Low computing power requirement mixed field channel estimation method, system, medium and computing device

Abstract

The present invention relates to the field of wireless mobile communications, and discloses a low computing power requirement mixed field channel estimation method, system, medium and computing equipment, which comprises: dividing an antenna array into subarrays, estimating the angle of the channel path for each of the four subarrays; if the angles corresponding to the four subarrays are consistent, using the angle domain DFT codebook to estimate the path parameters; if the angles corresponding to the four subarrays are inconsistent, using the geometric relationship of each subarray to obtain the distance of the path, and then using the distance to generate the corresponding codebook, and estimating the parameters of the path; after estimating a certain path, removing the part of the path from the received signal until the parameters of all paths are estimated, and the channel estimation is completed. The present invention solves the problem of inaccurate acquisition of high-precision channel information under mixed fields.

Description

Technical Field

[0001] The present invention relates to the field of wireless mobile communication technology, and in particular to a low computing power requirement mixed field channel estimation method, system, medium and computing equipment based on geometric relationship. Background Art

[0002] In order to meet the growing business needs, the use of extremely high bandwidth provided by high frequency bands such as millimeter waves (30GHz-300GHz, adopted by the 5G standard) and terahertz (0.1THz-10THz) for mobile communications will become an important technical means for future mobile communication networks. However, in the millimeter wave and terahertz bands with rich spectrum resources, there is serious path loss in wireless propagation. Taking the terahertz signal in the 0.16 THz band as an example, its propagation process will experience severe path loss of up to 80 dB / km. Massive multiple-input multi-output (MIMO) technology is recognized as one of the key technologies to overcome this challenge. By configuring an ultra-large-scale antenna array (for example, 256 antennas), massive MIMO technology forms a directional beam with extremely high array gain, which can compensate for the path loss in the high frequency band and improve the spectrum efficiency of the system.

[0003] Thanks to the high path attenuation of high-frequency signals, the number of multipaths is usually small in high-frequency communication systems, which greatly facilitates channel estimation. By transforming the received signal into the angle domain through the discrete Fourier transform (DFT) codebook, the angle parameters of each path can be extracted, which greatly facilitates channel estimation. However, in order to compensate for the serious path attenuation problem of high-frequency signals, the base station usually adopts a large-scale antenna array to obtain a large energy gain. As the antenna scale continues to increase, the different paths of users in the large-scale MIMO communication system may be located in the far field range or in the near field range. That is, it is necessary to estimate the channel of the far and near mixed field (hereinafter referred to as the mixed field), which will lead to the performance degradation of the traditional DFT codebook-based channel estimation method. The reason is that the traditional DFT codebook-based channel estimation method adopts the plane wave assumption, that is, it is assumed that the user is located in the far field of the base station antenna, and the electromagnetic wave is approximately transmitted as a plane wave. However, when the user is in the near field, existing studies have shown that the electromagnetic wave cannot be approximated as a plane wave transmission, but is transmitted in the form of a spherical wave, which will cause the angular domain sparsity corresponding to the Fourier transform to no longer exist. If the traditional Fourier transform-based channel estimation framework is adopted, it will cause serious channel estimation progress loss. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a mixed field channel estimation method, system, medium and computing device with low computing power requirements, which solves the problem of inaccurate acquisition of high-precision channel information in a mixed field.

[0005] To achieve the above object, the present invention adopts the following technical solution: a low computing power requirement mixed field channel estimation method, comprising: dividing the antenna array into subarrays, and estimate the angles of the channel paths for the four subarrays respectively; if the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the parameters of the path; if the angles corresponding to the four subarrays are inconsistent, the distance of the path is obtained by using the geometric relationship of each subarray, and then the corresponding codebook is generated using the distance to estimate the parameters of the path; after estimating a certain path, the part of the path is removed from the received signal until the parameters of all paths are estimated to complete the channel estimation.

[0006] Furthermore, the antenna array is divided into sub-arrays, and the angles of the channel paths are estimated for the four sub-arrays respectively, including:

[0007] The base station receives the signal The sub-array is divided into four parts, and each sub-array is processed using the DFT codebook. Make relevant

[0008] And using the DFT codebook Receiving signal from base station Do relevant, seek ;

[0009] Ask separately The number of the element with the largest value corresponds to the angle of this path relative to this sub-matrix.

