CN105978674B - The pilot frequency optimization method of extensive mimo channel estimation under compressed sensing based FDD - Google Patents
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Abstract
The invention discloses a kind of pilot frequency optimization methods of mimo channel extensive under compressed sensing based FDD estimation, and channel can be modeled as Y=HX+N in extensive mimo system first, whereinFor channel matrix,For pilot matrix,For receipt signal matrix,For interchannel noise, M is transmitting antenna number, and T is number of pilots；Then it converts channel matrix toWhereinThe variation of channel matrix is represented,The variation of pilot matrix is represented,Represent the variation that receiving end receives signal；Finally acquire optimal pilot matrix.Due toIt is a sparse vector, channel estimation problems can be modeled as compressed sensing Problems of Reconstruction: ||*||11- norm is represented, | | * | |2Represent 2- norm, 0 < ε < 1.It can ensure that the MIMO Downlink channel estimation of compressed sensing based FDD can reduce the mean square error of channel estimation significantly, improve the performance of channel estimation.
Description
Technical Field
The invention relates to the technical field of communication system pilot frequency assisted channel estimation and pilot frequency design, in particular to a pilot frequency optimization method for large-scale MIMO channel estimation under FDD based on compressed sensing.
Background
In modern wireless communication, the degree of freedom of a large-scale MIMO system under FDD (Frequency Division duplex) is increased, and diversity and multiplexing gains brought by multiple antennas can obviously improve the Frequency spectrum efficiency and the energy efficiency. In order to obtain the spatial multiplexing gain and the array gain, a sending end of the base station or a receiving end of a user needs to know Channel State Information (CSI), which needs to be obtained through channel estimation. In a massive MIMO system in TDD mode, a base station can acquire channel uplink CSI, and channel reciprocity makes channel estimation of downlink relatively easy. One challenge of FDD mode massive MIMO systems is that the number of pilots increases linearly with the number of transmit antennas, resulting in large pilot overhead, reduced communication system efficiency, and it is not easy to accurately estimate the CSI of the downlink. Since FDD is more efficient for delay sensitive systems and most cellular networks currently employ FDD, it is necessary to study channel estimation more efficiently in FDD.
The large number of transmit antennas at the base station causes limited local scattering. As the number of transmit antennas increases, the channel exhibits sparse properties. The channel estimation is carried out by using the implicit sparse property of the channel, so that the number of pilot frequencies can be reduced, and the effectiveness of the system is improved.
Compressed sensing has wide application in the signal and image processing fields. The compressed sensing is based on the fact that target signals are thinned, a proper measurement matrix is selected, sparse signal sampling and compression are conducted simultaneously, only a small amount of data need to be transmitted, and a receiving end recovers the signals according to a corresponding recovery matrix. Compressed sensing theory has demonstrated superior performance in channel estimation. In a large-scale MIMO system, a compressed sensing reconstruction algorithm can be utilized for channel estimation, so that the number of pilot frequencies is reduced.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a pilot optimization method for large-scale MIMO channel estimation under FDD based on compressed sensing aiming at the defects involved in the background technology, so that the MSE of the channel estimation is obviously reduced by the obtained optimal pilot matrix, and the channel estimation performance is improved.
The invention adopts the following technical scheme for solving the technical problems:
a pilot frequency optimization method for FDD large-scale MIMO channel estimation based on compressed sensing is provided, wherein the FDD large-scale MIMO channel is a flat fading channel, a base station is provided with M transmitting antennas, the number of the antennas of each user in a cell is 1, and the base station transmits a pilot frequency training sequence with the length of T, and the pilot frequency optimization method comprises the following steps:
step 1), establishing a channel model Y ═ HX + N;
whereinIn order to be a matrix of channels,in order to be a pilot matrix, the pilot matrix,in order to receive the matrix of signals,is the additive gaussian noise of the channel and,representing a complex vector space;
step 2), let Making the channel model correspond to the compressed sensing model to obtain the channel model corresponding to the compressed sensing model
Wherein,is a unitary matrix of the matrix,is an angle domain channel matrix, P is the signal-to-noise ratio of the pilot symbol, (+)HRepresenting the conjugate transpose of a matrix or vector; PT is the signal-to-noise ratio of transmitting T pilot symbols; the symbol of angular frequency is denoted by the superscript omega, which is used to illustrate HωIs the representation of the channel matrix H in the angle domain;representing a transformed form of the channel matrix,represents a transformed form of the pilot matrix,representing a transformed version of the received signal at the receiving end;
step 3), initializing the total iteration times IteroptThe current iteration number q is 1, and a pilot matrix in the first iteration The elements of the matrix satisfying a normal distribution, i.e.xi，jIs a pilot matrixAn element of (1);
step 4), solving the current gram matrix Is a current pilot matrix;
step 5), calculating a reduced gram matrix according to a preset reduction coefficient gamma
Wherein i, j ═ 1, 2.. M, gijIs a matrix GqThe elements are selected from the group consisting of,is a matrixAn element of (1);
step 6), using singular value decompositionReducing rank, reserving the first T maximum singular values, and obtaining matrix according to the first T singular values
Step 7) ofDecomposition of square root to obtain ZqLet us orderObtaining a pilot matrix in the next iteration;
step 8), adding 1 to the current iteration times q;
step 9), repeatedly executing the steps 4) to 8) until the current iteration number q is equal to the total iteration number Iteropt；
Step 10), inputting an optimized matrix
As a further optimization scheme of the pilot frequency optimization method for the large-scale MIMO channel estimation under FDD based on compressed sensing, the preset reduction coefficient γ is 0.95.
