CN117478251A - Ultra-large-scale MIMO near-field channel estimation method and system - Google Patents

Ultra-large-scale MIMO near-field channel estimation method and system Download PDF

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CN117478251A
CN117478251A CN202311463501.9A CN202311463501A CN117478251A CN 117478251 A CN117478251 A CN 117478251A CN 202311463501 A CN202311463501 A CN 202311463501A CN 117478251 A CN117478251 A CN 117478251A
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郭帅帅
曲开千
黄晓丹
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Shandong University
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    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention relates to a method and a system for estimating a super-large-scale MIMO near-field channel, and belongs to the technical field of wireless communication. Comprising the following steps: establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model; forward space smoothing operation is carried out on signals received by a base station; constructing a covariance matrix, introducing a precompensation distance, and performing one-dimensional angle domain MUSIC spectrum search to obtain the estimated angle information of all paths; performing one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths; an array manifold matrix is constructed to obtain an estimated channel. The invention solves the problem of multipath channel signal coherence, changes the two-dimensional search into a plurality of one-dimensional searches by introducing the precompensation distance, greatly reduces the complexity, can be compatible with the scene of only sight distance paths and sight distance and non-sight distance paths simultaneously, and has good estimation precision.

Description

Ultra-large-scale MIMO near-field channel estimation method and system
Technical Field
The invention relates to a method and a system for estimating a super-large-scale MIMO near-field channel, and belongs to the technical field of wireless communication.
Background
Massive Multiple Input Multiple Output (MIMO) is one of the most critical technologies for current 5G communications. Massive MIMO equips a Base Station (BS) with a massive antenna array, which can increase spectral efficiency by several orders of magnitude through beamforming or multiplexing. The 6G system has higher requirements on the aspects of spectrum efficiency, peak rate and the like, and the ultra-large-scale MIMO (XL-MIMO) technology is further evolved and upgraded for the large-scale MIMO technology, so that the improvement of the spectrum efficiency by 10 times can be effectively realized. In addition to array gain from an increase in the number of XL-MIMO antennas, far field to near field conversion is also an important characteristic of XL-MIMO. In the near field, the conventional plane wave assumption is no longer satisfied, but instead is a spherical wave. XL-MIMO near field communication, which benefits from spherical wave characteristics, exhibits great advantages such as high channel freedom, flexible beam focusing [ Cui M, wu Z, lu Y, et al near-Field MIMO Communications for 6G:Fundamentals,Challenges,Potentials,and Future Directions[J ]. IEEE Communications Magazine,2023,61 (1): 40-46]. These advantages are not separated from accurate channel state information. However, the non-uniform phase variation of the spherical wave also causes the near field channel characteristics to change, and accurate near field channel estimation becomes a challenge for near field communication.
Cui M et al demonstrate the polar region sparse nature of the Near Field channel, then build a polar region codebook, propose a polar region orthogonal matching pursuit algorithm (P-OMP) to solve the Near Field channel estimation problem [ Cui M, dai L. Channel Estimation for Extremely Large-Scale MIMO: far-Field or Near-Field? [J] EEE Transactions on Communications,2022,70 (4): 263-2677]. The method for utilizing compressed sensing can effectively reduce pilot frequency overhead. However, this approach assumes that the angle and distance of the channel path are located at discrete sample points in the polar region, while the actual angle and distance are continuous, which results in a loss of resolution.
Unlike the above method, the method of the present invention is intended to solve the problem of near field channel estimation by using a Spatial Spectrum Estimation (SSE) method. In the far-field angular domain sparse channel, the existing literature adopts spatial spectrum estimation to obtain path angle information, and then a least square method (LS) is used for estimating path gain, while the MUSIC algorithm is used for spatial spectrum estimation due to the advantage of high resolution, and the method shows good estimation performance. Although the ultra-large-scale antenna can further improve the estimation accuracy of the MUSIC algorithm, the ultra-large-scale antenna cannot be directly applied to near-field channel estimation. In light of this, a straightforward approach is to estimate the angle and distance of the path using two-dimensional MUSIC (2D-MUSIC), and then estimate the path gain. This direct approach faces the problem of high complexity of the two-dimensional search, inaccurate estimation due to signal coherence of the multipath channel.
