Decoding method and system for millimeter wave channel environment in massive MIMO system

Abstract

The present invention discloses a decoding method and system in a millimeter wave environment in a large-scale MIMO system. The method is as follows: S1, a base station receives information sent by a user terminal, and makes a preliminary channel estimation based on the information; S2, based on the data obtained by the preliminary estimation, a Hankel matrix is ​​constructed and singular value decomposition is performed to obtain singular values ​​and singular vectors; S3, noise reduction is performed on the signal, and the number of incident angles of the signal reaching the base station antenna is estimated; S4, the path gain is estimated using the obtained angle, and the channel is reconstructed; S5, based on the reconstructed channel, the transmitted signal is estimated. The present invention can estimate the channel information more accurately and further improve the performance of the communication system.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and in particular relates to a new decoding method and system in a millimeter wave channel environment in a large-scale MIMO system. Background Art

[0002] Future wireless communication systems will develop towards high speed and high stability. Massive MIMO is one way to achieve this goal. However, in a massive MIMO system, wireless communication is subject to many environmental interferences, such as shadow fading, frequency selective fading, etc. This poses a great challenge to the effective transmission of signals between user terminals and base stations. Therefore, it is crucial to obtain channel status information.

[0003] For a single-user massive MIMO system, when the channel matrix obeys a complex Gaussian distribution with a mean of zero and a variance of one, the MMSE_ML (minimum mean-square-errors maximum likelihood) method is optimal. However, when the millimeter wave reaches the base station antenna at an azimuth angle θ p MMSE_ML is no longer the optimal decoding method when built.

[0004] Based on the above situation, the present invention provides a new decoding technology solution. In a low signal-to-noise ratio environment, the decoding performance of the new technology solution is much better than the traditional MMSE_ML decoding method. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a new decoding method and system in a millimeter wave channel environment in a large-scale MIMO system, and the present invention improves the accuracy of channel estimation.

[0006] The present invention adopts the following technical solutions:

[0007] The decoding method in the millimeter wave environment of the massive MIMO system is as follows:

[0008] S1. The base station receives the information sent by the user terminal and makes a preliminary channel estimation based on the information;

[0009] S2. Based on the data obtained by preliminary estimation, the corresponding Hankel matrix is ​​constructed and singular value decomposition is performed to obtain singular values ​​and singular vectors;

[0010] S3, reduce noise on the signal and estimate the number of incident angles of the signal reaching the base station antenna;

[0011] S4, using the obtained angle to estimate the path gain, and finally reconstruct the channel;

[0012] S5. Estimate the transmitted signal based on the reconstructed channel.

[0013] Preferably, in step S1, the formula for preliminarily estimating channel information is:

[0014]

[0015] In the above formula, ρ is the signal-to-noise ratio; h = Ac, where

[0016]

[0017] A is the directional response matrix, θ p is the incident angle of the signal transmitted by the user terminal when it reaches the base station antenna through the pth path, M is the total number of antennas of the base station, p is the total number of paths from the user terminal to the base station, c is the channel gain vector, w is the noise vector in the channel, and both c and w obey a complex Gaussian distribution with a mean of zero and a variance of one.

[0018] Preferably, step S2 specifically includes:

[0019] S2.1, the constructed Hankel matrix is:

[0020]

[0021] Among them, K+L-1=M, K>L, For vector The kth component in .

[0022] S2.2, perform singular value decomposition on the Hankel matrix, H = UDV H ,in

[0023] D=(λ1,λ2,…λ L ),λ1≥λ2≥…≥λ L ≥0

[0024] λ i (i=1,2,…,L) are singular values, and the left singular vectors form a K×L matrix

[0025] U=(u1,u2,u3,…,u L )

[0026] u i (i=1,2,…,L) is a K×1 vector that satisfies U H U=I L (The superscript H represents the conjugate transpose, the same below, I L is an L-order unitary matrix) and a right singular vector consisting of an L-order unitary matrix

[0027] V=(v1,v2,…,vL )

[0028] v i (i=1,2,…,L) is a K×1 vector;

[0029] Preferably, step S3 specifically includes:

[0030] S3.1. Use the singular entropy increment curvature spectrum of singular values ​​to reduce the noise of the signal. First, calculate the singular entropy increment:

[0031]

[0032] Secondly, calculate the curvature of each point, the calculation formula is

[0033]

[0034] in

[0035] l” i =E i+1 -2E i +E i-1 (i=2,3,…,L-1)

[0036] l' i =E i+1 -E i (i=2,3,…,L-1)

[0037] S3.2. Find the index corresponding to the maximum curvature value, recorded as Right now

[0038]

[0039] It can be used as an estimate of the channel path.