[0010] Furthermore, the DFT codebook for:

[0011]

[0012] in, , , , the number of antennas is ; represents the far-field array response vector.

[0013] Furthermore, the base station receives the signal for:

[0014]

[0015] in, P is the transmission power, is additive white Gaussian noise, , is the number of antennas; Indicates a channel.

[0016] Furthermore, if the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the path parameters, including:

[0017] If the angles corresponding to the four sub-arrays are consistent, it means that the user is located in the far field range of the array, and the path gain of the path is obtained.

[0018] Furthermore, if the angles corresponding to the four subarrays are inconsistent, the distance of the stripe is obtained by using the geometric relationship between the subarrays, and then the corresponding codebook is generated by using the distance, and the parameters of the stripe are estimated, including:

[0019] If the angles corresponding to the four sub-arrays are inconsistent, it means that the user is within the near field of the array, and the geometric relationship between the user and the four sub-arrays is determined;

[0020] According to the geometric relationship between the user and the four sub-arrays, the relationship between the user's position and angle is obtained, and the relevant information of the four sub-arrays is combined to determine the distance from the user to the center of the array;

[0021] The distance from the user to the center of the array is used to generate a new near-field codebook, and the new near-field codebook is used to receive the signal from the base station. Do the correlation to get an estimate of the angle;

[0022] The corresponding path gain is obtained by the estimated value of the angle.

[0023] Furthermore, the new near-field codebook is:

[0024]

[0025] in, is the new near-field codebook; , , , the number of antennas is ; represents the near-field array response vector; is the distance from the user to the center of the array.

[0026] A low computing power requirement mixed field channel estimation system, comprising: an array division module, which divides the antenna array into subarrays, respectively estimating the angles of the channel paths for the four subarrays; in the first subprocessing module, if the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the path parameters; in the second subprocessing module, if the angles corresponding to the four subarrays are inconsistent, the geometric relationship of each subarray is used to obtain the distance of the path, and then the corresponding codebook is generated using the distance to estimate the parameters of the path; in the channel estimation module, after estimating a certain path, the part of the path is removed from the received signal until the parameters of all paths are estimated to complete the channel estimation.

[0027] A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the above methods.

[0028] A computing device comprises: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0029] The present invention adopts the above technical solution, which has the following advantages:

[0030] The present invention proposes a low-computing-power-demand mixed-field channel estimation method based on geometric relationships. By dividing the antenna array into four sub-arrays, the angles obtained from each sub-array can be used to determine whether each path is located in the far field or near field of the array, thereby effectively determining the codebook that should be used. Since the near-field path is correctly judged and estimated, the proposed method can achieve accurate channel estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of a mixed field channel estimation method with low computing power requirements in an embodiment of the present invention;

[0032] Figure 2 1 is a geometric relationship diagram of the near-field sub-array in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0035] In order to solve the problem of inaccurate channel estimation in large-scale MIMO mixed fields, the present invention adopts a new efficient channel estimation method that can simultaneously and efficiently estimate the far-field path and the near-field path. At the same time, if the traditional near-field codebook is directly used for estimation, the codebook size is too large and the dependence on computing power is too large, so an efficient channel estimation method with low computing power requirements is needed.

[0036] The present invention discloses a low-computing-power-demand mixed-field channel estimation method based on geometric relationships, which is characterized in that the geometric relationships of different sub-arrays of an antenna array are used to first determine whether a certain path is located in the near-field range or the far-field range of the array. If it is located in the far-field range, a traditional angle domain codebook, such as a discrete Fourier transform (DFT) codebook, is used to perform channel estimation. If it is located in the near-field range, the geometric relationship of the array is first used to obtain the distance of the path, and then the codebook corresponding to the distance is used to perform channel estimation, and it is iterated until the parameters of all paths are estimated, thereby obtaining accurate channel information and performing directional data transmission.

