Channel prediction method, communication device and storage medium

Abstract

The application provides a channel prediction method, a communication device and a storage medium. The method comprises the following steps: acquiring Doppler frequency information and complex amplitude information of a channel; and performing channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information. The embodiment of the application can improve the transmission performance between devices.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a channel prediction method, communication device and storage medium. Background Technology

[0002] In some communication scenarios, terminals may move at high speeds. When terminals move at high speeds, the wireless channel between the terminal and network devices changes significantly. However, current channel estimation mainly estimates the channel information at the current moment, which cannot adapt to significant changes in the wireless channel, resulting in poor transmission performance between devices. Summary of the Invention

[0003] This application provides a channel prediction method, a communication device, and a storage medium to address the problem of poor transmission performance between devices.

[0004] This application provides a channel prediction method, including:

[0005] Acquire the Doppler frequency information and complex amplitude information of the channel;

[0006] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information and the complex amplitude information.

[0007] Optionally, the method further includes:

[0008] Obtain the delay information of the channel;

[0009] The channel prediction based on the Doppler frequency information and the complex amplitude information, to obtain channel prediction information, includes:

[0010] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information.

[0011] Optionally, the Doppler frequency information includes:

[0012] The Doppler frequency of each sub-path in at least one multipath cluster of the channel;

[0013] The complex amplitude information includes:

[0014] The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

[0015] Optionally, acquiring the Doppler frequency information and complex amplitude information of the channel includes:

[0016] Determine the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters;

[0017] Estimate the Doppler frequency information of each sub-path in the nth multipath cluster;

[0018] Based on the Doppler frequency information of each sub-path in the nth multipath cluster, the complex amplitude of each sub-path in the nth multipath cluster is calculated.

[0019] Optionally, determining the number of sub-paths of the nth multipath cluster includes:

[0020] Obtain the time correlation matrix of the nth multipath cluster;

[0021] Based on the time correlation matrix, calculate the channel feature vector of the nth multipath cluster;

[0022] The number of sub-paths corresponding to the channel feature vector of the nth multipath cluster is calculated using the eigenvalue ratio method, thus obtaining the number of sub-paths of the nth multipath cluster.

[0023] Optionally, obtaining the time correlation matrix of the nth multipath cluster includes:

[0024] Obtain the frequency correlation matrix of the channel;

[0025] Based on the frequency correlation matrix, calculate the channel eigenvalue vector of the channel;

[0026] The number of multipath clusters corresponding to the channel feature vector of the channel is calculated using the eigenvalue ratio method, and the number of multipath clusters L of the channel is obtained, where L is an integer greater than 1.

[0027] Determine the time delay of the L multipath clusters;

[0028] Based on the time delay of the L multipath clusters, the complex amplitude of the L multipath clusters at multiple times is calculated to obtain the complex amplitude correlation matrix of the L multipath clusters at the multiple times.

[0029] The time correlation matrix of the nth multipath cluster is obtained from the complex amplitude correlation matrix.

[0030] Optionally, the at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the at least one multipath cluster; or

[0031] The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

[0032] Optionally, the feature value corresponding to the channel feature value vector of the at least one multipath cluster is greater than a preset threshold.

[0033] This application also provides a communication device, including: a memory, a transceiver, and a processor, wherein:

[0034] The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and perform the following operations:

[0035] Acquire the Doppler frequency information and complex amplitude information of the channel;

[0036] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information and the complex amplitude information.

[0037] Optionally, the processor is further configured to read the computer program in the memory and perform the following operations:

[0038] Obtain the delay information of the channel;

[0039] The channel prediction based on the Doppler frequency information and the complex amplitude information, to obtain channel prediction information, includes:

[0040] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information.

[0041] Optionally, the Doppler frequency information includes:

[0042] The Doppler frequency of each sub-path in at least one multipath cluster of the channel;

[0043] The complex amplitude information includes:

[0044] The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

[0045] Optionally, the processor is specifically configured to read the computer program in the memory and perform the following operations:

[0046] Determine the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters;

[0047] Estimate the Doppler frequency information of each sub-path in the nth multipath cluster;

[0048] Based on the Doppler frequency information of each sub-path in the nth multipath cluster, the complex amplitude of each sub-path in the nth multipath cluster is calculated.

[0049] Optionally, the processor is specifically configured to read the computer program in the memory and perform the following operations, including:

[0050] Obtain the time correlation matrix of the nth multipath cluster;

[0051] Based on the time correlation matrix, calculate the channel feature vector of the nth multipath cluster;

[0052] The number of sub-paths corresponding to the channel feature vector of the nth multipath cluster is calculated using the eigenvalue ratio method, thus obtaining the number of sub-paths of the nth multipath cluster.

[0053] Optionally, the processor is specifically configured to read the computer program in the memory and perform the following operations:

[0054] Obtain the frequency correlation matrix of the channel;

[0055] Based on the frequency correlation matrix, calculate the channel eigenvalue vector of the channel;

[0056] The number of multipath clusters corresponding to the channel feature vector of the channel is calculated using the eigenvalue ratio method, and the number of multipath clusters L of the channel is obtained, where L is an integer greater than 1.