[0040] S3.3, take the first 1 to K-1 rows of matrix U, the first 1 to The columns are made into a new matrix, denoted as U1, and the first 2 to K rows of matrix U are taken, and the first 1 to The columns are made into a new matrix, denoted as U2, that is, Re-order

[0041] H'=(U1 H U1) -1 U1 H U2

[0042] Calculate the eigenvalue of H' and get the corresponding phase according to each eigenvalue We can get an estimate of A The details are as follows

[0043]

[0044]

[0045] Where eig(H') is the eigenvalue of H', and angle{} is the phase angle of the complex number.

[0046] Preferably, in step S4, according to the Estimate Get the reconstructed channel:

[0047] Preferably, in step S5, the transmitted signal is s, and the signal received by the base station is Assuming s is a QAM signal, we estimate the transmitted signal

[0048]

[0049] Express request Frobenius modulus, arg min() means finding the value of the target variable when the value in the brackets is the smallest.

[0050] Based on the above estimation method, the present invention also discloses a decoding system in a millimeter wave environment in a large-scale MIMO system, which includes a preliminary estimation module, a matrix construction module, an incident angle number estimation module, a channel information recovery module, and a transmission signal estimation module connected in sequence;

[0051] A preliminary estimation module obtains preliminary estimated channel information according to the received signal;

[0052] The matrix construction module constructs the Hankel matrix and performs singular value decomposition based on the data obtained by the preliminary estimation to obtain singular values ​​and singular vectors;

[0053] The module for estimating the number of incident angles is used to reduce the noise of the signal and estimate the number of incident angles at which the signal reaches the base station antenna;

[0054] The channel information recovery module estimates the path gain using the obtained angle and finally obtains the reconstructed channel;

[0055] The transmit signal estimation module estimates the transmit signal according to the reconstructed channel.

[0056] The beneficial effects of the present invention are:

[0057] The present invention proposes a new decoding technology solution in the millimeter wave environment. Compared with the traditional MMSE_ML, when processing the millimeter wave reaching the base station antenna azimuth angle θ pWhen the problem is constructed, the technical solution proposed by the present invention is obviously better than the MMSE_ML method. The present invention can estimate the channel information more accurately and further improve the performance of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 is a flow chart of a new decoding method in a millimeter wave channel environment in a massive MIMO system according to the present invention;

[0060] Figure 2 It is a comparison diagram of decoding symbol error rate using the zf estimation method and the new method proposed by the present invention;

[0061] Figure 3 It is a structural schematic diagram of a new decoding system in a millimeter wave channel environment in the massive MIMO system of the present invention. DETAILED DESCRIPTION

[0062] The following describes the embodiments of the present invention through specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0063] Embodiment 1:

[0064] This embodiment illustrates the method of the present invention through a specific example. The present invention improves the method for MMSE channel estimation in a massive MIMO system.

[0065] The specific application cases are as follows:

[0066] Assume that there is one user, one base station and the number of antennas of the base station is 128. Table 1 below gives general parameter settings, and the AOA estimation and channel estimation are performed according to the parameters in Table 1.

[0067] Table 1 Parameter settings

[0068] parameter set up <![CDATA[Transmitting antenna N t > 1 Receiving antenna M 128 Number of times the user terminal sends training 1 Signal-to-noise ratio -10 -8 -6 -4 -2 0 2 4 6 8 10 Number of radiation paths p 8 <![CDATA[Matrix H QL Number of rows Q]]> 65 <![CDATA[Matrix H QL Number of columns L]]> 64

[0069] like Figure 1 As shown, in this embodiment, a new decoding method in a millimeter wave channel environment in a massive MIMO system includes the following steps:

[0070] S1. The base station receives the information sent by the user terminal and makes a preliminary channel estimation based on the information;

[0071] S2. Based on the data obtained by preliminary estimation, the corresponding Hankel matrix is ​​constructed and singular value decomposition is performed to obtain singular values ​​and singular vectors;

[0072] S3, reduce noise on the signal and estimate the number of incident angles of the signal reaching the base station antenna;

[0073] S4, using the obtained angle to estimate the path gain, and finally obtain the reconstructed channel;

[0074] S5. Estimate the transmitted signal based on the reconstructed channel.