Disclosure of Invention
The invention provides a method and a system for estimating a super-large-scale MIMO near-field channel. Specifically, the method considers the trend that the ultra-large-scale antenna array has large aperture and wide coverage of a near field region, and estimates a near field communication channel. First, the user transmits a pilot signal. When a line-of-sight path and a non-line-of-sight path (NLoS) coexist, forward Space Smoothing (FSS) operation is carried out on signals received by a base station, in a first stage, the base station estimates the angles of paths through a one-dimensional angle MUSIC of a precompensation distance, in a second stage, the base station estimates path distances through a one-dimensional distance MUSIC, all path gains are estimated through LS, the problem of multipath channel signal coherence is solved through space smoothing by the proposed algorithm, two-dimensional searching is changed into a plurality of one-dimensional searching through introducing the precompensation distance, complexity is greatly reduced, and meanwhile, the proposed algorithm can be compatible with scenes in which only the line-of-sight path exists simultaneously with the line-of-sight path and the non-line-of-sight path and has good estimation precision.
The technical scheme of the invention is as follows:
a super-large-scale MIMO near-field channel estimation method operates in a near-field communication system, wherein the near-field communication system comprises a base station provided with a plurality of antennas and single-antenna user equipment, and the method comprises the following steps:
establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model;
forward space smoothing operation is carried out on signals received by a base station;
constructing covariance matrix according to the signals obtained after forward space smoothing operation, and introducing precompensation distance r c And performing one-dimensional angle domain MUSIC spectral search of precompensation distanceAcquiring angle information of all estimated paths;
according to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths;
and constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, and estimating the path gain by utilizing least square by combining an array receiving signal model to finally obtain an estimated channel.
According to the invention, preferably, the establishment of the near field communication system model and the near field communication channel model means: the process of receiving the user transmitted pilot signal by the base station through the channel represents a model shown in formula (1), namely a near field communication system model:
Y＝hx T +N (1)
in the formula (1), the components are as follows,representing signals received by the base station; />Representing a near field communication channel model;pilot signals transmitted on behalf of users satisfying the normalized power constraint +.> Representing a obeying mean of 0 and a variance of sigma 2 Complex white gaussian noise, N t And the number of the antennas at the transmitting end of the base station is represented, and L is the length of the pilot frequency signal.
Further preferably, for the near field communication channel, a near field array response vector expression method is used to express a near field communication channel model h as formula (2):
in the formula (2), alpha 0 ，θ 0 And r 0 Respectively represent the gain, angle and distance, alpha of the LoS path k ，θ k And r k Respectively representing the gain, angle and distance of the kth NLoS path, wherein k represents the number of NLoS paths;
in addition, a (·) represents a near field array response vector, expressed as formula (3):
in the formula (3), θ, r represents the angle and distance from the observation point to the center of the antenna array, r i Representing the distance from the observation point to the ith antenna, expressed asWherein->And d represents the antenna spacing.
Further preferably, the re-description of the channel model as an array received signal model means that equation (1) is equivalently modified into equation (4):
Y＝AS+N (4)
in the formula (4), the amino acid sequence of the compound,representing a manifold matrix of an array of received signals, +.>Representing the reconstructed signal, wherein->Representing a gain coefficient vector.
According to the invention, the forward space smoothing operation is preferably performed on the signal received by the base station, which means that: dividing a base station antenna array into Q subarraysEach subarray comprises m=n t -q+1 antennas, constructing a transformation matrix of formula (5) using the first sub-array as a reference array:
Z q ＝[0 M×(q-1) I M 0 M×(Q-q) ] (5)
in the formula (5), 0 M×(q-1) M× (q-1) matrix representing elements of all 0, I M Representing an identity matrix of M×M, 0 M×(Q-q) M x (Q-Q) matrix representing elements of all 0;
obtaining Q received signals Y subjected to forward space smoothing according to the original received signal model shown in the formula (5) and the formula (4) q Q=1, 2, …, Q, expressed as formula (6):
Y q ＝Z q Y (6)。
according to the present invention, it is preferable that the covariance matrix is constructed from the signals obtained after the forward space smoothing operation, which means that: constructing a covariance matrix according to (6)As shown in formula (7):
in the formula (7), the amino acid sequence of the compound,representing the covariance matrix of the original received signal.