[0057] Determine the time delay of the L multipath clusters;

[0058] Based on the time delay of the L multipath clusters, the complex amplitude of the L multipath clusters at multiple times is calculated to obtain the complex amplitude correlation matrix of the L multipath clusters at the multiple times.

[0059] The time correlation matrix of the nth multipath cluster is obtained from the complex amplitude correlation matrix.

[0060] Optionally, the at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the subset; or

[0061] The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

[0062] Optionally, the feature value corresponding to the channel feature value vector of the at least one multipath cluster is greater than a preset threshold.

[0063] This application also provides a communication device, including:

[0064] The first acquisition unit is used to acquire the Doppler frequency information and complex amplitude information of the channel;

[0065] The estimation unit is used to perform channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information.

[0066] Optionally, the communication device further includes:

[0067] The second acquisition unit is used to acquire the delay information of the channel;

[0068] The estimation unit performs channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information, including:

[0069] The estimation unit performs channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information to obtain channel prediction information.

[0070] Optionally, the Doppler frequency information includes:

[0071] The Doppler frequency of each sub-path in at least one multipath cluster of the channel;

[0072] The complex amplitude information includes:

[0073] The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

[0074] Optionally, the first acquisition unit acquires the Doppler frequency information and complex amplitude information of the channel, including:

[0075] The first acquisition unit determines the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters;

[0076] The first acquisition unit estimates the Doppler frequency information of each sub-path in the nth multipath cluster;

[0077] The first acquisition unit calculates the complex amplitude of each sub-path in the nth multipath cluster based on the Doppler frequency information of each sub-path in the nth multipath cluster.

[0078] Optionally, the first acquisition unit determines the number of sub-paths of the nth multipath cluster, including:

[0079] The first acquisition unit acquires the time correlation matrix of the nth multipath cluster;

[0080] The first acquisition unit calculates the channel feature vector of the nth multipath cluster based on the time correlation matrix;

[0081] The first acquisition unit uses the eigenvalue ratio method to calculate the number of sub-paths corresponding to the channel eigenvalue vector of the nth multipath cluster, thereby obtaining the number of sub-paths of the nth multipath cluster.

[0082] Optionally, the first acquisition unit acquires the time correlation matrix of the nth multipath cluster, including:

[0083] The first acquisition unit acquires the frequency correlation matrix of the channel;

[0084] The first acquisition unit calculates the channel feature vector of the channel based on the frequency correlation matrix;

[0085] The first acquisition unit uses the eigenvalue ratio method to calculate the number of multipath clusters corresponding to the channel eigenvalue vector of the channel, and obtains the number of multipath clusters L of the channel, where L is an integer greater than 1;

[0086] The first acquisition unit determines the time delay of the L multipath clusters;

[0087] The first acquisition unit calculates the complex amplitude of the L multipath clusters at multiple times based on the time delay of the L multipath clusters, and obtains the complex amplitude correlation matrix of the L multipath clusters at the multiple times.

[0088] The first acquisition unit obtains the time correlation matrix of the nth multipath cluster from the complex amplitude correlation matrix.

[0089] Optionally, the at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the at least one multipath cluster; or

[0090] The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

[0091] Optionally, the at least one multipath cluster has a eigenvalue corresponding to the channel eigenvalue vector of the channel that is greater than a preset threshold. This application also provides a processor-readable storage medium storing a computer program for causing the processor to execute the channel prediction method provided in this application.

[0092] In this embodiment, Doppler frequency information and complex amplitude information of the channel are obtained; channel prediction is performed based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information. This enables channel prediction, thereby adapting to significant changes in the wireless channel and improving transmission performance between devices. Attached Figure Description

[0093] Figure 1 This is a schematic diagram of the network architecture applicable to the implementation of this application;

[0094] Figure 2 This is a flowchart of a channel prediction method provided in an embodiment of this application;

[0095] Figure 3 This is a schematic diagram illustrating a complex amplitude information estimation method provided in an embodiment of this application;

[0096] Figure 4This is a schematic diagram illustrating another method for estimating complex amplitude information provided in an embodiment of this application;

[0097] Figure 5 This application provides a structural diagram of a communication device;

[0098] Figure 6 This application provides a structural diagram of another communication device. Detailed Implementation

[0099] To make the technical problems, technical solutions and advantages of this application clearer, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0100] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0101] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0102] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0103] This application provides a channel prediction method, a communication device, and a storage medium to address the problem of poor transmission performance between devices.

[0104] The method and apparatus are based on the same concept of the application. Since the methods and apparatus solve problems in similar ways, the implementation of the apparatus and methods can refer to each other, and the repeated parts will not be described again.

[0105] The technical solutions provided in this application can be applied to various systems, especially 6G systems. For example, applicable systems include Global System for Mobile Communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Long Term Evolution Advanced (LTE-A), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), 5G New Radio (NR), and 6G systems. All of these systems include terminal equipment and network equipment. The systems may also include a core network component, such as Evolved Packet System (EPS) and 5G systems (5GS).

[0106] Please see Figure 1 , Figure 1 This is a schematic diagram of the network architecture applicable to the implementation of this application, such as... Figure 1 As shown, it includes terminal 11 and network device 12.

