can be reasonably configured or switched, a more suitable AI network is used to perform channel prediction, when the channel changes fast, a complex network is used to track the channel, the accuracy of channel prediction is improved, when the channel changes slowly, a relatively simple network is used to perform channel prediction, the complexity of the network is reduced, and RS overhead and CSI overhead can also be reduced.

[0203] Based on the above-mentioned embodiments, further, the step S510 includes at least one of the following:

[0204] The terminal determines the channel prediction information according to a first indication sent by the network side device;

[0205] The terminal determines the channel prediction information according to a configured period without receiving the first indication;

[0206] The terminal determines the channel prediction information according to a protocol agreement.

[0207] Further, the content indicated by the first indication includes at least one of the following:

[0208] Indicating whether the terminal reports or does not report the channel prediction information;

[0209] Correspondence between the channel prediction information and the configuration of channel prediction;

[0210] The calculation method of the channel prediction information;

[0211] The time-frequency resource position used by the terminal to perform channel estimation.

[0212] Further, the form of the first indication includes at least one of the following:

[0213] Radio resource control signaling;

[0214] Medium access control element signaling;

[0215] Downlink control information.

[0216] Further, before the terminal determines the channel prediction information according to the first indication sent by the network side device, the method further includes:

[0217] The terminal receives first configuration information sent by the network side device, and the first configuration information includes configuration used to determine the channel prediction information.

[0218] Furthermore, the first configuration information includes at least one of the following:

[0219] The correspondence between the channel prediction information and the channel prediction configuration;

[0220] The method for calculating the channel prediction information;

[0221] The terminal is used for time-frequency resource location for channel estimation.

[0222] As can be seen from the technical solutions of the above embodiments, the terminal in this application embodiment can determine and report channel prediction information according to the first instruction sent by the network-side device, the configured period and / or the protocol agreement, so as to enable the network-side device to determine the configuration for channel prediction, thereby enabling timely reporting of channel prediction information, so that the network-side device can timely configure or switch the AI ​​network reasonably and use a more suitable AI network for channel prediction.

[0223] It should be noted that the channel prediction method provided in this application can be executed by a channel prediction device, or by a control module within that channel prediction device. This application uses the example of a channel prediction device executing the channel prediction method to illustrate the channel prediction device provided in this application.

[0224] like Figure 6 As shown in the embodiment of this application, another channel prediction device is also provided, which includes a calculation module 601 and a reporting module 602.

[0225] The calculation module 601 is used to determine channel prediction information; the reporting module 602 is used to report the channel prediction information to the network-side device, and the channel prediction information is used to determine the configuration of the network-side device for performing channel prediction; wherein, the configuration of the channel prediction includes the configuration of the artificial intelligence network used to perform channel prediction.

[0226] Furthermore, the channel prediction information includes at least one of the following:

[0227] Channel change rate;

[0228] The level of the channel change rate.

[0229] Furthermore, the channel change rate includes at least one of the following: the change rate of time-domain correlation;

[0230] The rate of change of the amplitude of a specific diameter;

[0231] The rate of change of the phase of the specific path;

[0232] The rate of change of the time delay of the specific path;

[0233] The rate of change of amplitude at a specific port;

[0234] The rate of change of the phase at the specific port;

[0235] The terminal's moving speed;

[0236] The rotational speed of the terminal;

[0237] The rate of change of the beam.

[0238] Furthermore, the rate of change of the time-domain correlation includes the rate of change of the target parameters of the U-shaped spectrum.

[0239] Furthermore, the specific path includes at least one of the following:

[0240] The diameter with the highest power;

[0241] The several diameters with the highest power;

[0242] The radius in which power is concentrated in the line-of-sight propagation direction;

[0243] The path whose power exceeds the first threshold.

[0244] Furthermore, the beam variation rate includes at least one of the following:

[0245] The rate of change of the target parameters of a specific beam;

[0246] The rate at which the beam is switched.