According to the invention, preferably, a precompensation distance is introduced, and a one-dimensional angle domain MUSIC spectrum search of the precompensation distance is performed, so as to obtain the estimated angle information of all paths, including:
for covariance matrixSingular value decomposition is performed to obtain formula (8):
in the formula (8), the amino acid sequence of the compound,and Λ represents unitary matrix composed of eigenvectors and eigenvalue composes diagonal matrix respectively; according to the MUSIC algorithm, according to the magnitude of the characteristic value, decomposing U to obtain U= [ U ] S |U N ]Wherein->Representing signal subspace, +.>Representing a noise subspace, assuming that the NLoS path number K is known;
the one-dimensional angle domain MUSIC spectrum of the precompensated distance is represented as formula (9):
in the formula (9), a M (. Cndot.) represents the response vector of the sub-array reference array, rc represents the introduced precompensation distance, Ω θ Representing an angular search space, expressed as:
in the formula (10), θ, r represents the angle and distance from the observation point to the center of the subarray, r i Representing the distance from the observation point to the ith antenna, expressed asWherein->And d represents the antenna spacing.
Further preferably, it is assumed that the base station is known to be within a near field working range or that a near field user is predicted to be likely to occurSurrounding [ r ] min ,r max ]Will r c Is established as a correlation coefficient integral maximization problem as shown in equation (11):
in the formula (11), r min Representing the minimum value of the distance range of the base station in near field operation or the distance range in which near field users may occur, r max Representing the maximum of the distance range of the base station in near field operation or the distance range in which a near field user may be present.
Solving (11) to obtain r c The method comprises the steps of carrying out a first treatment on the surface of the The specific solving steps comprise:
1) Simplifying the problem based on taylor approximation; from the Taylor approximation, one can obtain the equation (10)Further, a correlation coefficient represented by the formula (12) is obtained:
in the formula (12), M represents the number of antennas of the subarray, and ρ represents the correlation coefficient, namely, the part on the right of the about equal sign of the formula (12);
2) Performing a real number taking operation on the correlation coefficient shown in the formula (12); after taking the real numbers, each term in (12) is a cosine expressionreal {>The complex correlation coefficient represented by (12) is approximated to be represented as a real correlation coefficient as shown in the formula (13):
in the formula (13), real {.cndot. } represents the operation of taking a real number,is a constant obtained by equal power sum calculation;
3) Correlation coefficient in the problem of formula (11)a M (θ,r c ) A>Instead, it is simplified to formula (14):
in the formula (14), log (·) represents a natural log function;
4) Solving equation (14) is equivalent to finding equation (14) relative to variable r c Is a zero point of the first derivative of (b) to obtain the formula (15):
finishing the selection of the precompensation distance;
according to the precompensation distance obtained in formula (15), performing spectral peak search on the MUSIC spectrum in formula (IX) to obtain angle estimation of all paths
And finally, introducing a precompensation distance, and performing one-dimensional angle domain MUSIC spectrum search to obtain angle information of all paths.
According to the present invention, preferably, according to the obtained angle information of all paths, a one-dimensional distance domain MUSIC spectrum search is performed to obtain estimated distance information of all paths, including: based on the angle estimation information of each path, MUSIC spectrum search of a distance domain is performed once, as shown in formula (16):
performing K+1 times of distance domain one-dimensional MUSIC spectral peak search according to formula (16), traversing all estimated angles to obtain distance estimation information of all paths
According to the invention, preferably, an array manifold matrix is constructed according to the obtained angle information of all paths and the distance information of all paths, and the channel is finally obtained by estimating the path gain by least square in combination with an array receiving signal model; comprising the following steps:
constructing an estimated array manifold matrix according to the obtained angle information of all paths and the distance information of all paths
And (3) carrying out matched filtering on the received signal shown in the formula (4) to obtain a formula (17):
in the formula (17), the amino acid sequence of the compound,representing the noise term after matched filtering.
Based on the estimated array manifold matrixThe least squares estimate of the path gain is expressed as equation (18):
finally obtaining an estimated channel
A super-MIMO near-field channel estimation system, comprising:
a near field communication system model and a near field communication channel model establishing unit configured to: establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model;
a forward spatial smoothing operation unit configured to: forward space smoothing operation is carried out on signals received by a base station;
a path angle information acquisition unit configured to: constructing a covariance matrix according to the signals obtained after the forward space smoothing operation, introducing a precompensation distance, and carrying out one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain the angle information of all estimated paths;
a path distance information acquisition unit configured to: according to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths;
a channel estimation unit configured to: and constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, and estimating the path gain by utilizing least square by combining an array receiving signal model to finally obtain an estimated channel.