[0107] The terminal involved in this application embodiment can be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The name of the terminal device may differ in different systems; for example, in a 5G system, the terminal device can be called User Equipment (UE). The wireless terminal device can communicate with one or more core networks (CNs) via a Radio Access Network (RAN). The wireless terminal device can be a mobile terminal device, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal device, for example, a portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile device. These exchange voice and / or data with the RAN. Examples include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), Redcap terminals, and other devices. Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, or user device, but this application does not limit the terminology.

[0108] The network device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, a base station may also be called an access point, or a device in an access network that communicates with a wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network equipment involved in the embodiments of this application can be a base transceiver station (BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), a NodeB in a Wide-band Code Division Multiple Access (WCDMA) network, an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, a base station in 6G, a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of this application. In some network structures, the network equipment may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and distributed unit may also be geographically separated.

[0109] Network devices and terminals can each use one or more antennas for Multiple-Input Multiple-Output (MIMO) transmission. MIMO transmission can be Single-User MIMO (SU-MIMO) or Multiple-User MIMO (MU-MIMO). Depending on the configuration and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, and can also be diversity transmission, precoding transmission, or beamforming transmission, etc.

[0110] Please see Figure 2 , Figure 2 This is a flowchart of a channel prediction method provided in an embodiment of this application, such as... Figure 2 As shown, it includes the following steps:

[0111] Step 201: Obtain the Doppler frequency information and complex amplitude information of the channel;

[0112] Step 202: Perform channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information.

[0113] The Doppler frequency information and complex amplitude information of the aforementioned channel can be the Doppler frequency information and complex amplitude information of the channel at the current moment, or it can be the Doppler frequency information and complex amplitude information of the channel at multiple moments.

[0114] The channel prediction based on the Doppler frequency information and the complex amplitude information described above can be used to predict channel prediction information at a future time, for example, predicting channel prediction information at time t+Δt, where t is the current time and Δt is the time change. Specifically, it can predict channel prediction information at any future time.

[0115] In this embodiment of the application, the above steps can be used to predict the channel, thereby adapting to significant changes in the wireless channel and improving the transmission performance between devices.

[0116] It should be noted that the methods provided in the embodiments of this application can be executed by a communication device, which may be a network device or a terminal.

[0117] As an optional implementation, the method further includes:

[0118] Obtain the delay information of the channel;

[0119] The channel prediction based on the Doppler frequency information and the complex amplitude information, to obtain channel prediction information, includes:

[0120] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information.

[0121] The delay information of the aforementioned channel can be the delay information of the multipath cluster of the aforementioned channel.

[0122] In this embodiment, the channel prediction described above can be performed using the following formula to obtain the channel prediction information:

[0123]

[0124] in, This represents the channel prediction information at time t+Δt, where f is the frequency within the bandwidth, Δt is the time variation, N is the number of multipath clusters in the channel, M is the number of subpaths in each multipath cluster, and α... n,m f is the complex amplitude of the m-th sub-path in the n-th multipath cluster. d,n,m Let τ be the Doppler frequency of the m-th sub-path in the n-th multipath cluster. n Let be the time delay of the nth multipath cluster.

[0125] The above formula can be used to predict the channel at any future time.

[0126] It should be noted that this disclosure does not limit channel prediction to the above formula, but only to the complex amplitude and Doppler frequency of a portion of the multipath clusters. That is, it does not require channel prediction based on the complex amplitude and Doppler frequency of each sub-path in each of the N multipath clusters as in the above formula, but rather based on the complex amplitude and Doppler frequency of a portion of the multipath clusters.

[0127] In this embodiment, since channel prediction is performed based on the Doppler frequency information, the complex amplitude information, and the time delay information, the accuracy of the channel prediction information can be improved.

[0128] It should be noted that in some implementations, time delay can be disregarded, and channel prediction can be directly performed based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information. For example, in some implementations, the time delay τ in the above formula can be... n A related factor is converted to the intermediate complex amplitude α. n,m In the above formula, if exp(-j2πfτ) is not calculated... n This item.

[0129] As an optional implementation, the Doppler frequency information includes:

[0130] The Doppler frequency of each sub-path in at least one multipath cluster of the channel;

[0131] The complex amplitude information includes:

[0132] The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

[0133] Among them, at least one multipath cluster can be all the multipath clusters of the above channel, or it can be a part of the multipath clusters of the above channel, such as a part of the multipath clusters with energy higher than a preset threshold.

[0134] In this implementation, the computational complexity can be reduced because it supports channel prediction based on the Doppler frequency and complex amplitude of at least one multipath cluster.

[0135] Optionally, acquiring the Doppler frequency information and complex amplitude information of the channel includes:

[0136] Determine the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters;

[0137] Estimate the Doppler frequency information of each sub-path in the nth multipath cluster;

[0138] Based on the Doppler frequency information of each sub-path in the nth multipath cluster, the complex amplitude of each sub-path in the nth multipath cluster is calculated.

[0139] The number of sub-paths in the nth multipath cluster can be pre-configured or calculated based on the time correlation matrix of the nth multipath cluster.

[0140] The Doppler frequency information of each sub-path in the nth multipath cluster can be estimated by using the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm.

