A near-field CSI estimation method for XL-MIMO based on spatial geometry distributed processing

Abstract

The present invention discloses an XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing. A single subarray in a base station first estimates partial CSI using uplink pilot signals, including the azimuth and delay of one or more propagation paths in a single subarray channel. The estimated single subarray delay information is then used to orthogonally decouple the multipath received signals from other subarrays and estimate the azimuth of one or more propagation paths in other subarray channels. Finally, the base station uses the estimated azimuths from different subarrays to jointly estimate the distance information of one or more propagation paths based on spatial geometry, thereby reconstructing the full-dimensional XL-MIMO near-field channel. This invention provides a method for estimating the full-dimensional near-field CSI of an XL-MIMO system, particularly overcoming the computational complexity of obtaining full-dimensional near-field CSI in very large-scale multiple-input, multiple-output (VLSI) systems.

Description

Technical Field

[0001] The present invention relates to an XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing, belonging to the technical field of full-dimensional near-field CSI estimation. Background Art

[0002] In sixth-generation wireless communication systems, the requirements for regional throughput and spectral efficiency will be greatly increased, which is beyond the support of fifth-generation wireless communication technology. To achieve higher spectral efficiency, ultra-large-scale multiple-input multiple-output (XL-MIMO) is considered a promising technology for future wireless communication systems. In future wireless communication systems, base stations (BSs) will use thousands or even more antennas, which can provide stronger beamforming gain and higher spectral efficiency. To fully utilize the performance advantages of XL-MIMO, it is necessary to obtain channel state information (CSI) in advance. However, the greatly increased number of antennas will lead to high computational complexity overhead. Since the near-field characteristics in XL-MIMO systems cannot be ignored, how to estimate near-field CSI under limited computational complexity conditions requires research and exploration. Therefore, the estimation of near-field CSI has become a key issue that needs to be solved in XL-MIMO systems.

[0003] Compressed sensing-based CSI estimation is a mainstream algorithm for acquiring near-field CSI in XL-MIMO systems. In XL-MIMO systems, the near-field channel is modeled as a spherical wavefront, where the signals received by different receiving antennas have phase differences due to different transmission distances. The base station uses a fully sampled dictionary in the polar coordinate domain to detect sparse multipath within the near-field channel and sequentially extracts multiple paths through an iterative algorithm. However, compressed sensing-based algorithms require the establishment of a dictionary dimension proportional to the cube of the number of antennas, resulting in unacceptable computational complexity overhead in practical wireless communication systems. Furthermore, the greatly increased number of antennas will lead to high hardware costs. To ensure the low cost requirements of practical wireless communication systems, the base station will deploy a hybrid precoding architecture, connecting a large number of antennas through only a few RF chains. This reduces the dimensionality of the received signal, making it difficult to acquire full-dimensional CSI. Therefore, acquiring full-dimensional near-field CSI in hybrid-architecture ultra-large-scale MIMO systems has become a bottleneck.

[0004] Preliminary research has been conducted on obtaining full-dimensional near-field CSI in hybrid-architecture ultra-large-scale MIMO systems. A widely adopted strategy is to evenly divide the full-dimensional array into several subarrays, with each subarray independently processing the received signal and reconstructing partial CSI information. The base station then reconstructs the full-dimensional CSI using the partial CSI estimated by the subarrays. However, in practical XL-MIMO systems, the number of subarrays is still large, resulting in high computational complexity. Furthermore, the independent processing of received signals by multiple subarrays ignores the correlation between subarrays and the near-field characteristics of the full-dimensional array. This limits the accuracy of full-dimensional CSI estimation and fails to meet the low computational complexity and high-precision full-dimensional near-field CSI estimation requirements of practical hybrid-architecture XL-MIMO systems.

[0005] In summary, how to obtain high-precision hybrid architecture ultra-large-scale MIMO full-dimensional near-field CSI with low computational complexity has become a difficult problem that needs to be overcome in the sixth generation of mobile communications. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing. The central subarray estimates partial CSI with low computational complexity. The other subarrays use the estimated information from the central subarray to perform multipath orthogonal decoupling and estimate the azimuth angles of multiple paths respectively. The spatial geometric relationship between the multiple subarrays is utilized to jointly estimate the distance. This method achieves the reconstruction of the full-dimensional near-field CSI of the hybrid architecture XL-MIMO system with low computational complexity, while ensuring the high-precision characteristics of the reconstructed channel.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] An XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing includes the following steps:

[0009] Step 1: In a very large-scale MIMO system, the entire base station array is divided into several subarrays, one of which is selected as a central subarray, and four other subarrays are evenly distributed in the entire array, and the central subarray and the four other subarrays have the same dimensions;

[0010] Step 2: The central subarray receives the all-one uplink pilot signal sent by the user equipment using a hybrid precoding architecture, processes and calculates the received signal, and obtains an estimated number of central subarray propagation paths and the azimuth and delay parameters of each path.