The beneficial effects of the invention are as follows:
the invention provides a method and a system for estimating a near-field communication channel of ultra-large-scale MIMO. The invention solves the problem of multipath channel signal coherence through space smoothing, changes the two-dimensional search into a plurality of one-dimensional searches through introducing a precompensation distance, greatly reduces the complexity, and simultaneously, the provided algorithm can be compatible with scenes in which only the line-of-sight path exists and the line-of-sight and non-line-of-sight paths exist simultaneously and has good estimation precision.
Drawings
FIG. 1 is a schematic diagram of a very large scale MIMO near field communication system to which the present invention applies;
FIG. 2 is a schematic diagram of a forward spatial smoothing algorithm;
FIG. 3 is a graph showing normalized mean square error performance versus results for different methods with different signal-to-noise ratios in the presence of only line-of-sight paths;
FIG. 4 is a graph showing the comparison of normalized mean square error performance of different methods under different signal to noise ratios when line-of-sight and non-line-of-sight paths coexist;
FIG. 5 is a graph showing the effect of different precompensation distances on the normalized mean square error performance of the proposed method;
FIG. 6 is a graph of normalized mean square error performance versus results for different methods for different pilot lengths;
fig. 7 is a graph showing normalized mean square error performance versus results for different methods at different distances.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
A super-large-scale MIMO near-field channel estimation method operates in a near-field communication system, as shown in FIG. 1, which comprises a plurality of N devices t Base station with root antenna and single antenna user equipment, the base station antenna adopts uniform linear array and the distance d between antenna elements is half of wavelength lambda, the user equipment is located in near field range of base station, i.e. the distance between user equipment and base station is less than Rayleigh distance of base stationThe near field communication system adopts a time division duplex working mode, and before formally transmitting communication data, a user transmits pilot signals with the length of L>Channel estimation is performed, and the pilot signal satisfies normalized power constraint +.>Comprising the following steps:
establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model;
forward space smoothing operation is carried out on signals received by a base station;
constructing covariance matrix according to the signals obtained after forward space smoothing operation, and introducing precompensation distance r c Performing one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain the angle information of all estimated paths;
according to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths;
and constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, and estimating the path gain by utilizing least square by combining an array receiving signal model to finally obtain an estimated channel.
Example 2
The ultra-large-scale MIMO near-field channel estimation method according to embodiment 1 is different in that:
establishing a near field communication system model and a near field communication channel model, which means that: the process of receiving the user transmitted pilot signal by the base station through the channel represents a model shown in formula (1), namely a near field communication system model:
Y＝hx T +N (1)
in the formula (1), the components are as follows,representing signals received by the base station;/>representing a near field communication channel model;pilot signals transmitted on behalf of users satisfying the normalized power constraint +.> Representing a obeying mean of 0 and a variance of sigma 2 Complex white gaussian noise, N t And the number of the antennas at the transmitting end of the base station is represented, and L is the length of the pilot frequency signal.
For a near field communication channel, a near field array response vector representation method is adopted, and a near field communication channel model h is represented as formula (2):
in the formula (2), alpha 0 ，θ 0 And r 0 Respectively represent the gain, angle and distance, alpha of the LoS path k ，θ k And r k Respectively representing the gain, angle and distance of the kth NLoS path, wherein k represents the number of NLoS paths;
in addition, a (·) represents a near field array response vector, expressed as formula (3):
in the formula (3), θ, r represents the angle and distance from the observation point to the center of the antenna array, r i Representing the distance from the observation point to the ith antenna, expressed asWherein->And d represents the antenna spacing.
The re-description of the near field communication system model as an array received signal model means that equation (1) is equivalently modified to equation (4):
Y＝AS+N (4)
in the formula (4), the amino acid sequence of the compound,representing a manifold matrix of an array of received signals, +.>Representing the reconstructed signal, wherein->Representing a gain coefficient vector.