[0247] Furthermore, the specific beam includes at least one of the following:

[0248] The smallest set of control resources identifies the corresponding beam;

[0249] Control the beam corresponding to resource set CORESET0;

[0250] The beam indicated by network-side equipment or base station.

[0251] Furthermore, the channel prediction configuration includes at least one of the following:

[0252] The structure of the artificial intelligence network;

[0253] The parameters of the artificial intelligence network;

[0254] The input data of the artificial intelligence network;

[0255] The predicted time span;

[0256] Configuration of the reference signal;

[0257] Configuration of channel state information;

[0258] Configuration of the channel state information reporting;

[0259] The period for reporting channel status information;

[0260] The complexity of non-AI prediction algorithms.

[0261] As can be seen from the technical solutions of the above embodiments, the embodiments of this application determine channel prediction information; report the channel prediction information to the network-side device, the channel prediction information being used to determine the configuration of the network-side device for channel prediction; wherein, the configuration of the channel prediction includes the configuration of the artificial intelligence network used to perform channel prediction, thereby enabling reasonable configuration or switching of the AI ​​network, using a more suitable AI network for channel prediction, using a complex network to track the channel when the channel changes rapidly to improve the accuracy of channel prediction, and using a relatively simple network to perform channel prediction when the channel changes slowly to reduce network complexity, and also reducing RS overhead and CSI overhead.

[0262] Based on the above embodiments, the computing module is further configured to perform at least one of the following:

[0263] The channel prediction information is determined based on the first instruction sent by the network-side device;

[0264] If the first instruction is not received, channel prediction information is determined according to the configured period.

[0265] Channel prediction information is determined according to the agreement.

[0266] Furthermore, the content indicated by the first instruction includes at least one of the following:

[0267] Instructions to report or not report channel prediction information;

[0268] The correspondence between the channel prediction information and the channel prediction configuration;

[0269] The method for calculating the channel prediction information;

[0270] Location of time-frequency resources used for channel estimation.

[0271] Furthermore, the first instruction may take the form of at least one of the following:

[0272] Radio resource control signaling;

[0273] Media access control unit signaling;

[0274] Downlink control information.

[0275] Furthermore, the calculation module is also configured to receive first configuration information sent by the network-side device, the first configuration information including configuration for determining the channel prediction information.

[0276] Furthermore, the first configuration information includes at least one of the following:

[0277] The correspondence between the channel prediction information and the channel prediction configuration;

[0278] The method for calculating the channel prediction information;

[0279] The terminal is used for time-frequency resource location for channel estimation.

[0280] As can be seen from the technical solutions of the above embodiments, the embodiments of this application can determine and report channel prediction information according to the first instruction sent by the network-side device, the configured period and / or the protocol agreement, so as to enable the network-side device to determine the configuration for channel prediction, thereby enabling the terminal to report channel prediction information in a timely manner, so as to enable the network-side device to reasonably configure or switch the AI ​​network in a timely manner, and use a more suitable AI network for channel prediction.

[0281] The channel prediction device in this application embodiment can be a device, a device with an operating system, or an electronic device, or it can be a component, integrated circuit, or chip in a terminal. The device or electronic device can be a mobile terminal or a non-mobile terminal. For example, a mobile terminal can include, but is not limited to, the types of terminals 11 listed above, while a non-mobile terminal can be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the type of terminal.

[0282] The channel prediction device provided in this application embodiment can achieve... Figure 5 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0283] Furthermore, such as Figure 7As shown, this application embodiment also provides a communication device 700, including a processor 701, a memory 702, and a program or instructions stored in the memory 702 and executable on the processor 701. For example, when the communication device 700 is a terminal, the program or instructions executed by the processor 701 implement the various processes of the above-described channel prediction method embodiment and achieve the same technical effect. When the communication device 700 is a network-side device, the program or instructions executed by the processor 701 implement the various processes of the above-described channel prediction method embodiment and achieve the same technical effect; to avoid repetition, further details are omitted here.