[0075] The present invention provides a new decoding method in a millimeter wave channel environment. Compared with a traditional method, under the condition of known equal amount of channel information, the new decoding method of the present invention can achieve a lower bit error rate, and the performance of the communication system is further improved.

[0076] Specifically:

[0077] Assume that the large-scale antenna system includes one single-antenna user and one base station, and the base station is equipped with 128 antennas. Specifically, the S1 step is that the channel obtained by the user terminal based on the preliminary estimation of the received signal is recorded as

[0078] In this large-scale antenna system, the channel from the base station to the user is expressed as:

[0079]

[0080] Wherein, h=Ac, A is the directional response matrix, c is the channel gain coefficient, and w is Gaussian noise with zero mean and variance of one.

[0081] In step S1, the user terminal sends a training sequence once, and the base station receives the signal y0. The base station preliminarily estimates the channel information using the formula:

[0082]

[0083] Where ρ is the signal-to-noise ratio; h=Ac,

[0084]

[0085] A is the directional response matrix, θ is the incident angle of the signal transmitted by the user terminal when it reaches the base station antenna through the path, c is the channel gain, and w is the noise in the channel. Both c and w obey a complex Gaussian distribution with a mean of zero and a variance of one.

[0086] Step S2 specifically includes the following steps:

[0087] S2.1, the constructed Hankel matrix is:

[0088]

[0089] For vector The kth component in .

[0090] S2.2, perform singular value decomposition on the Hankel matrix, H = UDV H ,in

[0091] D=(λ1,λ2,…λ 64 ),λ1≥λ2≥…≥λ 64 ≥0

[0092] λ i (i=1,2,…,64) are singular values, and the left singular vectors form a K×L matrix

[0093] U=(u1,u2,u3,…,u 64 )

[0094] u i (i=1,2,…,64) is a 65×1 vector that satisfies U H U=I L , the right singular vectors form a unitary matrix of order L

[0095] V=(v1,v2,…,v 64 )

[0096] v i (i=1,2,…,64) is a 64×1 vector;

[0097] Step S3 specifically includes the following steps:

[0098] S3.1. To reduce the noise of the signal using the singular entropy increment curvature spectrum of the singular value, we must first calculate the singular entropy increment:

[0099]

[0100] Secondly, calculate the curvature of each point, the calculation formula is

[0101]

[0102] in

[0103] l” i =E i+1 -2E i +E i-1 (i=2,3,…,63)

[0104] l' i =E i+1 -E i (i=2,3,…,63)

[0105] S3.2. Find the index corresponding to the maximum curvature value, recorded as Right now

[0106]

[0107] It can be used as an estimate of the number of channel paths.

[0108] S3.3, take the first 1 to 64 rows of matrix U, the first 1 to The columns are made into a new matrix, denoted as U1, and the first 2 to 65 rows of matrix U are taken, and the first 1 to The columns are made into a new matrix, denoted as U2, that is, Re-order

[0109] H'=(U1 H U1) -1 U1 H U2

[0110] Calculate the eigenvalue of H' and get the corresponding phase according to each eigenvalue We can get an estimate of A The details are as follows

[0111]

[0112]

[0113] Where eig(H') is the eigenvalue of H', and angle{} is the phase angle of the complex number.

[0114] In step S4, according to the Estimate Get the reconstructed channel:

[0115] In step S5, the transmitted signal is s, and the signal received by the base station is Assuming s is a QAM signal, the estimated transmitted signal is

[0116]

[0117] By comparing it with the original transmitted signal, the estimated error rate can be obtained.

[0118] Embodiment 2:

[0119] Reference Figure 3 As shown, this embodiment provides a new decoding system in a millimeter wave channel environment in a massive MIMO system, based on the estimation method described in the above embodiment 1, including a preliminary estimation module, a matrix construction module, an incident angle number estimation module, a channel information recovery module, and a transmission signal estimation module connected in sequence;

[0120] A preliminary estimation module obtains preliminary estimated channel information according to the received signal;

[0121] The matrix construction module constructs the Hankel matrix and performs singular value decomposition based on the data obtained by the preliminary estimation to obtain singular values ​​and singular vectors;

[0122] The module for estimating the number of incident angles is used to reduce the noise of the signal and estimate the number of incident angles at which the signal reaches the base station antenna;

[0123] The channel information recovery module estimates the path gain using the obtained angle and finally obtains the reconstructed channel;

[0124] The transmit signal estimation module estimates the transmit signal according to the reconstructed channel.