[0141] The above calculation of the complex amplitude of each sub-path in the nth multipath cluster based on the Doppler frequency information of each sub-path can be achieved by using the maximum likelihood principle to calculate the complex amplitude corresponding to the Doppler frequency information of each sub-path, thus obtaining the complex amplitude of each sub-path in the nth multipath cluster.

[0142] In this embodiment, since the nth multipath cluster is any one of the at least one multipath clusters, it is possible to calculate the complex amplitude and Doppler frequency of all sub-paths of the at least one multipath cluster.

[0143] Optionally, determining the number of sub-paths of the nth multipath cluster includes:

[0144] Obtain the time correlation matrix of the nth multipath cluster;

[0145] Based on the time correlation matrix, calculate the channel feature vector of the nth multipath cluster;

[0146] The number of sub-paths corresponding to the channel feature vector of the nth multipath cluster is calculated using the eigenvalue ratio method, thus obtaining the number of sub-paths of the nth multipath cluster.

[0147] The time correlation matrix of the nth multipath cluster can be pre-configured or calculated based on the frequency correlation matrix of the channel.

[0148] The above-mentioned calculation of the channel eigenvalue vector of the nth multipath cluster based on the time correlation matrix can be performed by performing matrix conjugate rearrangement on the time correlation matrix to obtain the conjugate rearranged time correlation matrix, and then performing eigenvalue decomposition on the time correlation matrix to obtain the channel eigenvalue vector of the nth multipath cluster.

[0149] For example, the time correlation matrix of the nth multipath cluster mentioned above can be represented as follows:

[0150]

[0151] Where Ψ1 is the time correlation matrix of the nth multipath cluster mentioned above, and N t N represents the number of channel groups used at different times. win For a time-sliding window, N win Not exceeding 0.5 (N) t -1), g j For N t The j-th sliding window in the frequency domain channel estimation results at time n. G represents j The conjugate transpose of .

[0152] After obtaining Ψ1, performing matrix conjugate rearrangement can expand the original sample by a factor of 2, i.e.

[0153]

[0154] Among them, R f J represents the average correlation matrix after conjugate rearrangement, where J is a matrix of dimension N. win ×N win A square matrix whose anti-diagonal elements are 1s and all other elements are 0s, Ψ1 * This represents the conjugate of Ψ1.

[0155] Using the ESPRIT algorithm, on R f Perform eigenvalue decomposition to calculate the channel eigenvector V of the nth multipath cluster. f Its dimension is N win×N win The eigenvalue corresponding to each eigenvector is λ. i i = 0, 1, ..., N win -1. The estimated number of sub-paths of the nth multipath cluster is obtained using the eigenvalue ratio method, specifically through the following formula:

[0156] for i = 0, 1, ..., N win -2

[0157]

[0158] end

[0159]

[0160] Among them, the estimated number of sub-paths of the nth multipath cluster. for The maximum value in The index.

[0161] The above implementation method can accurately estimate the number of sub-paths in each multipath cluster.

[0162] Optionally, obtaining the time correlation matrix of the nth multipath cluster includes:

[0163] Obtain the frequency correlation matrix of the channel;

[0164] Based on the frequency correlation matrix, calculate the channel eigenvalue vector of the channel;

[0165] The number of multipath clusters corresponding to the channel feature vector of the channel is calculated using the eigenvalue ratio method, and the number of multipath clusters L of the channel is obtained, where L is an integer greater than 1.

[0166] Determine the time delay of the L multipath clusters;

[0167] Based on the time delay of the L multipath clusters, the complex amplitude of the L multipath clusters at multiple times is calculated to obtain the complex amplitude correlation matrix of the L multipath clusters at the multiple times.

[0168] The time correlation matrix of the nth multipath cluster is obtained from the complex amplitude correlation matrix.

[0169] The frequency correlation matrix of the channel described above can be generated based on the frequency domain channel estimation results of multiple pre-acquired sets of uplink channel sounding reference signals (SRS). In some embodiments, to suppress noise and improve estimation accuracy when obtaining the channel's frequency correlation matrix, matrix conjugate rearrangement and moving average processing can be used. Specifically, it can be as follows:

[0170] Assume that the frequency domain channel estimation result of a single SRS includes N f For each frequency point, the channel estimation vector is used in the frequency domain with an N-value. win A sliding window of length, where N is generally win It can not exceed 0.5 (N) f -1) Calculate the correlation matrix corresponding to the frequency domain channel estimation vector of the sliding window sequentially, and average the correlation matrices of all sliding windows to obtain the average correlation matrix Ψ2:

[0171]

[0172] Among them, g j For N f The frequency domain channel estimation vector within the j-th sliding window of the frequency domain channel estimation results at each frequency point. G represents j The conjugate transpose of Ψ2. After obtaining Ψ2, matrix conjugate rearrangement can expand the original sample by a factor of 2, i.e.:

[0173]

[0174] Among them, R f J represents the average correlation matrix after conjugate rearrangement, where J is a matrix of dimension N. win ×N win A square matrix whose anti-diagonal elements are 1s and all other elements are 0s, Ψ2 * This represents the conjugate of Ψ2.