[0011] Step 3: For each other subarray, perform multipath orthogonal decoupling on the received signals of the other subarrays using the estimated number of propagation paths in the central subarray and the delay parameters of each path, and estimate the direction angle of one or more propagation paths in the other subarrays based on the decoupled multipath signals;

[0012] In step 4, the angular information of each path estimated by the central subarray and the angular information of each path estimated by the four other subarrays are used to jointly estimate the distance information of all paths in the entire array based on spatial geometric relationships. The path gain is estimated based on the distance information, and the full-dimensional XL-MIMO near-field channel is reconstructed.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing are implemented.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing.

[0015] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0016] 1. The present invention utilizes the phase-controllable characteristics of the hybrid precoding architecture and sets the analog phase shift matrix as an angle-domain undersampling dictionary by adjusting the phase. This method requires only low computational complexity to estimate the number of propagation paths, azimuth angle, delay, and gain of the central subarray, effectively reducing the pilot overhead of CSI estimation and overcoming the impact of the hybrid precoding architecture on channel sparsity.

[0017] 2. After the central subarray estimates the number and delay information of multiple paths in the channel, the present invention utilizes the common delay characteristics between different subarrays to perform multipath orthogonal decoupling of the received signals of other subarrays, thus solving the problems of disordered paths estimated by multiple subarrays and large interference between paths.

[0018] 3. After obtaining the angle information of multiple subarrays, the present invention utilizes the spatial geometric relationship between the multiple subarrays to jointly derive the distance information of multiple paths and re-estimate the near-field gain, thereby ensuring the accuracy of full-dimensional near-field channel reconstruction of the ultra-large-scale MIMO system and significantly reducing the computational complexity required for channel reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of an XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing according to the present invention;

[0020] Figure 2 This is a diagram of the XL-MIMO system model with a sub-array hybrid architecture;

[0021] Figure 3 It is the angle domain undersampling point dictionary map of the hybrid precoding architecture;

[0022] Figure 4 It is a spatial geometric relationship diagram between multiple sub-arrays. DETAILED DESCRIPTION

[0023] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be interpreted as limiting the present invention.

[0024] like Figure 1 As shown in the figure, the present invention proposes an XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing. The base station divides the entire array into several subarrays, each of which processes the received signal independently. After a single subarray estimates partial CSI, the other subarrays use common delay orthogonal decoupling of multipath received signals. The directional angle information of each subarray is estimated with a small amount of computational complexity. The base station uses the angle information of multiple subarrays to jointly estimate the distance information of multiple paths in the entire array based on spatial geometric relationships, and reconstructs the full-dimensional near-field channel. The method specifically includes the following steps:

[0025] (1) The user equipment (UE) sends an uplink pilot signal. The BS's single central subarray uses the received uplink pilot signal to estimate the uplink CSI, which is information about one or more propagation paths, including but not limited to the direction angle and delay of the propagation path.

[0026] The base station subarray division strategies include but are not limited to the following: the entire array is evenly divided into several subarrays of the same dimension, the entire array is divided into five subarrays of the same dimension in the center and four corners, and the other subarrays are divided arbitrarily.

[0027] The user needs to send multiple all-one pilot signals. The number of pilot signals is proportional to the number of sampling points in the angle domain undersampling dictionary. In the subarray single RF chain system, it is the number of subarray antennas divided by 4.

[0028] (2) The other subarrays of the base station use the delay information estimated by the central subarray to perform orthogonal decoupling of the multipath received signals, obtaining L interference-free received signals for each subarray, and using the decoupled multipath signals to estimate the directional angle information of the multiple paths in different subarrays.

[0029] (3) The BS estimates the distance information of the L paths of the full-dimensional channel based on the azimuth information of the L paths estimated by the central subarray and the azimuth information of the L paths estimated by multiple other subarrays, and reconstructs the full-dimensional near-field channel.

[0030] The CSI used by the base station to reconstruct the full-dimensional near-field channel includes but is not limited to: the direction angles and delays of the L propagation paths estimated by the central subarray, and the distances and gains of the L propagation paths jointly estimated by multiple subarrays.

