CN109462559B - Sparse millimeter wave channel estimation method in presence of mutual coupling - Google Patents

Sparse millimeter wave channel estimation method in presence of mutual coupling Download PDF

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CN109462559B
CN109462559B CN201811418880.9A CN201811418880A CN109462559B CN 109462559 B CN109462559 B CN 109462559B CN 201811418880 A CN201811418880 A CN 201811418880A CN 109462559 B CN109462559 B CN 109462559B
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mutual coupling
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陈鹏
曹振新
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Southeast University
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Abstract

The invention discloses a sparse millimeter wave channel estimation method in the presence of mutual coupling, which comprises the following steps: constructing a sparse signal model when mutual coupling exists between the antennas; initializing mutual coupling parameters in the model; estimating channel parameters of a sparse millimeter wave communication path according to the current mutual coupling parameter value and a sparse reconstruction algorithm based on compressed sensing; deriving a conjugate partial derivative of the target function about a mutual coupling coefficient between the transmitting antennas and the receiving antennas according to the channel parameters; solving a conjugate partial derivative, and realizing estimation of a mutual coupling parameter based on a steepest descent method; and iteratively calculating the millimeter wave channel parameters and the mutual coupling parameters until the set iteration times is reached, and then stopping the calculation, and outputting the estimation results of the millimeter wave channel parameters and the mutual coupling parameters. According to the invention, the space domain sparse characteristics of the millimeter wave channel are fully utilized, the mutual coupling vector is introduced into the sparse signal model based on the compressed sensing, the millimeter wave channel estimation performance is effectively improved by adopting the sparse reconstruction method based on the compressed sensing, and the high-precision estimation of the millimeter wave channel is realized.