Example 3
The ultra-large-scale MIMO near-field channel estimation method according to embodiment 1 or 2 is different in that:
forward space smoothing operation is performed on signals received by the base station, as shown in fig. 2, and refers to: when the NLoS path number K > 0, s=bx in the formula (4) is analyzed T The rank is always 1, is smaller than the actual path number (signal number), generates a signal coherence phenomenon, and in order to ensure the effectiveness of the MUSIC algorithm to be adopted, forward space smoothing decoherence needs to be carried out on the received signal, and the rank of the signal is recovered, specifically, the method is as follows: dividing a base station antenna array into Q sub-arrays, each sub-array containing m=n t -q+1 antennas, constructing a transformation matrix of formula (5) using the first sub-array as a reference array:
Z q ＝[0 M×(q-1) I M 0 M×(Q-q) ] (5)
in the formula (5), 0 M×(q-1) M× (q-1) matrix representing elements of all 0, I M Representing an identity matrix of M×M, 0 M×(Q-q) M x (Q-Q) matrix representing elements of all 0;
according to the original received signal shown in the formula (5) and the formula (4)Model, get Q received signals Y after forward space smoothing q Q=1, 2, …, Q, expressed as formula (6):
Y q ＝Z q Y (6)。
example 4
A method for ultra-large scale MIMO near field channel estimation according to any of embodiments 1-3, which differs in that:
constructing a covariance matrix according to signals obtained after forward space smoothing operation, which means that: constructing a covariance matrix according to (6)As shown in formula (7):
in the formula (7), the amino acid sequence of the compound,representing the covariance matrix of the original received signal.
Introducing a precompensation distance, and performing one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain estimated angle information of all paths, wherein the method comprises the following steps:
for covariance matrixSingular value decomposition is performed to obtain formula (8):
in the formula (8), the amino acid sequence of the compound,and Λ represents unitary matrix composed of eigenvectors and eigenvalue composes diagonal matrix respectively; according to the MUSIC algorithm, according to the magnitude of the characteristic value, decomposing U to obtain U= [ U ] S |U N ]Wherein->Representing signal subspace, +.>Representing a noise subspace, assuming that the NLoS path number K is known;
the one-dimensional angle domain MUSIC spectrum of the precompensated distance is represented as formula (9):
in the formula (9), a M (. Cndot.) represents the response vector of the sub-array reference array, rc represents the introduced precompensation distance, Ω θ The angular search space is expressed, similarly to equation (3), as:
in the formula (10), θ, r represents the angle and distance from the observation point to the center of the subarray, r i Representing the distance from the observation point to the ith antenna, expressed asWherein->And d represents the antenna spacing.
The selection criterion for the precompensation distance is to make rc correspond to the array response vector a M (θ,r c ) Array response vector a corresponding to true distance M Correlation coefficient between (θ, r)As large as possible [ He, J, shu, T.Effect of Approximate Planar Wavefront on Far-Field Direction Finding [ J ]].IEEE Communications Letters,2022,26(3):657-661.]. Reasonable assumption is made that the distance over which the base station works in the near field is knownRange or range of distances [ r ] that predicts the likely presence of near field users min ,r max ]Will r c Is established as a correlation coefficient integral maximization problem as shown in equation (11):
in the formula (11), r min Representing the minimum value of the distance range of the base station in near field operation or the distance range in which near field users may occur, r max Representing the maximum of the distance range of the base station in near field operation or the distance range in which a near field user may be present.
Solving (11) to obtain r c The method comprises the steps of carrying out a first treatment on the surface of the The specific solving steps comprise:
1) Simplifying the problem based on taylor approximation; from the Taylor approximation, one can obtain the equation (10)Further, a correlation coefficient represented by the formula (12) is obtained:
in the formula (12), M represents the number of antennas of the subarray, and ρ represents the correlation coefficient, namely, the part on the right of the about equal sign of the formula (12);
2) Because the real number of the complex correlation coefficient does not affect the measurement of the correlation, the real number of the correlation coefficient shown in the formula (12) is obtained; taking the real number, each term in equation (12) is a cosine expressionreal {The complex correlation coefficient represented by the formula (12) is expressed approximately as a real correlation coefficient as shown in the formula (13):
in the formula (13), real {.cndot. } represents the operation of taking a real number,is a constant obtained by equal power sum calculation;
3) Correlation coefficient in the problem of formula (11)A>Instead, it is simplified to formula (14):
in the formula (14), log (·) represents a natural log function;
4) Solving equation (14) is equivalent to finding equation (14) relative to variable r c Is a zero point of the first derivative of (b) to obtain the formula (15):
finishing the selection of the precompensation distance;
according to the precompensation distance obtained in the formula (15), performing spectral peak search on the MUSIC spectrum in the formula (9) to obtain angle estimation of all paths
And finally, introducing a precompensation distance, and performing one-dimensional angle domain MUSIC spectrum search to obtain angle information of all paths.