As a further optimization scheme of the pilot frequency optimization method of the FDD large-scale MIMO channel estimation based on the compressed sensing, the total iteration number IteroptThe number of the treatment was 800 times.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
in the compressed sensing-based channel estimation of the FDD massive MIMO system, compared with the use of a randomly generated non-optimized pilot matrix, the optimal pilot matrix obtained by the invention can obviously reduce the Mean Square Error (MSE) of the channel estimation. The performance of channel estimation is improved.
Drawings
FIG. 1 is a graph of the impact of pilot matrix optimization and non-optimization on reconstruction performance for different pilot numbers;
fig. 2 is a graph of the impact of pilot matrix optimization versus non-optimization on reconstruction performance for different numbers of transmit antennas.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the method comprises two main technical problems, one is to convert a channel estimation problem into a compressed sensing problem so as to model a pilot frequency sequence optimization problem into a measurement matrix optimization problem in compressed sensing; and the other is to provide a pilot optimization algorithm to solve the measurement matrix optimization problem so as to obtain an optimal pilot matrix. The following respectively describes the embodiments of the two parts, and explains the beneficial effect of the pilot frequency allocation method on improving the performance of channel estimation based on compressed sensing through simulation.
Acquisition of pilot optimization criterion
Considering a massive MIMO system in FDD mode, the channel is a flat fading channel, the base station has M uniform transmitting antennas spaced by half a wavelength, and each user in the cell has only one antenna. The base station transmits a pilot training sequence of length T. The pilot signal of the ith time slot is recorded asThe signal y received on the receiving antennaiComprises the following steps:
yi＝Hxi+ni，i＝1，2，...，T (1)
channel matrixIn order to be a quasi-static channel,additive gaussian noise. Note the bookComprises the following steps:
Y＝HX+N (2)
the signal-to-noise ratio of each pilot frequency time slot is recorded as P, and the total signal-to-noise ratio of the T pilot frequency time slots is tr (X)HX)＝PT。
In practical use, the virtual angular domain representation can enable nonlinear channel model parameters to be approximately linear, and the channel is subjected to virtual representation processing for analysis and estimation. The virtual representation of the non-selective MIMO channel is:
is a unitary matrix and M is the number of transmit antennas.Is the angle domain channel matrix. Due to local scattering effects at the base station, HωAre sparse.
Equation (2) is converted into a compressed sensing model. Will be provided with
Andsubstituting formula (2) to obtain:
equation (6) corresponds to the noisy compressed sensing model (7):
y＝Ds+N (7)
where s of tx 1 is a sparse signal,in order to recover the matrix, the matrix is restored,additive gaussian noise. The sparse signal s solving problem can be converted into:
ε is a positive constant near zero.
It is possible to obtain: pilot matrixIn correspondence with the measurement matrix D,is a sparse signal, corresponding to s. Thereby, estimating channel parametersCan be converted into a sparse signal reconstruction problem in the compressed sensing theory. Meanwhile, the optimization problem of the pilot frequency sequence can also be converted into the optimization problem of the compressed sensing measurement matrix.
In order to solve the compressed sensing problem, if the recovery matrix D satisfies Mutual Irrelevance (MIP), the sparse signal can be accurately reconstructed with a high probability. Therefore, the compressive sensing problem solving and measurement matrix optimization problem can be based on MIP.
For a recovery matrixIts cross-correlation value is defined as the absolute value of the normalized inner product between the largest two different columns, i.e.
The cross-correlation value μ { D } reflects the maximum similarity between the two columns of the measurement matrix. Another manifestation of the cross-correlation values is as follows: gram matrix G ═ DHD, D is in a form after column normalization. Off diagonal element G of Gi，jThe number of cross-correlations is the maximum of the off-diagonal elements for the inner product appearing in equation (9). The cross-correlation value represents the maximum correlation between matrix elements, and the smaller the cross-correlation value, the smaller the reconstruction error. Therefore, the reduction of the cross-correlation value directly affects the reconstruction performance of the compressed sensing recovery algorithm.