[0031] Example

[0032] In a hybrid architecture ultra-large-scale MIMO system, the BS is equipped with a uniform planar array (UPA) with N antennas and N r Row N c The entire array is evenly divided into K sub-arrays, and the number of antennas in each sub-array is N s , N sr Row N sc Column, where N sr =N sc Each subarray antenna is connected to a single RF chain, and the user equipment (UE) uses a single antenna configuration. To perform data detection and transmission, the base station needs to obtain full-dimensional near-field CSI and reconstruct the full-dimensional near-field channel H. This embodiment will reconstruct H, including the following steps:

[0033] Step 1: User equipment sends N s / 4 all-one uplink pilot signals, such as Figure 2 As shown, each subarray of the ultra-large-scale UPA receives the uplink pilot signal through a hybrid precoding architecture, where the phase of the hybrid precoding architecture is set to the angle domain undersampling dictionary, as shown in Figure 3 As shown in the shaded area, the signal model received by the central sub-array at this time is:

[0034]

[0035] in, is the signal received by the central sub-array, is the uplink channel between the user equipment and the central sub-array, is the angle domain undersampling dictionary designed by phase adjustment for the hybrid precoding architecture, P is the user equipment transmit power, is the noise, M is the number of transmitted subcarriers, and the uplink central subarray channel has the following expression:

[0036]

[0037] Where L is the number of propagation paths, g cen,lis the gain of the lth path in the central subarray channel, a(·) is the central subarray response vector, p(·) is the multi-subcarrier frequency domain steering vector, are the azimuth and elevation angles of the lth path in the central subarray channel, τ l is the delay of the lth path;

[0038] The base station uses the received uplink pilot signal, sets the hybrid precoding angle domain undersampling dictionary, and adopts the compressed sensing algorithm to perform the received signal Y cen Process and calculate to get propagation paths Group angle and delay parameter estimation results, each group of parameter estimation includes

[0039] Step 2: The base station uses the estimated number of propagation paths of the central subarray and Path delay parameters Multipath orthogonal decoupling is performed on the received signals of the other subarrays (the four corner subarrays). Based on the fact that different subarrays have the same number of paths and delay parameters, the received signal of the kth (k=1,…,4) subarray can be multipath orthogonally decoupled as follows:

[0040]

[0041] in, represents the decoupled k-th subarray The signal is received by the path, Receive the signal for the kth subarray, is the kth channel in the kth subarray The gain of the path, is the kth channel in the kth subarray The azimuth and elevation angles of the paths,

[0042] After the kth subarray receives the multipath orthogonal decoupling signal, k = 1,...,4, each path channel uses a compressed sensing algorithm to estimate the azimuth and elevation angle of the path. The decoupling of the paths can ensure that the estimated paths between different sub-arrays have a matching order and can remove the multipath interference. Each set of estimated parameters includes

[0043]

[0044] Step 3: The base station estimates multiple subarrays After obtaining the azimuth and elevation angle information of each path, the spatial geometric relationship between different sub-arrays is used to combine the estimated angles of the four sub-arrays at the four corners of the entire array and the central sub-array to deduce the angles. Path distance parameter information, such as Figure 4 As shown, the distance parameter information of each path can be expressed as:

[0045]

[0046] in represents the pseudo-inverse operation, represents the distance from the user (direct line of sight path) / scatterer (scattering path) in the lth path to the center of the full-dimensional subarray, d is the spacing between adjacent base station antennas, set to half a wavelength, and the equivalent angle vector It can be expressed as:

[0047]

[0048] in Equivalent angle Definition as Figure 4 As shown, the path gain is re-estimated using the least squares method The BS uses the angle, delay, distance, and gain estimated by the central subarray to reconstruct the full-dimensional near-field channel between the user and the base station:

[0049]

[0050] where w(·) is the near-field response vector of the full-dimensional UPA array, is the reconstructed full-dimensional near-field channel between the user and the base station.

[0051] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing are implemented.

[0052] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the aforementioned XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing.

[0053] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0055] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0057] The above embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for estimating near-field CSI of XL-MIMO based on spatial geometry distributed processing, characterized in that: The steps include: Step 1: In a very large-scale MIMO system, the entire base station array is divided into several subarrays, one of which is selected as a central subarray, and four other subarrays are evenly distributed in the entire array, and the central subarray and the four other subarrays have the same dimensions; Step 2: The central subarray receives the all-one uplink pilot signal sent by the user equipment using a hybrid precoding architecture, processes and calculates the received signal, and obtains an estimated number of central subarray propagation paths and the azimuth and delay parameters of each path. Step 3: For each other subarray, perform multipath orthogonal decoupling on the received signals of the other subarrays using the estimated number of propagation paths in the central subarray and the delay parameters of each path, and estimate the direction angle of one or more propagation paths in the other subarrays based on the decoupled multipath signals; In step 4, the angular information of each path estimated by the central subarray and the angular information of each path estimated by the four other subarrays are used to jointly estimate the distance information of all paths in the entire array based on spatial geometric relationships. The path gain is estimated based on the distance information, and the full-dimensional XL-MIMO near-field channel is reconstructed.