According to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths, wherein the method comprises the following steps: based on the angle estimation information of each path, MUSIC spectrum search of a distance domain is performed once, as shown in formula (16):
performing K+1 times of distance domain one-dimensional MUSIC spectral peak search according to formula (16), traversing all estimated angles to obtain distance estimation information of all paths
Example 5
The method for estimating a near-field channel of a super-MIMO according to any one of embodiments 1 to 4, wherein:
constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, combining an array receiving signal model, estimating path gain by utilizing least squares, and finally obtaining an estimated channel; comprising the following steps:
constructing an estimated array manifold matrix according to the obtained angle information of all paths and the distance information of all paths
And (3) carrying out matched filtering on the received signal shown in the formula (4) to obtain a formula (17):
in the formula (17), the amino acid sequence of the compound,representing the noise term after matched filtering.
Based on the estimated array manifold matrixThe least squares estimate of the path gain is expressed as equation (18):
finally obtaining an estimated channel
In the present embodiment, the carrier wavelength λ=10mm, the carrier frequency f c Set number N of antennas of uniform linear array of base station =30 GHz t =129, the number of subarray antennas for forward spatial smoothing m=105, assuming a user or scatterer to base station distance of [8,30]In-meter generation, the angle between the user or the scattering body and the base station is minus 60 DEG, 60 DEG]In-generation, the estimation performance is measured by Normalized Mean Square Error (NMSE) of channel estimation, which is defined asIn this embodiment, the proposed method is compared with the existing polar-domain P-OMP method, the least squares LS method, and the conventional 2-D MUSIC.
Fig. 3 shows the channel estimation performance of different methods under different signal-to-noise ratios only when the line-of-sight path exists, the pilot length is set to l=1, and it can be seen that the method proposed by the present invention is due to the existing polar region P-OMP and least squares LS method. The traditional 2-D MUSIC is directly expanded to have slightly better performance under the condition of only LoS because of larger array aperture. However, as can be seen from the performance result graph of the case where the line-of-sight path and the non-line-of-sight path shown in fig. 4 exist simultaneously (the number of the non-line-of-sight paths is set to k=3), the multiple paths cause signal coherence, the performance loss of the conventional 2-D MUSIC algorithm is larger, and the forward space smoothing algorithm is introduced in the method provided by the invention to resist multipath coherence phenomenon, so that the method still has good performance. In addition, the algorithm provided by the invention decomposes the two-dimensional search into one-dimensional search, so that the complexity is greatly reduced. By combining fig. 3 and fig. 4, the method provided by the invention has good performance when only the view path and the view path exist in the same scene as the non-view path while reducing complexity.
Fig. 5 shows the effect of different precompensation distances on the performance of the proposed method, and the number of non-line-of-sight paths k=3, it can be seen that the normalized mean square error performance is best around the precompensation distance deduced in this example (15), indicating the correctness of the precompensation distance deduced in the present invention.
Fig. 6 shows the performance of different methods under different pilot lengths, where the signal-to-noise ratio is set to 5dB and the number of non-line-of-sight paths k=3, and it can be seen that the performance of the proposed method exceeds that of the comparison algorithm, which shows that the proposed method also has advantages in terms of low pilot overhead.
FIG. 7 shows a graph of normalized mean square error performance versus results for different methods at different distances, assuming the same distance but different angles between the user and scatterer, and maintaining a pilot length of 1, a number of non-line-of-sight paths of 3, and a signal-to-noise ratio of 5dB, to highlight the effect of distance variation on NMSE performance, it can be seen from the curve of the proposed method that the closer the actual distance is to the precompensated distance r c The higher the estimation accuracy, the higher the accuracy. The proposed algorithm is superior to the comparison algorithm over most distances during the distance change.
Example 6
A super-MIMO near-field channel estimation system, comprising:
a near field communication system model and a near field communication channel model establishing unit configured to: establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model;
a forward spatial smoothing operation unit configured to: forward space smoothing operation is carried out on signals received by a base station;
a path angle information acquisition unit configured to: constructing a covariance matrix according to the signals obtained after the forward space smoothing operation, introducing a precompensation distance, and carrying out one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain the angle information of all estimated paths;
a path distance information acquisition unit configured to: according to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths;
a channel estimation unit configured to: and constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, and estimating the path gain by utilizing least square by combining an array receiving signal model to finally obtain an estimated channel.