[0284] This application also provides a network-side device, including a processor and a communication interface. The processor is used to determine a channel prediction configuration based on the channel prediction information. The channel prediction configuration includes the configuration of an artificial intelligence network for performing channel prediction. The communication interface is used to acquire channel prediction information from a terminal. This network-side device embodiment corresponds to the above-described network-side device method embodiment. All implementation processes and methods of the above method embodiments can be applied to this network-side device embodiment and achieve the same technical effects.

[0285] Specifically, embodiments of this application also provide a network-side device. For example... Figure 8 As shown, the network device 800 includes an antenna 801, a radio frequency (RF) device 802, and a baseband device 803. The antenna 801 is connected to the RF device 802. In the uplink direction, the RF device 802 receives information through the antenna 801 and transmits the received information to the baseband device 803 for processing. In the downlink direction, the baseband device 803 processes the information to be transmitted and sends it to the RF device 802. The RF device 802 processes the received information and transmits it through the antenna 801.

[0286] The aforementioned frequency band processing device can be located in the baseband device 803. The method executed by the network-side device in the above embodiments can be implemented in the baseband device 803, which includes a processor 804 and a memory 805.

[0287] The baseband device 803 may, for example, include at least one baseband board on which multiple chips are disposed, such as... Figure 8 As shown, one of the chips, for example, is a processor 804, which is connected to a memory 805 to call the program in the memory 805 and execute the network device operations shown in the above method embodiment.

[0288] The baseband device 803 may also include a network interface 806 for exchanging information with the radio frequency device 802, such as a common public radio interface (CPRI).

[0289] Specifically, the network-side device in this embodiment of the invention further includes: instructions or programs stored in memory 805 and executable on processor 804, wherein processor 804 calls the instructions or programs in memory 805 to execute... Figure 4 The methods executed by each module shown achieve the same technical effect, and to avoid repetition, they will not be described in detail here.

[0290] This application embodiment also provides a terminal, including a processor and a communication interface. The processor is used to calculate channel prediction information, and the communication interface is used to report the channel prediction information to a network-side device. The channel prediction information is used to determine the configuration of the network-side device for performing channel prediction; wherein, the channel prediction configuration includes the configuration of an artificial intelligence network for performing channel prediction. This terminal embodiment corresponds to the above-described terminal-side method embodiment. All implementation processes and methods of the above method embodiments can be applied to this terminal embodiment and achieve the same technical effect. Specifically, Figure 9 A schematic diagram of the hardware structure of a terminal to implement an embodiment of this application.

[0291] The terminal 900 includes, but is not limited to, at least some of the following components: radio frequency unit 901, network module 902, audio output unit 903, input unit 904, sensor 905, display unit 906, user input unit 907, interface unit 908, memory 909, and processor 910.

[0292] Those skilled in the art will understand that the terminal 900 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 910 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 9 The terminal structure shown does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0293] It should be understood that, in this embodiment, the input unit 904 may include a graphics processing unit (GPU) 9041 and a microphone 9042. The GPU 9041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 906 may include a display panel 9061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 907 includes a touch panel 9071 and other input devices 9072. The touch panel 9071 is also called a touch screen. The touch panel 9071 may include a touch detection device and a touch controller. Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0294] In this embodiment, the radio frequency unit 901 receives downlink data from the network-side device and processes it for the processor 910; additionally, it sends uplink data to the network-side device. Typically, the radio frequency unit 901 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0295] The memory 909 can be used to store software programs or instructions and various data. The memory 909 may primarily include a program or instruction storage area and a data storage area. The program or instruction storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 909 may include high-speed random access memory and non-transient memory, wherein the non-transient memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. For example, at least one disk storage device, flash memory device, or other non-transient solid-state storage device.

[0296] Processor 910 may include one or more processing units; optionally, processor 910 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications or instructions, and the modem processor mainly handles wireless communication, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 910.

[0297] The processor 910 is used to determine channel prediction information.