[0125] It should be noted that this embodiment provides a new decoding system in a millimeter wave channel environment in a massive MIMO system, which is similar to the first embodiment and will not be described in detail here.

[0126] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope of the present invention.

Claims

1. A method for decoding a millimeter wave channel environment in a massive MIMO system, characterized by: Follow these steps: S1. The base station receives information sent by the user terminal and makes a preliminary channel estimation based on the information; S2. Based on the data obtained by preliminary estimation, construct the Hankel matrix and perform singular value decomposition to obtain singular values ​​and singular vectors; S3, reduce noise on the signal and estimate the number of incident angles of the signal reaching the base station antenna; S4, using the obtained angle to estimate the path gain and reconstruct the channel; S5. Estimate the transmitted signal according to the reconstructed channel; In step S1, the formula for preliminarily estimating channel information is: In the above formula, ρ is the signal-to-noise ratio; h=Ac, where: A is the directional response matrix, θ p is the incident angle of the signal transmitted by the user terminal when it reaches the base station antenna through the pth path, M is the total number of antennas of the base station, p is the total number of paths from the user terminal to the base station, c is the channel gain vector, w is the noise vector in the channel, and both c and w obey a complex Gaussian distribution with a mean of zero and a variance of one; Step S2 specifically includes: S2.1, the constructed Hankel matrix is: Among them, K+L-1=M, K>L, For vector The kth component in ; S2.2, perform singular value decomposition on the Hankel matrix, H = UDV H ,in: D=(λ1,λ2,…,λ L ),λ1≥λ2≥…≥λ L ≥0 λ1,λ2,…λ L All are singular values, and the left singular vectors form a K×L matrix: U=(u1,u2,u3,…,u L ) u1,u2,u3,…,u L are all K×1 vectors, satisfying U H U=I L And the L-order unitary matrix composed of right singular vectors: V=(v1,v2,…,v L ) v1,v2,…,v L They are all K×1 vectors; Step S3 specifically includes: S3.

1. Use the singular entropy increment curvature spectrum of singular values ​​to reduce the noise of the signal. First, calculate the singular entropy increment: Secondly, calculate the curvature of each point. The calculation formula is: in: the i =E i+1 -2E i +E i-1 i=2,3,…,L-1 the i =And i+1 -AND i i=2,3,…,L-1 S3.

2. Find the index corresponding to the maximum curvature value, recorded as Right now: As an estimate of the channel path; S3.3, take the first 1 to K-1 rows of matrix U, the first 1 to The columns are made into a new matrix, denoted as U1, and the first 2 to K rows of matrix U are taken, and the first 1 to The columns are made into a new matrix, denoted as U2, that is, Then order: H'=(U1 H U1) -1 U1 H U2 Calculate the eigenvalue of H' and get the corresponding phase according to each eigenvalue Get an estimate of A The details are as follows: Among them, eig(H') is to find the eigenvalue of H', and angle{} is to find the phase angle of the complex number; In step S4, according to the Estimate Get the reconstructed channel:

2. The method for decoding the millimeter wave channel environment in a massive MIMO system as claimed in claim 1, characterized in that: In step S5, the transmitted signal is s, and the signal received by the base station is Assume s is a QAM signal, the estimated transmitted signal is: in, Express request Frobenius modulus, argmin() means finding the value of the target variable when the value in the brackets is the smallest.

3. A decoding system for a millimeter wave channel environment in a massive MIMO system, based on the decoding method according to claim 1 or 2, characterized in that It includes a preliminary estimation module, a matrix construction module, an incident angle number estimation module, a channel information recovery module, and a transmission signal estimation module which are connected in sequence; A preliminary estimation module obtains preliminary estimated channel information according to the received signal; The matrix construction module constructs the Hankel matrix and performs singular value decomposition based on the data obtained by the preliminary estimation to obtain singular values ​​and singular vectors; The module for estimating the number of incident angles is used to reduce the noise of the signal and estimate the number of incident angles at which the signal reaches the base station antenna; The channel information recovery module estimates the path gain using the obtained angle and obtains the reconstructed channel; The transmit signal estimation module estimates the transmit signal according to the reconstructed channel.

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