Description

Sparse millimeter wave channel estimation method in presence of mutual coupling
Technical Field
The invention belongs to the technical field of millimeter wave communication, and relates to a sparse millimeter wave channel estimation method in the presence of mutual coupling.
Background
The millimeter wave communication technology is one of the key technologies of future 5G cellular communication, but the attenuation of the electromagnetic signal path adopting the millimeter wave frequency band is very large, so that more antennas, beam forming and other technologies are needed to compensate the energy lost in the transmission process of the electromagnetic wave signal. In addition, the channel estimation process is an important general component, and the parameters of the channel can be obtained through estimation, so that the millimeter wave communication performance is effectively improved.
The high attenuation characteristic of the millimeter wave signal causes the space domain sparse characteristic of the channel, so that a sparse-based method is provided in the industry to convert the channel estimation problem into the sparse reconstruction problem by mining the natural sparse characteristic of the millimeter wave channel. Such as a Compressed Sensing (CS) channel estimation algorithm based on low rank and sparse structure combination. However, in an actual millimeter wave communication system, the channel estimation performance is reduced due to the non-ideality of the antenna array, and although the existing method is studied on channel estimation in the presence of mutual coupling, a method for mining the millimeter wave sparse feature and the array non-ideality at the same time has not been proposed.
The existing millimeter wave channel estimation technology is considered comprehensively, and the following problems are mainly faced:
1. the influence of mutual coupling between antennas in an actual millimeter wave communication system on the channel estimation performance cannot be fully considered;
2. mutual coupling and channel sparsity characteristics cannot be considered at the same time, and further improvement of channel estimation performance is limited.
Disclosure of Invention
In order to solve the problems, the invention provides a sparse millimeter wave channel estimation method in mutual coupling, which solves the problem of mutual coupling among antennas in the existing millimeter wave communication and improves the estimation performance of a millimeter wave channel under the condition of sparse airspace of the millimeter wave channel by a sparse reconstruction theory based on compressed sensing.
In order to achieve the purpose, the invention provides the following technical scheme:
a sparse millimeter wave channel estimation method in the presence of mutual coupling comprises the following steps:
step 1, constructing a sparse channel millimeter wave signal model when mutual coupling exists among antennas;
step 2, initializing unknown parameters in the signal model, wherein the unknown parameters comprise millimeter wave channel parameters and receiving and transmitting end antenna mutual coupling coefficients;
step 3, estimating parameters of a sparse millimeter wave channel according to the current unknown parameter value and a sparse reconstruction algorithm based on compressed sensing;
step 4, according to the millimeter wave channel parameters obtained by current estimation, a conjugate partial derivative of the target function about the mutual coupling coefficient between the receiving and transmitting antennas is obtained through deduction;
step 5, solving the conjugate partial derivative of the target function and the mutual coupling coefficient, and realizing the estimation of the mutual coupling coefficient based on the steepest descent method;
and 6, iterating and calculating the steps 3 to 5 until the set iteration times are reached, stopping, solving the millimeter wave channel parameters through the sparse vectors obtained through estimation, and finally outputting the channel parameters and the mutual coupling coefficients.
Further, the signal model constructed in step 1 is:
Figure BDA0001880117190000021
wherein r ═ r (0), r (T)s),...,r((P-1)Ts)]For P sampled signals received by the antenna, the sampling period is Ts(ii) a The definition of the matrix Ψ is
Figure BDA0001880117190000022
a(pTs) Indicating the transmitting end is at pTsAntenna beamforming weights for time instants, b (pT)s) Indicating that the receiving end is pTsA beamforming weight for a time instant; qEAnd QFRespectively representing matrixes formed by the discretized angle of the direction of arrival and the direction of departure of the millimeter wave channel and vector manifold vector elements formed by the antenna array;
Figure BDA0001880117190000023
where u represents a sparse vector and where u represents a sparse vector,
Figure BDA0001880117190000024
represents Nt×NtThe unit matrix of (a) is,
Figure BDA0001880117190000025
represents Nr×NrIdentity matrix of NtDiscretizing the number of points, N, for the DoArDiscretizing the number of points for the DoD, cTRepresenting the mutual coupling coefficient vector between the antennas of the transmitting end, cRRepresenting the mutual coupling coefficient vector between the antennas of the receiving end; n represents additive white gaussian noise.
Further, the parameters of the sparse millimeter wave channel estimated in step 3 at least include: channel coefficients, direction of arrival and direction of departure.
Further, the sparse reconstruction algorithm based on the compressed sensing in the step 3 is an orthogonal matching tracking algorithm or a sparse bayesian algorithm.
Further, the objective function in step 4 is a mean square error between the reconstructed signal and the received signal.
Further, the objective function in step 4 is specifically defined as:
Figure BDA0001880117190000026
and the derived conjugate partial derivative is:
Figure BDA0001880117190000027
Figure BDA0001880117190000028
wherein xiTA matrix formed by the mutual coupling vector, the channel coefficient and the beam forming weight of the receiving end; omegaTIs a fixed matrix; xiRAnd forming a matrix formed by the mutual coupling vector, the channel coefficient and the beam forming weight of the transmitting end.
Further, the system operating frequency of the millimeter wave channel is located in a millimeter wave frequency band.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention makes full use of the space domain sparse characteristics of the millimeter wave channel, makes up the influence of mutual coupling between the antennas of the millimeter wave communication system on channel estimation, and effectively improves the millimeter wave channel estimation performance by adopting a sparse reconstruction method based on compressed sensing.
2. According to the invention, the mutual coupling vector is introduced into a sparse signal model based on compressed sensing, and the estimation value of the mutual coupling coefficient is updated according to an iterative algorithm, so that the millimeter wave channel estimation performance loss caused by the mutual coupling is compensated.
3. According to the method, the problem of millimeter wave channel estimation is modeled into the problem of sparse reconstruction based on compressed sensing, so that the sparse characteristics of the millimeter wave channel can be fully excavated, and the estimation performance of the millimeter wave channel is improved.
4. According to the method, aiming at unknown mutual coupling coefficients, iterative estimation is carried out by adopting a steepest descent method, and conjugate partial derivatives of the target function about the mutual coupling coefficients are theoretically deduced, so that iterative estimation of the mutual coupling coefficients is obtained.
Drawings
Fig. 1 is a system block diagram of the present invention applied to a millimeter wave communication system.
FIG. 2 is a flow chart of the algorithm implementation of the present invention.
FIG. 3 shows the channel estimation performance (mutual coupling factor-15 dB) under different Signal-to-Noise ratios (SNR) according to the present invention.