[0298] Radio frequency unit 901 is used to report the channel prediction information to the network-side device, the channel prediction information being used to determine the configuration of the network-side device for performing channel prediction; wherein, the configuration of the channel prediction includes the configuration of the artificial intelligence network used to perform channel prediction.

[0299] Furthermore, the channel prediction information includes at least one of the following:

[0300] Channel change rate;

[0301] The level of the channel change rate.

[0302] Furthermore, the channel change rate includes at least one of the following:

[0303] The rate of change of temporal correlation;

[0304] The rate of change of the amplitude of a specific diameter;

[0305] The rate of change of the phase of the specific path;

[0306] The rate of change of the time delay of the specific path;

[0307] The rate of change of amplitude at a specific port;

[0308] The rate of change of the phase at the specific port;

[0309] The terminal's moving speed;

[0310] The rotational speed of the terminal;

[0311] The rate of change of the beam.

[0312] Furthermore, the rate of change of the time-domain correlation includes the rate of change of the target parameters of the U-shaped spectrum.

[0313] Furthermore, the specific path includes at least one of the following:

[0314] The diameter with the highest power;

[0315] The several diameters with the highest power;

[0316] The radius in which power is concentrated in the line-of-sight propagation direction;

[0317] The path whose power exceeds the first threshold.

[0318] Furthermore, the beam variation rate includes at least one of the following:

[0319] The rate of change of the target parameters of a specific beam;

[0320] The rate at which the beam is switched.

[0321] Furthermore, the specific beam includes at least one of the following:

[0322] The smallest set of control resources identifies the corresponding beam;

[0323] Control the beam corresponding to resource set 0;

[0324] The beam indicated by network-side equipment or base station.

[0325] Furthermore, the configuration of the channel prediction includes at least one of the following: the structure of the artificial intelligence network;

[0326] The parameters of the artificial intelligence network;

[0327] The input data of the artificial intelligence network;

[0328] The predicted time span;

[0329] Configuration of the reference signal;

[0330] Configuration of channel state information;

[0331] Configuration of the channel state information reporting;

[0332] The period for reporting channel status information;

[0333] The complexity of non-AI prediction algorithms.

[0334] As can be seen from the technical solutions of the above embodiments, the embodiments of this application can reasonably configure or switch the AI ​​network, use a more suitable AI network for channel prediction, use a complex network to track the channel when the channel changes rapidly to improve the accuracy of channel prediction, and use a relatively simple network for channel prediction when the channel changes slowly to reduce the complexity of the network, and can also reduce RS overhead and CSI overhead.

[0335] Furthermore, the processor 910 is configured to perform at least one of the following:

[0336] The terminal determines the channel prediction information based on the first instruction sent by the network-side device;

[0337] If the terminal does not receive the first instruction, it determines the channel prediction information according to the configured period.

[0338] The terminal determines the channel prediction information according to the protocol.

[0339] Furthermore, the content indicated by the first instruction includes at least one of the following:

[0340] The terminal is instructed to report or not report channel prediction information;

[0341] The correspondence between the channel prediction information and the channel prediction configuration;

[0342] The method for calculating the channel prediction information;

[0343] The terminal is used for time-frequency resource location for channel estimation.

[0344] Furthermore, the first instruction may take the form of at least one of the following:

[0345] Radio resource control signaling;

[0346] Media access control unit signaling;

[0347] Downlink control information.

[0348] Furthermore, the radio frequency unit 901 is also used to receive first configuration information sent by the network-side device, the first configuration information including configuration for determining the channel prediction information.

[0349] Furthermore, the first configuration information includes at least one of the following:

[0350] The correspondence between the channel prediction information and the channel prediction configuration;

[0351] The method for calculating the channel prediction information;

[0352] The terminal is used for time-frequency resource location for channel estimation.

[0353] As can be seen from the technical solutions of the above embodiments, the embodiments of this application can report channel prediction information in a timely manner, so that the network-side devices can make reasonable configurations or switches of the AI ​​network in a timely manner and use a more suitable AI network for channel prediction.