[0037] In one embodiment of the present invention, a low computing power requirement mixed field channel estimation method is provided. In this embodiment, Figure 1 As shown, the method comprises the following steps:

[0038] 1) Divide the antenna array into sub-arrays, and estimate the angles of the channel paths for the four sub-arrays respectively;

[0039] 2) If the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the path parameters;

[0040] 3) If the angles corresponding to the four subarrays are inconsistent, the distance of the stripe is obtained using the geometric relationship of each subarray, and then the corresponding codebook is generated using the distance to estimate the parameters of the stripe;

[0041] 4) After estimating a certain path, remove the part of the path in the received signal, re-estimate the angle of the channel path, and repeat steps 2) to 3) until the parameters of all paths are estimated and the channel estimation is completed.

[0042] In this embodiment, it is assumed that the number of antennas is , the number of antennas of the user is 1, and the user channel can be expressed as:

[0043]

[0044] in, represents the channel, denote the number of far-field paths and near-field paths, respectively. They represent the path gains corresponding to the far-field path and the near-field path, respectively. Respectively represent the far field The pitch angle and azimuth angle corresponding to each path, Respectively represent the near field The distance, elevation and azimuth corresponding to each path. represents the far-field array response vector, which is expressed as:

[0045]

[0046] in, represents the wave number, represents the signal frequency, is the speed of light in vacuum, Indicates elements.

[0047] In the near-field scenario, represents the near-field array response vector, which is expressed as:

[0048] ;

[0049] in:

[0050] .

[0051] In the above step 1), the antenna array is divided into sub-arrays, estimating the angles of the channel paths for the four sub-arrays respectively, including the following steps:

[0052] 1.1) The base station receives the signal The sub-arrays are divided into four parts: , whose dimensions are , using the DFT codebook for each sub-array Make relevant

[0053] In this embodiment, the user needs to send a known pilot signal. Assuming that the pilot signal is all 1, the base station receives the signal for:

[0054]

[0055] in, P is the transmission power, is additive white Gaussian noise, , represents the complex field, that is is a dimension of A complex vector where every element in the vector is a complex number.

[0056] DFT codebook for:

[0057]

[0058] in, , , , the number of antennas is ; represents the far-field array response vector.

[0059] 1.2) and use the DFT codebook Receiving signal from base station Do relevant, seek , the dimensions of these four vectors are ;

[0060] 1.3) Find The number of the element with the largest value, and its corresponding angle is the angle of this path to this sub-array, recorded as .

[0061] In the above step 2), if the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the parameters of the path, specifically:

[0062] If the angles corresponding to the four sub-arrays are consistent, that is, four groups of angles If they are equal, it means that the user is in the far field of the array and the path gain of the path is obtained.

[0063] The path gain of this path for:

[0064]

[0065] In the above step 3), if the angles corresponding to the four subarrays are inconsistent, the distance of the stripe is obtained by using the geometric relationship between the subarrays, and then the corresponding codebook is generated by using the distance, and the parameters of the stripe are estimated, including the following steps:

[0066] 3.1) If the angles corresponding to the four sub-arrays are inconsistent, it means that the user is within the near field of the array. Determine the geometric relationship between the user and the four sub-arrays, such as Figure 2 As shown, Represents the coordinates of the center of each sub-array;

[0067] 3.2) Based on the geometric relationship between the user and the four sub-arrays, the relationship between the user's position and angle is obtained, and the relevant information of the four sub-arrays is combined to determine the distance from the user to the center of the array;

[0068] In this embodiment, the relationship between the user position and the angle is:

[0069]

[0070] in, Indicates the subarray number, Indicates near field The azimuth corresponding to the path, Indicates near field The pitch angle corresponding to the path.

[0071] Combining the relevant information of the four sub-arrays, we can get:

[0072]

[0073] The leftmost large matrix is , the matrix on the right is , the user's location It can be obtained by the least squares method as ,in express The pseudo-inverse of , then the distance from the user to the center of the array is for:

[0074]

[0075] 3.3) Generate a new near-field codebook using the distance from the user to the center of the array, and use the new near-field codebook to receive the signal from the base station To do the relevant , get an estimate of the angle;

[0076] In this embodiment, the new near-field codebook is:

[0077]

[0078] in, is the new near-field codebook; , , , the number of antennas is ; represents the near-field array response vector; is the distance from the user to the center of the array.

[0079] Get an estimate of the angle, specifically: , get the number of the element with the maximum value to get the estimated value of the angle .