Claims (10)

1. The ultra-large-scale MIMO near-field channel estimation method is characterized by running in a near-field communication system, wherein the near-field communication system comprises a base station provided with a plurality of antennas and single-antenna user equipment, and the method comprises the following steps of:
establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model;
forward space smoothing operation is carried out on signals received by a base station;
constructing a covariance matrix according to the signals obtained after the forward space smoothing operation, introducing a precompensation distance, and carrying out one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain the angle information of all estimated paths;
according to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths;
and constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, and estimating the path gain by utilizing least square by combining an array receiving signal model to finally obtain an estimated channel.
2. The method for estimating a near-field channel of a super-large-scale MIMO according to claim 1, wherein the step of establishing a near-field communication system model and a near-field communication channel model is: the process of receiving the user transmitted pilot signal by the base station through the channel represents a model shown in formula (1), namely a near field communication system model:
Y＝hx T +N (1)
in the formula (1), the components are as follows,representing signals received by the base station; />Representing a near field communication channel model; />Pilot signals transmitted on behalf of users satisfying the normalized power constraint +.> Representing a obeying mean of 0 and a variance of sigma 2 Complex white gaussian noise, N t The number of antennas at a transmitting end of a base station is represented, and L is the length of a pilot frequency signal;
further preferably, for the near field communication channel, a near field array response vector expression method is used to express a near field communication channel model h as formula (2):
in the formula (2), alpha 0 ，θ 0 And r 0 Respectively represent the gain, angle and distance, alpha of the LoS path k ，θ k And r k Respectively representing the gain, angle and distance of the kth NLoS path, wherein k represents the number of NLoS paths;
in addition, a (·) represents a near field array response vector, expressed as formula (3):
in the formula (3), θ, r represents the angle and distance from the observation point to the center of the antenna array, r i Representing the distance from the observation point to the ith antenna, expressed asWherein->And d represents the antenna spacing.
3. The method of claim 2, wherein re-describing the channel model as an array received signal model means equivalent transformation of equation (1) to equation (4):
Y＝AS+N (4)
in the formula (4), the amino acid sequence of the compound,representing a manifold matrix of an array of received signals,representing the reconstructed signal, wherein->Representing a gain coefficient vector.
4. The method for estimating a near-field channel of a super-MIMO system according to claim 3, wherein performing forward spatial smoothing on the signal received by the base station means: dividing a base station antenna array into Q sub-arrays, each sub-array containing m=n t -q+1 antennas, constructing a transformation matrix of formula (5) using the first sub-array as a reference array:
Z q ＝[0 M×(q-1) I M 0 M×(Q-q) ] (5)
in the formula (5), 0 M×(q-1) M× (q-1) matrix representing elements of all 0, I M Representing an identity matrix of M×M, 0 M×(Q-q) M x (Q-Q) matrix representing elements of all 0;
obtaining Q received signals Y subjected to forward space smoothing according to the original received signal model shown in the formula (5) and the formula (4) q Q=1, 2, …, Q, expressed as formula (6):
Y q ＝Z q Y (6)。
5. the method for estimating a near-field channel of a super-massive MIMO system according to claim 4, wherein the constructing a covariance matrix from the signals obtained after the forward spatial smoothing operation means: constructing a covariance matrix according to (6)As shown in formula (7):
in the formula (7), the amino acid sequence of the compound,representing the covariance matrix of the original received signal.
6. The method for estimating near-field channel of ultra-large-scale MIMO according to claim 1, wherein introducing a precompensation distance and performing a one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain angle information of all estimated paths comprises:
for covariance matrixSingular value decomposition is performed to obtain formula (8):
in the formula (8), the amino acid sequence of the compound,and Λ represents unitary matrix composed of eigenvectors and eigenvalue composes diagonal matrix respectively; according to the MUSIC algorithm, according to the magnitude of the characteristic value, decomposing U to obtain U= [ U ] S |U N ]Wherein->Representing signal subspace, +.>Representing a noise subspace, assuming that the NLoS path number K is known;
the one-dimensional angle domain MUSIC spectrum of the precompensated distance is represented as formula (9):
in the formula (9), a M (. Cndot.) represents the response vector of the sub-array reference array, r c Representing the introduced precompensation distance, Ω θ Representing an angular search space, expressed as:
in the formula (10), θ, r represents the angle and distance from the observation point to the center of the subarray, r i Representing the distance from the observation point to the ith antenna, expressed asWherein->And d represents the antenna spacing.