Fig. 4 shows the channel estimation performance (mutual coupling factor-10 dB) for different snr according to the present invention.
Fig. 5 shows the channel estimation performance under different mutual coupling strengths according to the present invention.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention.
The invention provides a sparse millimeter wave channel estimation method in the presence of mutual coupling, which is used for solving the problem of high-precision channel estimation under the condition of unknown mutual coupling between antennas. The invention aims to fully excavate the sparse characteristics of a channel, and construct a sparse channel estimation method based on a compressive sensing theory through reasonably modeling mutual coupling parameters and a sparse channel so as to effectively improve the channel estimation performance in millimeter wave communication. The invention provides an iterative solution algorithm for alternately optimizing the coefficients of channels, mutual coupling and the like; the method can be applied to a millimeter wave communication system (the system operating frequency is greater than or equal to 20GHz, and the number of paths in the communication process is small) shown in fig. 1, a logic flow chart of the method is shown in fig. 2, and the operating process includes the following steps:
step 1, constructing a sparse channel millimeter wave signal model when antennas are mutually coupled.
First, for the millimeter wave wireless communication system shown in fig. 1, the received signal may be modeled as
Figure BDA0001880117190000031
Wherein r ═ r (0), r (T)s),...,r((P-1)Ts)]For P sampled signals received by the antenna, the sampling period is Ts(ii) a The definition of the matrix Ψ is
Figure BDA0001880117190000041
a(pTs) Indicating the transmitting end is at pTsAntenna beamforming weights for time instants, b (pT)s) Indicating that the receiving end is pTsA beamforming weight for a time instant; qEAnd QFRespectively representing matrixes formed by vector manifold vector elements formed by the discretized angles of a millimeter wave channel DoA (Direction of arrival) and a DoD (Direction of departure);
Figure BDA0001880117190000042
where u represents a sparse vector and where u represents a sparse vector,
Figure BDA0001880117190000043
represents Nt×NtThe unit matrix of (a) is,
Figure BDA0001880117190000044
represents Nr×NrIdentity matrix of NtDiscretizing the number of points, N, for the DoArDiscretizing the number of points for the DoD, cTRepresenting the mutual coupling coefficient vector between the antennas of the transmitting end, cRRepresenting the mutual coupling coefficient vector between the antennas of the receiving end; n represents Additive White Gaussian Noise (AWGN).
By constructing a mutual coupling vector cTAnd cRThe original millimeter wave channel estimation problem can be converted into a sparse reconstruction problem for the sparse vector u.
Step 2, initializing unknown parameters, which mainly comprise antenna mutual coupling coefficients, channel parameters and the like;
step 3, estimating parameters of a sparse millimeter wave channel, including channel coefficients, direction of arrival, direction of departure and the like, according to the current unknown parameter value and a sparse reconstruction algorithm based on compressed sensing; the channel parameters include DoD, DoA, channel coefficients and the like, and the sparse reconstruction algorithm includes, but is not limited to, Orthogonal Matching Pursuit (OMP), a sparse bayes algorithm and the like.
Step 4, according to the millimeter wave channel parameters obtained by current estimation, a conjugate partial derivative of the target function about the mutual coupling coefficient between the receiving and transmitting antennas is obtained through deduction;
in this example, the mean square error of the objective function selection reconstruction signal and the received signal can be defined as
Figure BDA0001880117190000045
Figure BDA0001880117190000046
Then the following conjugate partial derivatives can be found:
Figure BDA0001880117190000047
Figure BDA0001880117190000048
wherein xiTA matrix formed by the mutual coupling vector, the channel coefficient and the beam forming weight of the receiving end; omegaTIs a fixed matrix; xiRAnd forming a matrix formed by the mutual coupling vector, the channel coefficient and the beam forming weight of the transmitting end. Other functional formulas may also be selected for the objective function.
And 5, estimating the mutual coupling coefficient, wherein the iterative parameter estimation based on the steepest descent method is realized by solving the conjugate partial derivative of the objective function and the mutual coupling coefficient in the process.
And 6, iterating and calculating the steps 3 to 5 until the set iteration times are reached, stopping, solving the millimeter wave channel parameters through the sparse vectors obtained through estimation, and finally outputting the channel parameters and the mutual coupling coefficients.
The following provides a verification example of the present invention, which verifies that the present invention can make up for the performance loss of millimeter wave channel estimation caused by mutual coupling, and obtain better estimation performance.
Figure BDA0001880117190000051
TABLE 1 simulation parameters
For the millimeter wave communication system, the simulation parameters in table 1 are adopted to compare the current main channel estimation methods, including the OMP algorithm (within MC effect) when there is no mutual coupling effect, the OMP algorithm (within MC information) with perfectly known mutual coupling information, the OMP algorithm (within MC information) that ignores the mutual coupling information, and the sparse millimeter wave channel estimation method (deployed method) when there is mutual coupling provided by the present invention. Fig. 3 shows the channel estimation performance under different signal-to-noise ratios, wherein the mutual coupling between the antennas is-15 dB, as shown in the figure, when the mutual coupling influence exists, the method provided by the invention can effectively improve the estimation performance of the sparse millimeter wave channel. When the signal-to-noise ratio is 20dB, the root mean square error of the estimation of the traditional OMP algorithm is 0.899 degrees, and when the method is adopted, the root mean square error can be reduced to 0.800 degrees, which is equivalent to the estimation performance when the signal-to-noise ratio is 12dB, so the estimation performance can be improved by 8 dB.
Fig. 4 shows the sparse millimeter wave estimation performance under different signal-to-noise ratios, where the mutual coupling between antennas is-10 dB, and it can be seen from the diagram that when the signal-to-noise ratio is 20dB, the root mean square error of the channel estimation of the conventional OMP algorithm is 1.276 °, whereas the estimation performance of the method provided by the present invention is 0.948 °, which is improved by 25.7%, so that when the mutual coupling is strong, the method provided by the present invention can provide a millimeter wave channel estimation result with higher accuracy. Fig. 5 shows the sparse millimeter wave channel estimation performance under different mutual coupling conditions, and it can be seen from the graph that the channel estimation performance is significantly deteriorated when the mutual coupling coefficient is higher than-10 dB, and when the mutual coupling coefficient is lower than-10 dB, the mutual coupling between the antennas can be reduced by 4dB by the proposed method. Therefore, the method provided by the invention can obviously improve the estimation precision in the sparse millimeter wave channel estimation when mutual coupling exists.
In conclusion, the method realizes the estimation of the millimeter wave channel by constructing a sparse channel model when mutual coupling exists in millimeter wave communication and adopting a sparse reconstruction algorithm based on compressed sensing, improves the channel estimation performance and realizes the high-precision estimation of the millimeter wave channel when mutual coupling exists.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.