[0080] 3.4) Obtain the corresponding path gain through the estimated angle :

[0081]

[0082] It should be noted that since the number of codewords in the codebook at this time is proportional to the number of antenna units, it is significantly reduced compared to the number of codewords in the traditional near-field codebook, so the computational complexity can be reduced and the computing power requirement can be saved.

[0083] After obtaining the corresponding parameters for this path, the received signal Remove the component of this path, that is, if this path is the far field, , if this path is near field, ;

[0084] After multiple iterations until all paths are restored, channel estimation can be completed and subsequent information transmission can be carried out.

[0085] In one embodiment of the present invention, a low computing power requirement mixed field channel estimation system is provided, which includes:

[0086] The array division module divides the antenna array into sub-arrays, and estimate the angles of the channel paths for the four sub-arrays respectively;

[0087] In the first sub-processing module, if the angles corresponding to the four sub-arrays are consistent, an angle domain DFT codebook is used to estimate the parameters of the path;

[0088] The second sub-processing module, if the angles corresponding to the four sub-arrays are inconsistent, obtains the distance of the stripe using the geometric relationship of each sub-array, then generates a corresponding codebook using the distance, and estimates the parameters of the stripe;

[0089] The channel estimation module, after estimating a certain path, removes the part of the path from the received signal until the parameters of all paths are estimated to complete the channel estimation.

[0090] In the above embodiment, the antenna array is divided into sub-arrays, and the angles of the channel paths are estimated for the four sub-arrays respectively, including:

[0091] The base station receives the signal The sub-array is divided into four parts, and each sub-array is processed using the DFT codebook. Make relevant

[0092] And using the DFT codebook Receiving signal from base station Do relevant, seek ;

[0093] Ask separately The number of the element with the largest value corresponds to the angle of this path relative to this sub-matrix.

[0094] In the above embodiment, the DFT codebook for:

[0095]

[0096] in, , , , the number of antennas is ; represents the far-field array response vector.

[0097] In the above embodiment, the base station receives the signal for:

[0098]

[0099] in, P is the transmission power, is additive white Gaussian noise, , is the number of antennas; Indicates a channel.

[0100] In the above embodiment, if the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the path parameters, including: if the angles corresponding to the four subarrays are consistent, it means that the user is located in the far field range of the array, and the path gain of the path is obtained.

[0101] In the above embodiment, if the angles corresponding to the four subarrays are inconsistent, the distance of the stripe is obtained by using the geometric relationship between the subarrays, and then the corresponding codebook is generated by using the distance, and the parameters of the stripe are estimated, including:

[0102] If the angles corresponding to the four sub-arrays are inconsistent, it means that the user is within the near field of the array, and the geometric relationship between the user and the four sub-arrays is determined;

[0103] According to the geometric relationship between the user and the four sub-arrays, the relationship between the user's position and angle is obtained, and the relevant information of the four sub-arrays is combined to determine the distance from the user to the center of the array;

[0104] The distance from the user to the center of the array is used to generate a new near-field codebook, and the new near-field codebook is used to receive the signal from the base station. Do the correlation to get an estimate of the angle;

[0105] The corresponding path gain is obtained by the estimated value of the angle.

[0106] In the above embodiment, the new near-field codebook is:

[0107]

[0108] in, is the new near-field codebook; , , , the number of antennas is ; represents the near-field array response vector; is the distance from the user to the center of the array.

[0109] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0110] In one embodiment of the present invention, a computing device is provided, which may be a terminal, and may include: a processor, a communication interface, a memory, a display screen, and an input device. Among them, the processor, the communication interface, and the memory communicate with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory, and the non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, the method in each of the above embodiments is implemented; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a management network, NFC (near field communication) or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen, and the input device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computing device, or an external keyboard, touchpad or mouse, etc. The processor may call the logic instructions in the memory.

[0111] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0112] In one embodiment of the present invention, a computer program product is provided, wherein the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0113] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores server instructions, and the computer instructions enable a computer to execute the methods provided in the above embodiments.