[0354] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described channel prediction method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0355] The processor mentioned above is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0356] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described channel prediction method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0357] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0358] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0359] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0360] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A channel prediction method characterized by, The method comprises: a network side device acquires channel prediction information from a terminal; the network side device determines a configuration for channel prediction according to the channel prediction information; wherein the configuration for channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction; wherein the channel prediction information comprises at least one of the following: a channel variation rate; a level of the channel variation rate; wherein the configuration for channel prediction comprises at least one of the following: a structure of the artificial intelligence network; a parameter of the artificial intelligence network; input data of the artificial intelligence network; a time span that can be predicted by the artificial intelligence network.

2. The method of claim 1, wherein, The channel variation rate comprises at least one of the following: a variation rate of a time domain correlation; a variation rate of an amplitude of a specific path; a variation rate of a phase of the specific path; a variation rate of a time delay of the specific path; a variation rate of an amplitude of a specific port; a variation rate of a phase of the specific port; a moving speed of the terminal; a rotating speed of the terminal; a variation rate of a beam.

3. The method of claim 2, wherein, The variation rate of the time domain correlation comprises: a variation rate of a target parameter of a U-shaped spectrum.

4. The method of claim 2, wherein, The specific path comprises at least one of the following: a path with the largest power; a plurality of paths with the largest power; a path with power concentrated in a line-of-sight propagation direction; a path with power exceeding a first threshold.

5. The method of claim 2, wherein, The variation rate of the beam comprises at least one of the following: a variation rate of a target parameter of a specific beam; a rate of switching beams.

6. The method of claim 5, wherein, The specific beam comprises at least one of the following: a beam corresponding to a smallest control resource set identifier; a beam corresponding to a control resource set CORESET 0; a beam indicated by a network side device or a base station.

7. The method of claim 1, wherein, The configuration for channel prediction further comprises at least one of the following: a configuration of a reference signal; a configuration of channel state information; a configuration of reporting of the channel state information; a period for reporting the channel state information; a complexity of a non-artificial intelligence prediction algorithm.

8. The method of claim 1, wherein, Before the acquiring of the channel prediction information from the terminal, the method further comprises: the network side device sends a first indication to the terminal, the first indication being used to instruct the terminal to determine the channel prediction information.

9. The method of claim 8, wherein, The content indicated by the first indication comprises at least one of the following: instructing the terminal to report or not to report the channel prediction information; a correspondence between the channel prediction information and the configuration for channel prediction; a calculation method of the channel prediction information; a time-frequency resource position used by the terminal for channel estimation.

10. The method of claim 8, wherein, Before the network side device sends the first indication to the terminal, the method further comprises: the network side device sends first configuration information to the terminal, the first configuration information comprising a configuration used for determining the channel prediction information.

11. The method of claim 10, wherein, The first configuration information comprises at least one of the following: a correspondence between the channel prediction information and the configuration for channel prediction; a calculation method of the channel prediction information; a time-frequency resource position used by the terminal for channel estimation.

12. A channel prediction device, characterized by, The method comprises: a transceiving module, configured to acquire channel prediction information from a terminal; a configuration module, configured to determine a configuration for channel prediction according to the channel prediction information; wherein the configuration for channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction; wherein the channel prediction information comprises at least one of the following: A channel variation rate; A level of the channel variation rate; The configuration of the channel prediction comprises at least one of: A structure of the artificial intelligence network; A parameter of the artificial intelligence network; Input data of the artificial intelligence network; A time span that the artificial intelligence network can predict.

13. A method of channel prediction, characterized by, Comprise: The terminal determines channel prediction information; The terminal reports the channel prediction information to the network side device, and the channel prediction information is used to determine the configuration of the network side device for channel prediction; wherein the configuration of the channel prediction comprises the configuration of the artificial intelligence network used for performing channel prediction; The channel prediction information comprises at least one of: A channel variation rate; A level of the channel variation rate; The configuration of the channel prediction comprises at least one of: A structure of the artificial intelligence network; A parameter of the artificial intelligence network; Input data of the artificial intelligence network; A time span that the artificial intelligence network can predict.