[0175] Then, using the ESPRIT algorithm, R... f Perform eigenvalue decomposition to calculate the eigenvector V of the channel. f Its dimension is N win ×N win The eigenvalue corresponding to each eigenvector is λ. i i = 0, 1, ..., N win -1. The number of multipath clusters in the channel is estimated using the eigenvalue ratio method, specifically using the following formula:

[0176] for i = 0, 1, ..., N win -2

[0177]

[0178] end

[0179]

[0180] Among them, the estimated value of the channel multipath number for The maximum value in The index.

[0181] The time delay of the L multipath clusters mentioned above can be determined by using the principle of subspace rotation invariance under the equal-interval distribution of SRS resources in the frequency domain to determine the time delay τ of all multipath clusters in the channel. n n = 0, 1, ..., L-1.

[0182] The calculation of the complex amplitude of the L multipath clusters at multiple times based on their time delays can be achieved by using the maximum likelihood principle to calculate the complex amplitude time series of the channel multipath clusters. For example, the time delay τ obtained above can be used. n By constructing the Fourier transform matrix and its pseudo-inverse matrix, the complex amplitude time series of each time delay path is obtained based on maximum likelihood estimation. Specifically, it can be done as follows:

[0183] According to the SRS pilot resource frequency vector f configured by the system SRS and the obtained time delay path time delay vector τ n Generate the Fourier transformation matrix F (with dimension N). f ×L)

[0184]

[0185] Where L represents the total number of multi-delay clusters in the channel, f SRS The vector contains elements f k k = 1, 2, 3, ..., N f N f The number of frequency points described above.

[0186] Calculate its pseudo-inverse matrix for:

[0187]

[0188] Among them, F H Denotes the conjugate transpose of F, (F H F) -1 Indicates the relationship between (F) H F) Inverse the equation. Then the complex amplitude of all multipath groups can be expressed as A. L :

[0189]

[0190] in, This represents the channel estimate for all frequency points.

[0191] It should be noted that the above A L Representing the complex amplitude at a given moment, in the above implementation, multiple sets of A can be calculated using multiple sets of channel estimates at different moments according to the above process. Ldenoted as the complex amplitude correlation matrix Where, N t This represents the number of channel groups used at different times.

[0192] The time correlation matrix of the nth multipath cluster mentioned above can be derived from the complex amplitude correlation matrix. Extract the correlation vector of the nth multipath cluster to obtain the time correlation matrix of the nth multipath cluster.

[0193] Optionally, in this embodiment, the complex amplitude of each sub-path in the nth multipath cluster can also be calculated in a similar manner as described above, that is, the complex amplitude of the nth multipath cluster is obtained based on maximum likelihood estimation.

[0194] It should be noted that in this embodiment, the ESPRIT algorithm can be used for Doppler estimation to accurately determine the Doppler frequency of each multipath cluster in the channel. Furthermore, since the time delay and Doppler frequency can be considered unchanged over a short period, the pseudo-inverse matrix of the Fourier transform matrix used in the maximum likelihood estimation process can be saved during the estimation of time delay and Doppler frequency. Subsequent estimation of the complex amplitude of each sub-path in each multipath cluster only requires matrix multiplication, thus reducing the computational load.

[0195] Optionally, the at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the at least one multipath cluster; or

[0196] The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

[0197] When at least one of the aforementioned multipath clusters is all of the L multipath clusters, the complex amplitude of each sub-path in the L multipath clusters can be calculated as follows: Figure 3 As shown, it may include the following steps:

[0198] The channel frequency correlation matrix is ​​obtained by using matrix conjugate rearrangement and moving average processing;

[0199] The number of multipath clusters L is determined using the eigenvalue ratio method. This step can be based on the channel eigenvalue vector obtained when calculating the channel subspace using EVD in the ESPRIT algorithm, and the number of multipath clusters L is calculated using the eigenvalue ratio method.

[0200] Based on the principle of subspace rotation invariance under the equal-interval distribution of SRS resources in the frequency domain, the channel multipath delay is finally accurately calculated.

[0201] After estimating the time delay, the complex amplitude time series of the channel multipath cluster can be obtained using the maximum likelihood principle;

[0202] For each multipath cluster (also known as a multipath time delay cluster), matrix conjugate rearrangement is used to obtain the time correlation matrix of each multipath cluster and determine the number of sub-paths of the multipath cluster. Then, the ESPRIT algorithm is used to estimate the Doppler spectrum of the sub-paths. Finally, the maximum likelihood principle is used to estimate the complex amplitude of each sub-path to obtain the complex amplitude of each sub-path in all multipath clusters.

[0203] The Doppler estimation method described above can be reused repeatedly. Specifically, based on the principle of subspace rotation invariance under equal time interval sampling of the complex amplitude of the multipath cluster, the Doppler frequency of each multipath delay cluster in the channel can be accurately determined. After obtaining the Doppler frequency of a certain cluster, the complex amplitude of each sub-path within the delay cluster is obtained again based on the maximum likelihood principle.

[0204] When at least one of the multipath clusters is a part of the L multipath clusters, the feature value of the at least one multipath cluster corresponding to the channel feature value vector of the channel can be greater than a preset threshold.