7. The method of claim 1, wherein the distance range of near-field operation of the base station is assumed to be known or the distance range of near-field user is predicted to be possible min ,r max ]Will r c Is established as a correlation coefficient integral maximization problem as shown in equation (11):
in the formula (11), r min Representing the minimum value of the distance range of the base station in near field operation or the distance range in which near field users may occur, r max Representing the maximum value of the distance range of the base station in near field operation or the distance range in which a near field user may occur;
further preferably, the formula (11) is solved to obtain r c The method comprises the steps of carrying out a first treatment on the surface of the The specific solving steps comprise:
1) Simplifying the problem based on taylor approximation; from the Taylor approximation, we obtain the equation (10)Further, a correlation coefficient represented by the formula (12) is obtained:
in the formula (12), M represents the number of antennas of the subarray, and ρ represents the correlation coefficient, namely, the part on the right of the about equal sign of the formula (12);
2) Performing a real number taking operation on the correlation coefficient shown in the formula (12); taking the real number, each term in equation (12) is a cosine expressionreal {>The complex correlation coefficient represented by the formula (12) is expressed approximately as a real correlation coefficient as shown in the formula (13):
in the formula (13), real {.cndot. } represents the operation of taking a real number,is a constant obtained by equal power sum calculation;
3) Correlation coefficient in the problem of formula (11)A>Instead, it is simplified to formula (14):
in the formula (14), log (·) represents a natural log function;
4) Solving equation (14) is equivalent to finding equation (14) relative to variable r c Is a zero point of the first derivative of (b) to obtain the formula (15):
finishing the selection of the precompensation distance;
according to the precompensation distance obtained in the formula (15), performing spectral peak search on the MUSIC spectrum in the formula (9) to obtain angle estimation of all paths
And finally, introducing a precompensation distance, and performing one-dimensional angle domain MUSIC spectrum search to obtain angle information of all paths.
8. The method for estimating a near-field channel of a super-large-scale MIMO according to claim 1, wherein performing a one-dimensional distance domain MUSIC spectrum search according to the obtained angle information of all paths to obtain the estimated distance information of all paths comprises: based on the angle estimation information of each path, MUSIC spectrum search of a distance domain is performed once, as shown in formula (16):
performing K+1 times of distance domain one-dimensional MUSIC spectral peak search according to formula (16), traversing all estimated angles to obtain distance estimation information of all paths
9. The method for estimating a near-field channel of a super-large-scale MIMO according to any one of claims 1-8, wherein an array manifold matrix is constructed according to the obtained angle information of all paths and the distance information of all paths, and an estimated channel is finally obtained by using least square estimated path gain in combination with an array received signal model; comprising the following steps:
constructing an estimated array manifold matrix according to the obtained angle information of all paths and the distance information of all paths
And (3) performing matched filtering on the received signal shown in the formula (4) to obtain (17):
in the formula (17), the amino acid sequence of the compound,representing noise terms after matched filtering;
based on the estimated array manifold matrixThe least squares estimate of the path gain is expressed as equation (18):
finally obtaining an estimated channel
10. A super-MIMO near-field channel estimation system, comprising:
a near field communication system model and a near field communication channel model establishing unit configured to: establishing a near field communication system model and a near field communication channel model, and re-describing the near field communication system model as an array receiving signal model;
a forward spatial smoothing operation unit configured to: forward space smoothing operation is carried out on signals received by a base station;
a path angle information acquisition unit configured to: constructing a covariance matrix according to the signals obtained after the forward space smoothing operation, introducing a precompensation distance, and carrying out one-dimensional angle domain MUSIC spectrum search of the precompensation distance to obtain the angle information of all estimated paths;
a path distance information acquisition unit configured to: according to the obtained angle information of all paths, carrying out one-dimensional distance domain MUSIC spectrum search to obtain estimated distance information of all paths;
a channel estimation unit configured to: and constructing an array manifold matrix according to the obtained angle information of all paths and the distance information of all paths, and estimating the path gain by utilizing least square by combining an array receiving signal model to finally obtain an estimated channel.
CN202311463501.9A 2023-11-06 2023-11-06 Ultra-large-scale MIMO near-field channel estimation method and system Pending CN117478251A (en)

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