14. The method of claim 13, wherein, The channel variation rate comprises at least one of: A variation rate of a time domain correlation; A variation rate of an amplitude of a specific path; A variation rate of a phase of the specific path; A variation rate of a time delay of the specific path; A variation rate of an amplitude of a specific port; A variation rate of a phase of the specific port; A moving speed of the terminal; A rotating speed of the terminal; A variation rate of a beam.

15. The method of claim 14, wherein, The variation rate of the time domain correlation comprises: A variation rate of a target parameter of a U-shaped spectrum.

16. The method of claim 14, wherein, The specific path comprises at least one of: A path with the maximum power; A plurality of paths with the maximum power; A path with power concentrated in a line of sight propagation direction; A path with power exceeding a first threshold.

17. The method of claim 14, wherein, The variation rate of the beam comprises at least one of: A variation rate of a target parameter of a specific beam; A rate of switching beams.

18. The method of claim 17, wherein, The specific beam comprises at least one of: A beam corresponding to a minimum control resource set identifier; A beam corresponding to control resource set 0; A beam indicated by the network side device or the base station.

19. The method of claim 13, wherein, The configuration of the channel prediction further comprises at least one of: A configuration of a reference signal; A configuration of channel state information; A configuration of reporting of the channel state information; A period of reporting the channel state information; A complexity of a non-artificial intelligence prediction algorithm.

20. The method of claim 13, wherein, The terminal determines the channel prediction information, comprising at least one of: The terminal determines the channel prediction information according to a first indication sent by the network side device; The terminal determines the channel prediction information according to a configured period without receiving the first indication; The terminal determines the channel prediction information according to a protocol agreement.

21. The method of claim 20, wherein, The content indicated by the first indication comprises at least one of: Indicating whether the terminal reports or does not report the channel prediction information; A corresponding relationship between the channel prediction information and the configuration of the channel prediction; A calculation method of the channel prediction information; A time-frequency resource position used by the terminal for channel estimation.

22. The method of claim 20, wherein, Before the terminal determines the channel prediction information according to the first indication sent by the network side device, the method further comprises: The terminal receives first configuration information sent by the network side device, and the first configuration information comprises a configuration used to determine the channel prediction information.

23. The method of claim 22, wherein, The first configuration information comprises at least one of: A corresponding relationship between the channel prediction information and a configuration of channel prediction; A calculation method of the channel prediction information; A time-frequency resource position used by the terminal for channel estimation.

24. A channel prediction apparatus, characterized by comprising: The method comprises the following steps: A calculation module is configured to determine channel prediction information; A reporting module is configured to report the channel prediction information to a network side device, wherein the channel prediction information is used to determine a configuration of channel prediction of the network side device; and the configuration of channel prediction comprises a configuration of an artificial intelligence network used for performing channel prediction. The channel prediction information comprises at least one of the following: A channel variation rate; A level of the channel variation rate. The configuration of channel prediction comprises at least one of the following: A structure of the artificial intelligence network; A parameter of the artificial intelligence network; Input data of the artificial intelligence network; A time span that can be predicted by the artificial intelligence network.

25. A network-side device, comprising: A processor, a memory, and a program or instruction stored in the memory and executable on the processor are included, and the program or instruction is executed by the processor to implement the steps of the channel prediction method according to any one of claims 1 to 11.

26. A terminal, characterized by A processor, a memory, and a program or instruction stored in the memory and executable on the processor are included, and the program or instruction is executed by the processor to implement the steps of the channel prediction method according to any one of claims 13 to 23.

27. A readable storage medium characterized by, A program or instruction is stored in the readable storage medium, and the program or instruction is executed by the processor to implement the channel prediction method according to any one of claims 1 to 11, or to implement the steps of the channel prediction method according to any one of claims 13 to 23.

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