[0205] In the implementation where at least one multipath cluster is a subset of the L multipath clusters, the computational load can be reduced while maintaining the accuracy of channel prediction information. This is because, in practice, it has been found that the number of sub-paths contained in different delay clusters is often unequal, and most high-power multipath clusters contain very few sub-paths (e.g., a LOS delay cluster often contains only one direct path). Furthermore, the power of each sub-path within a multipath cluster is often unequal, with most power concentrated in one or two sub-paths. Therefore, channel prediction based on at least one multipath cluster can also ensure the accuracy of channel prediction information.

[0206] For example: the eigenvalues ​​λ of each multipath cluster can be used... i For each i = 0, 1, ..., L-1, a threshold decision is made. The threshold can be an empirical value. Paths greater than the threshold are considered to be multipath clusters with higher energy. The number of multipath clusters with higher energy can be denoted as L. main Generally L main The value is relatively small, approximately 1 to 2.

[0207] In this implementation, it can be as follows: Figure 4 As shown, it may include the following steps:

[0208] The channel frequency correlation matrix is ​​obtained by using matrix conjugate rearrangement and moving average processing;

[0209] The number of multipath clusters L is determined using the eigenvalue ratio method. This step can be based on the channel eigenvalue vector obtained when calculating the channel subspace using EVD in the ESPRIT algorithm, and the number of multipath clusters L is calculated using the eigenvalue ratio method.

[0210] Based on the principle of subspace rotation invariance under the equal-interval distribution of SRS resources in the frequency domain, the channel multipath delay is finally accurately calculated.

[0211] After estimating the time delay, the complex amplitude time series of the channel multipath cluster can be obtained using the maximum likelihood principle;

[0212] For at least one multipath cluster, matrix conjugate rearrangement is used to obtain the time correlation matrix of the multipath cluster and determine the number of sub-paths of the multipath cluster. Then, the ESPRIT algorithm is used to estimate the Doppler spectrum of the sub-paths. Finally, the maximum likelihood principle is used to estimate the complex amplitude of each sub-path to obtain the complex amplitude of each sub-path in at least one multipath cluster.

[0213] In this implementation, the Doppler frequencies of all sub-paths of the channel can be estimated at once, and compared to... Figure 3 The process shown remains unchanged for estimating channel delay. After obtaining the complex amplitude matrix of the multipath clusters, since most of the energy of each multipath cluster is concentrated on 1 to 2 subpaths, the overall number of strong subpaths is not large. Therefore, we can skip Doppler spectrum estimation for each multipath cluster and instead sum the complex amplitudes of the strong multipath clusters to obtain the matrix.

[0214]

[0215] Then, multiple sets of A at different times... sum Doppler estimation is performed to obtain the Doppler spectra of each sub-path of a multipath cluster with higher energy. Generally, the results are very close to the Doppler spectra of all sub-paths of all multipath clusters. Compared to... Figure 3 Algorithm flow, Figure 4 The complexity of the algorithm shown can be further reduced. In this embodiment, the Doppler frequency information and complex amplitude information of the channel are obtained; channel prediction is performed based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information. This enables channel prediction, thereby adapting to significant changes in the wireless channel and improving the transmission performance between devices.

[0216] Please see Figure 5 , Figure 5 This is a structural diagram of a communication device provided in an embodiment of this application, such as... Figure 5 As shown, it includes a memory 520, a transceiver 500, and a processor 510:

[0217] The memory 520 is used to store computer programs; the transceiver 500 is used to send and receive data under the control of the processor 510; the processor 510 is used to read the computer program in the memory 520 and perform the following operations:

[0218] Acquire the Doppler frequency information and complex amplitude information of the channel;

[0219] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information and the complex amplitude information.

[0220] Among them, Figure 5 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 510 and memory represented by memory 520 together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 500 can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium, including wireless channels, wired channels, optical fibers, etc. For different user equipment, the user interface 530 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0221] Processor 510 is responsible for managing the bus architecture and general processing, while memory 520 can store data used by processor 500 when performing operations.

[0222] Optionally, the processor 510 can be a CPU (Central Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or CPLD (Complex Programmable Logic Device), and the processor can also adopt a multi-core architecture.

[0223] The processor executes any of the methods described in the embodiments of this application by invoking a computer program stored in memory, according to the obtained executable instructions. The processor and memory may also be physically separated.

[0224] Optionally, the processor 510 is also used for:

[0225] Obtain the delay information of the channel;

[0226] The channel prediction based on the Doppler frequency information and the complex amplitude information, to obtain channel prediction information, includes:

[0227] Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information.

[0228] Optionally, the Doppler frequency information includes:

[0229] The Doppler frequency of each sub-path in at least one multipath cluster of the channel;

[0230] The complex amplitude information includes:

[0231] The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

[0232] Optionally, the processor 510 is specifically configured to read the computer program in the memory and perform the following operations:

[0233] Determine the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters;

[0234] Estimate the Doppler frequency information of each sub-path in the nth multipath cluster;

[0235] Based on the Doppler frequency information of each sub-path in the nth multipath cluster, the complex amplitude of each sub-path in the nth multipath cluster is calculated.