2. The XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing according to claim 1, characterized in that In step 1, the division strategy for dividing the entire base station array into a plurality of subarrays includes one of the following forms: evenly dividing the entire base station array into a plurality of subarrays of the same dimension; and dividing the entire base station array into a center and four corner subarrays of the same dimension, with the remaining subarrays divided arbitrarily.

3. The XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing according to claim 1, characterized in that The specific process of step 2 is as follows: The central subarray receives all-one uplink pilot signals sent by the user equipment through a hybrid precoding architecture. The phase of the hybrid precoding architecture is set to the angle-domain undersampling dictionary. The number of all-one uplink pilot signals is one-fourth the number of central subarray antennas, and the number of pilots of the all-one uplink pilot signal is proportional to the number of sampling points in the angle-domain undersampling dictionary. The signal model received by the central sub-array is: Among them, Y cen is the signal received by the central subarray, P is the transmit power of the user equipment, is the angle domain undersampling dictionary of hybrid precoding implemented by analog phase shifter, H cen is the uplink channel between the user equipment and the central sub-array, N is the noise, H cen The expression is as follows: Where L is the number of propagation paths, g cen,l is the gain of the lth path in the central subarray channel, a(·) is the central subarray response vector, p(·) is the multi-subcarrier frequency domain steering vector, θ cen,l are the azimuth and elevation angles of the lth path in the central subarray channel, τ l is the delay of the lth path; The central subarray uses the received all-one uplink pilot signal and the angle domain undersampling dictionary implemented by the analog phase shifter in the hybrid precoding architecture to use the compressed sensing algorithm to calculate the received signal Y cen Processing and calculation are performed to obtain the estimated number of central sub-array propagation paths And the estimated results of the direction angle and delay parameters of each path are the estimated first The azimuth, elevation and delay of each path.

4. The XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing according to claim 1, characterized in that The specific process of step 3 is as follows: Using the estimated number of central subarray propagation paths and the delay parameters of each path Perform multipath orthogonal decoupling on the received signals of other subarrays. Based on the characteristics that different subarrays have the same number of paths and delay parameters, the received signal of the kth other subarray is multipath orthogonally decoupled as follows: in, represents the kth sub-array decoupled from the other sub-arrays Paths receive signals, Y k is the kth other subarray receiving signal, p(·) is the multi-subcarrier frequency domain steering vector, is the kth channel in the other subarray The gain of each path, P is the transmit power of the user equipment, is the angle-domain undersampling dictionary of hybrid precoding implemented by analog phase shifter, a(·) is the central subarray response vector, are the kth channels in the other sub-arrays. The azimuth and elevation angles of the paths, k = 1,...,4, After the kth other sub-array multipath orthogonal decoupling receives the signal, each path channel uses the compressed sensing algorithm to estimate the azimuth and elevation angle of the path, that is, are estimated for the kth other subarrays respectively. The azimuth and elevation angles of each path.

5. The XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing according to claim 1, characterized in that: The specific process of step 4 is as follows: Using the azimuth information of each path estimated by the central subarray and the azimuth information of each path estimated by the four other subarrays, the distance parameter information of all paths of the full array is jointly estimated based on the spatial geometric relationship. And re-estimate the gain of each path using the least squares method According to distance Gain The angle and delay estimated by the central subarray are used to reconstruct the full-dimensional XL-MIMO near-field channel between the user and the base station: in, To reconstruct the full-dimensional near-field channel between the user and the base station, The estimated number of propagation paths for the central subarray, For the The gain of each path, w(·) is the near-field response vector of the full-dimensional subarray, Indicates the The distance from the user / scatterer in the path to the center of the full-dimensional subarray, p(·) is the multi-subcarrier frequency domain steering vector, are the estimated first The azimuth, elevation and delay of each path.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the XL-MIMO near-field CSI estimation method based on spatial geometric distributed processing are implemented as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the XL-MIMO near-field CSI estimation method based on spatial geometry distributed processing are implemented.

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