Claims (7)

1. A sparse millimeter wave channel estimation method in the presence of mutual coupling is characterized by comprising the following steps:
step 1, constructing a sparse channel millimeter wave signal model when mutual coupling exists among antennas;
step 2, initializing unknown parameters in the signal model, wherein the unknown parameters comprise millimeter wave channel parameters and receiving and transmitting end antenna mutual coupling coefficients;
step 3, estimating parameters of a sparse millimeter wave channel according to the current unknown parameter value and a sparse reconstruction algorithm based on compressed sensing;
step 4, according to the millimeter wave channel parameters obtained by current estimation, a conjugate partial derivative of the target function about the mutual coupling coefficient between the receiving and transmitting antennas is obtained through deduction;
step 5, solving the conjugate partial derivative of the target function and the mutual coupling coefficient, and realizing the estimation of the mutual coupling coefficient based on the steepest descent method;
and 6, iterating and calculating the steps 3 to 5 until the set iteration times are reached, stopping, solving the millimeter wave channel parameters through the sparse vectors obtained through estimation, and finally outputting the channel parameters and the mutual coupling coefficients.
2. The method according to claim 1, wherein the signal model constructed in step 1 is:
Figure FDA0001880117180000011
wherein r ═ r (0), r (T)s),...,r((P-1)Ts)]For P sampled signals received by the antenna, the sampling period is Ts(ii) a The definition of the matrix Ψ is
Figure FDA0001880117180000012
a(pTs) Indicating the transmitting end is at pTsAntenna beamforming weights for time instants, b (pT)s) Indicating that the receiving end is pTsA beamforming weight for a time instant; qEAnd QFRespectively representing matrixes formed by the discretized angle of the direction of arrival and the direction of departure of the millimeter wave channel and vector manifold vector elements formed by the antenna array;
Figure FDA0001880117180000013
where u represents a sparse vector and where u represents a sparse vector,
Figure FDA0001880117180000014
represents Nt×NtThe unit matrix of (a) is,
Figure FDA0001880117180000015
represents Nr×NrIdentity matrix of NtDiscretizing the number of points, N, for the DoArDiscretizing the number of points for the DoD, cTRepresenting the mutual coupling coefficient vector between the antennas of the transmitting end, cRRepresenting the mutual coupling coefficient vector between the antennas of the receiving end; n represents additive white gaussian noise.
3. The method according to claim 1, wherein the parameters of the sparse millimeter wave channel estimated in step 3 at least comprise: channel coefficients, direction of arrival and direction of departure.
4. The method for channel estimation of sparse millimeter waves in the presence of mutual coupling according to claim 1 or 3, wherein the sparse reconstruction algorithm based on compressed sensing in the step 3 is an orthogonal matching tracking algorithm or a sparse Bayesian algorithm.
5. The method as claimed in claim 1, wherein the objective function in step 4 is the mean square error between the reconstructed signal and the received signal.
6. The method according to claim 5, wherein the objective function in step 4 is specifically defined as:
Figure FDA0001880117180000021
and the derived conjugate partial derivative is:
Figure FDA0001880117180000022
Figure FDA0001880117180000023
wherein xiTA matrix formed by the mutual coupling vector, the channel coefficient and the beam forming weight of the receiving end; omegaTIs a fixed matrix; xiRAnd forming a matrix formed by the mutual coupling vector, the channel coefficient and the beam forming weight of the transmitting end.
7. The method of claim 1, wherein the system operating frequency of the millimeter wave channel is in the millimeter wave band.
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