[0236] Optionally, the processor 510 is specifically configured to read the computer program in the memory and perform the following operations:

[0237] Obtain the time correlation matrix of the nth multipath cluster;

[0238] Based on the time correlation matrix, calculate the channel feature vector of the nth multipath cluster;

[0239] The number of sub-paths corresponding to the channel feature vector of the nth multipath cluster is calculated using the eigenvalue ratio method, thus obtaining the number of sub-paths of the nth multipath cluster.

[0240] Optionally, the processor 510 is specifically configured to read the computer program in the memory and perform the following operations:

[0241] Obtain the frequency correlation matrix of the channel;

[0242] Based on the frequency correlation matrix, calculate the channel eigenvalue vector of the channel;

[0243] The number of multipath clusters corresponding to the channel feature vector of the channel is calculated using the eigenvalue ratio method, and the number of multipath clusters L of the channel is obtained, where L is an integer greater than 1.

[0244] Determine the time delay of the L multipath clusters;

[0245] Based on the time delay of the L multipath clusters, the complex amplitude of the L multipath clusters at multiple times is calculated to obtain the complex amplitude correlation matrix of the L multipath clusters at the multiple times.

[0246] The time correlation matrix of the nth multipath cluster is obtained from the complex amplitude correlation matrix.

[0247] Optionally, the at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the at least one multipath cluster; or

[0248] The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

[0249] Optionally, the feature value corresponding to the channel feature value vector of the at least one multipath cluster is greater than a preset threshold.

[0250] It should be noted that the communication device provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0251] Please see Figure 6 , Figure 6 This is a structural diagram of another communication device provided in an embodiment of this application, such as... Figure 6 As shown, the communication device 600 includes:

[0252] The first acquisition unit 601 is used to acquire the Doppler frequency information and complex amplitude information of the channel;

[0253] The estimation unit 602 is used to perform channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information.

[0254] Optionally, the communication device further includes:

[0255] The second acquisition unit is used to acquire the delay information of the channel;

[0256] The estimation unit 602 performs channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information, including:

[0257] The estimation unit 602 performs channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information to obtain channel prediction information.

[0258] Optionally, the Doppler frequency information includes:

[0259] The Doppler frequency of each sub-path in at least one multipath cluster of the channel;

[0260] The complex amplitude information includes:

[0261] The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

[0262] Optionally, the first acquisition unit 601 acquires the Doppler frequency information and complex amplitude information of the channel, including:

[0263] The first acquisition unit 601 determines the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters;

[0264] The first acquisition unit 601 estimates the Doppler frequency information of each sub-path in the nth multipath cluster;

[0265] The first acquisition unit 601 calculates the complex amplitude of each sub-path in the nth multipath cluster based on the Doppler frequency information of each sub-path in the nth multipath cluster.

[0266] Optionally, the first acquisition unit 601 determines the number of sub-paths of the nth multipath cluster, including:

[0267] The first acquisition unit 601 acquires the time correlation matrix of the nth multipath cluster;

[0268] The first acquisition unit 601 calculates the channel feature vector of the nth multipath cluster based on the time correlation matrix;

[0269] The first acquisition unit 601 uses the eigenvalue ratio method to calculate the number of sub-paths corresponding to the channel eigenvalue vector of the nth multipath cluster, thereby obtaining the number of sub-paths of the nth multipath cluster.

[0270] Optionally, the first acquisition unit 601 acquires the time correlation matrix of the nth multipath cluster, including:

[0271] The first acquisition unit 601 acquires the frequency correlation matrix of the channel;

[0272] The first acquisition unit 601 calculates the channel feature vector of the channel based on the frequency correlation matrix;

[0273] The first acquisition unit 601 uses the eigenvalue ratio method to calculate the number of multipath clusters corresponding to the channel eigenvalue vector of the channel, and obtains the number of multipath clusters L of the channel, where L is an integer greater than 1.

[0274] The first acquisition unit 601 determines the time delay of the L multipath clusters;

[0275] The first acquisition unit 601 calculates the complex amplitude of the L multipath clusters at multiple times based on the time delay of the L multipath clusters, and obtains the complex amplitude correlation matrix of the L multipath clusters at the multiple times.

[0276] The first acquisition unit 601 acquires the time correlation matrix of the nth multipath cluster from the complex amplitude correlation matrix.

[0277] Optionally, the at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the at least one multipath cluster; or

[0278] The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

[0279] Optionally, the feature value corresponding to the channel feature value vector of the at least one multipath cluster is greater than a preset threshold.

[0280] It should be noted that the communication device provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0281] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0282] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0283] This application also provides a processor-readable storage medium storing a computer program for causing the processor to execute the channel prediction method provided in this application.

[0284] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0285] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0286] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0287] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0288] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0289] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A channel prediction method, characterized in that, include: Acquire the Doppler frequency information and complex amplitude information of the channel; Channel prediction is performed based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information; The Doppler frequency information includes: The Doppler frequency of each sub-path in at least one multipath cluster of the channel; The complex amplitude information includes: The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

2. The method as described in claim 1, characterized in that, The method further includes: Obtain the delay information of the channel; The channel prediction based on the Doppler frequency information and the complex amplitude information, to obtain channel prediction information, includes: Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information.

3. The method as described in claim 1, characterized in that, The acquisition of the Doppler frequency information and complex amplitude information of the channel includes: Determine the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters; Estimate the Doppler frequency information of each sub-path in the nth multipath cluster; Based on the Doppler frequency information of each sub-path in the nth multipath cluster, the complex amplitude of each sub-path in the nth multipath cluster is calculated.