[0114] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low computing power requirement mixed field channel estimation method, characterized in that: include: The antenna array is divided into 2×2 sub-arrays, and the angles of the channel paths are estimated for the four sub-arrays respectively; If the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the parameters of the channel path; If the angles corresponding to the four sub-arrays are inconsistent, the distance of the channel path is obtained using the geometric relationship between the sub-arrays, and then the corresponding codebook is generated using the distance to estimate the parameters of the channel path; After estimating a channel path, remove the part of the channel path from the received signal until the parameters of all channel paths are estimated and the channel estimation is completed; The antenna array is divided into 2×2 sub-arrays, and the angles of the channel paths are estimated for the four sub-arrays respectively, including: The base station received signal y is divided into four parts according to the subarray, namely y1, y2, y3, y4, and each subarray is correlated using the DFT codebook F; And use the DFT codebook F to correlate the base station received signal y to obtain F H y1,F H y2,F H y3,F H y4; Find F H y1,F H y2,F H y3,F H The serial number of the element with the largest y4 value corresponds to the angle of this channel path relative to this subarray.

2. The low computing power requirement mixed field channel estimation method according to claim 1, characterized in that: The DFT codebook F is: in, n1=1,2,…,N1,n2=1,2,…,N2, represents the pitch angle corresponding to the n1th channel path in the near field, represents the azimuth angle corresponding to the n1th channel path in the near field, and the number of antennas is N=N1×N2; a() represents the far-field array response vector.

3. The low computing power requirement mixed field channel estimation method according to claim 1, characterized in that: The base station receives the signal y as: Where P is the transmission power, w is the additive Gaussian white noise, N is the number of antennas; h represents the channel.

4. The low computing power requirement mixed field channel estimation method according to claim 1, characterized in that: If the angles corresponding to the four subarrays are consistent, the angle domain DFT codebook is used to estimate the parameters of the channel path, including: If the angles corresponding to the four sub-arrays are consistent, it means that the user is located in the far field range of the array, and the channel path gain of the channel path is obtained.

5. The low computing power requirement mixed field channel estimation method according to claim 1, characterized in that: If the angles corresponding to the four subarrays are inconsistent, the distance of the channel path is obtained using the geometric relationship between the subarrays, and then the corresponding codebook is generated using the distance, and the parameters of the channel path are estimated, including: If the angles corresponding to the four sub-arrays are inconsistent, it means that the user is within the near field of the array, and the geometric relationship between the user and the four sub-arrays is determined; According to the geometric relationship between the user and the four sub-arrays, the relationship between the user's position and the angle is obtained, and the relevant information of the four sub-arrays is combined to determine the distance from the user to the center of the array; A new near-field codebook is generated using the distance from the user to the center of the array, and the new near-field codebook is used to correlate the base station received signal y to obtain an estimated value of the angle; The corresponding channel path gain is obtained through the estimated value of the angle.

6. The low computing power requirement mixed field channel estimation method according to claim 5, characterized in that: The new near-field codebook is: Where G is the new near-field codebook; n1=1,2,…,N1,n2=1,2,…,N2, the number of antennas is N=N1×N2, represents the pitch angle corresponding to the n1th channel path in the near field, represents the azimuth angle corresponding to the n1th channel path in the near field; b() represents the near-field array response vector; is the distance from the user to the center of the array.

7. A low computing power requirement mixed field channel estimation system, characterized in that: include: The array division module divides the antenna array into 2×2 sub-arrays and estimates the angle of the channel path for each of the four sub-arrays. The first sub-processing module uses an angle domain DFT codebook to estimate the parameters of the channel path if the angles corresponding to the four sub-arrays are consistent; The second sub-processing module, if the angles corresponding to the four sub-arrays are inconsistent, obtains the distance of the channel path using the geometric relationship between the sub-arrays, then generates a corresponding codebook using the distance, and estimates the parameters of the channel path; The channel estimation module, after estimating a certain path, removes the part of the channel path from the received signal until the parameters of all channel paths are estimated to complete the channel estimation; The antenna array is divided into 2×2 sub-arrays, and the angles of the channel paths are estimated for the four sub-arrays respectively, including: The base station received signal y is divided into four parts according to the subarray, namely y1, y2, y3, y4, and each subarray is correlated using the DFT codebook F; And use the DFT codebook F to correlate the base station received signal y to obtain F H y1,F H y2,F H y3,F H y4; Find F H y1,F H y2,F H y3,F H The serial number of the element with the largest y4 value corresponds to the angle of this channel path relative to this subarray.

8. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 6.

9. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods described in claims 1 to 6.

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