4. The method as described in claim 3, characterized in that, Determining the number of sub-paths of the nth multipath cluster includes: Obtain the time correlation matrix of the nth multipath cluster; Based on the time correlation matrix, calculate the channel feature vector of the nth multipath cluster; The number of sub-paths corresponding to the channel feature vector of the nth multipath cluster is calculated using the eigenvalue ratio method, thus obtaining the number of sub-paths of the nth multipath cluster.

5. The method as described in claim 4, characterized in that, Obtaining the time correlation matrix of the nth multipath cluster includes: Obtain the frequency correlation matrix of the channel; Based on the frequency correlation matrix, calculate the channel eigenvalue vector of the channel; The number of multipath clusters corresponding to the channel feature vector of the channel is calculated using the eigenvalue ratio method, and the number of multipath clusters L of the channel is obtained, where L is an integer greater than 1. Determine the time delay of the L multipath clusters; Based on the time delay of the L multipath clusters, the complex amplitude of the L multipath clusters at multiple times is calculated to obtain the complex amplitude correlation matrix of the L multipath clusters at the multiple times. The time correlation matrix of the nth multipath cluster is obtained from the complex amplitude correlation matrix.

6. The method as described in claim 5, characterized in that, The at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the at least one multipath cluster; or The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

7. The method as described in claim 5 or 6, characterized in that, The feature value corresponding to the channel feature value vector of the at least one multipath cluster is greater than a preset threshold.

8. A communication device, characterized in that, include: Memory, transceiver, and processor, among which: The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and perform the following operations: Acquire the Doppler frequency information and complex amplitude information of the channel; Channel prediction is performed based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information; The Doppler frequency information includes: The Doppler frequency of each sub-path in at least one multipath cluster of the channel; The complex amplitude information includes: The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

9. The communication device as claimed in claim 8, characterized in that, The processor is also configured to read the computer program in the memory and perform the following operations: Obtain the delay information of the channel; The channel prediction based on the Doppler frequency information and the complex amplitude information, to obtain channel prediction information, includes: Channel prediction information is obtained by performing channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information.

10. The communication device as described in claim 8, characterized in that, The processor is specifically used to read the computer program in the memory and perform the following operations: Determine the number of sub-paths of the nth multipath cluster, wherein the nth multipath cluster is any one of the at least one multipath clusters; Estimate the Doppler frequency information of each sub-path in the nth multipath cluster; Based on the Doppler frequency information of each sub-path in the nth multipath cluster, the complex amplitude of each sub-path in the nth multipath cluster is calculated.

11. The communication device as described in claim 10, characterized in that, The processor is specifically used to read the computer program in the memory and perform the following operations: Obtain the time correlation matrix of the nth multipath cluster; Based on the time correlation matrix, calculate the channel feature vector of the nth multipath cluster; The number of sub-paths corresponding to the channel feature vector of the nth multipath cluster is calculated using the eigenvalue ratio method, thus obtaining the number of sub-paths of the nth multipath cluster.

12. The communication device as described in claim 11, characterized in that, The processor is specifically used to read the computer program in the memory and perform the following operations: Obtain the frequency correlation matrix of the channel; Based on the frequency correlation matrix, calculate the channel eigenvalue vector of the channel; The number of multipath clusters corresponding to the channel feature vector of the channel is calculated using the eigenvalue ratio method, and the number of multipath clusters L of the channel is obtained, where L is an integer greater than 1. Determine the time delay of the L multipath clusters; Based on the time delay of the L multipath clusters, the complex amplitude of the L multipath clusters at multiple times is calculated to obtain the complex amplitude correlation matrix of the L multipath clusters at the multiple times. The time correlation matrix of the nth multipath cluster is obtained from the complex amplitude correlation matrix.

13. The communication device as described in claim 12, characterized in that, The at least one multipath cluster is a subset of the L multipath clusters, and the energy of the at least one multipath cluster is greater than the energy of the other multipath clusters in the L multipath clusters excluding the subset; or The at least one multipath cluster is all the multipath clusters among the L multipath clusters.

14. The communication device as described in claim 12 or 13, characterized in that, The feature value corresponding to the channel feature value vector of the at least one multipath cluster is greater than a preset threshold.

15. A communication device, characterized in that, include: The first acquisition unit is used to acquire the Doppler frequency information and complex amplitude information of the channel; An estimation unit is used to perform channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information; The Doppler frequency information includes: The Doppler frequency of each sub-path in at least one multipath cluster of the channel; The complex amplitude information includes: The complex amplitude of each sub-path in the at least one multipath cluster of the channel.

16. The communication device as claimed in claim 15, characterized in that, The communication device also includes: The second acquisition unit is used to acquire the delay information of the channel; The estimation unit performs channel prediction based on the Doppler frequency information and the complex amplitude information to obtain channel prediction information, including: The estimation unit performs channel prediction based on the Doppler frequency information, the complex amplitude information, and the time delay information to obtain channel prediction information.

17. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program for causing the processor to perform the channel prediction method according to any one of claims 1 to 7.

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