Pilot matrix optimization algorithm
By reducing μ of the gram matrix GtPerforming pilot matrixAnd (4) optimizing. The core idea of the optimization is to select proper optimizationThreshold t vs element | G of GijThe reduction is made for | g greater than the value of tijI is optimized, t belongs to [0, 1 ]]. The reduction equation becomes:
gamma is attenuation factor, 0.95 is taken. Obtaining a reduced matrix Is a matrixOf (2) is used. Due to the fact thatMay be greater than the number of rows of the pilot matrix, and in order to perform square root Decomposition on the pilot matrix to obtain an optimized pilot matrix, Singular Value Decomposition (SVD) is required to reduce the rank of G to T. The specific process of reducing the rank is as follows: in the q-th iteration, using SVD willReducing the rank and reserving the first T singular values to obtain
UTAnd VMUnitary matrices of TxT and MxM, respectively, the diagonals of sigma being singular values and consisting ofDecomposition of square root to obtain ZqLet us orderThe above reduction and rank reduction processes need to be iterated many times to reduce the number of cross-correlations to a relatively stable value. At the end of the iteration we get an optimized matrixFinally, obtaining the final optimized pilot matrix X according to the formula (5)opt. The specific process of pilot optimization is shown in algorithm 1:
algorithm 1: optimization algorithm of pilot matrix
Inputting: pilot matrixIt is a randomly generated Gaussian matrix, the elements of which And independent equal distribution is satisfied. Each row of which represents a base station transmitting a number T of pilot sequences per antenna.
Step 1), obtainingFrom X according to formula (5)
Step 2), optimizingTo obtainThe number of initialization iterations q is 1, setCo-iteration Iteropt800 times.
Step 2.1), solving a gram matrix Gq: byObtaining;
step 2.2) to obtainObtaining a reduced gram matrix according to equation (10)
Step 2.3), Z is solvedq: using SVD willReducing rank, and obtaining matrix by reserving maximum first T singular valuesAnd is composed ofDecomposition of square root to obtain Zq，
Step 2.4), judging: if q reaches IteroptThen, jumping out of the loop to obtain an optimized matrixOtherwise, put q ═ q +1, jump to step 2.1).
Step 3), optimizing the pilot frequency matrixAnd (6) outputting.
In the next section, we will show through simulation that the pilot matrix obtained by algorithm 1 will enable FDD massive MIMO downlink channel estimation based on Matching Orthogonal tracking (OMP) to obtain smaller mean square error compared to the non-optimized pilot matrix, thereby enabling the system to obtain higher channel estimation performance.
(III) simulation results
Virtual angular domain channel matrix H used in simulation based on simulation needsωA Spatial Channel Model SCM (Spatial Channel Model) of 3GPP is adopted and generated based on urban microcell scenarios. The pilot frequency length is T, the number of the transmitting antennas of the base station is M, and the signal sparsity degree is K. In the simulation, the pilot sequence is sent in a time division manner. Channel matrix HωIs sparse, let sparsity K be 6. As shown in equation (2), the base station transmits a signal Y to the user, and converts the signal into a form conforming to the compressed sensing model according to equation (4)Since the receiving end knows pilot X, the conversion is made according to equation (5)For equation (6) we recover the channel matrix according to the compressive perceptual reconstruction algorithm OMPAnd obtaining a channel matrix H through conversion, and finishing channel estimation. Normalized MSE is adopted to measure the quality of the recovery performance of the sparse signal. MSE is defined as:
whereinRepresenting sparse signalsThe k-th element of (a) the first,representing a reconstructionThe kth element. We perform simulation analysis in terms of both different numbers of pilots and different numbers of transmit antennas.
1) We first study the impact of the optimized pilot matrix for different pilot numbers on the channel estimation performance. The number of transmitting antennas M of the base station is selected to be 200, and the number of pilot frequencies T is selected to be 20, 30 and 40 respectively. The simulation results are shown in fig. 1. The result shows that the reconstruction performance between the optimization and the non-optimization of the pilot frequency has obvious difference under different pilot frequency numbers. When the SNR is 25dB and 20 pilots and 30 pilots are used, the MSE can be reduced by 1-2 dB by using the optimized pilots; when the SNR is larger, the MSE can drop further. It is noted that the MSE for channel estimation using the optimized pilot matrix for pilot number 30 is very close to the MSE for channel estimation using the un-optimized pilot matrix for pilot number 40. That is, on the premise of the same reconstruction performance, the number of the pilot frequencies can be reduced by using the optimized pilot frequency, so that the effectiveness of the system is improved. In addition, as can be seen from the simulation curve, it is obvious that the optimization effect gradually weakens as the number of pilots increases.
2) The improvement situation of channel estimation MSE by pilot matrix optimization when the number of transmitting antennas of a base station is different is researched. Considering the number of pilots as 30, the number of base station transmit antennas is 100, 200 and 300, respectively. As can be seen from fig. 2, as the number of transmit antennas of the base station increases, the recovery performance of the channel matrix decreases. However, under the condition of three different numbers of transmitting antennas, the optimization of the pilot matrix can obtain good reconstruction effect under the normal signal propagation environment with the SNR larger than 15 dB. Using the optimized pilot matrix can reduce the MSE of the channel estimate by about 1.5dB over the non-optimized pilot matrix for three transmit antenna numbers at an SNR of 25 dB. This value will be up to 4dB when the SNR is larger. It can also be seen from the simulation curve in fig. 2 that when the number of transmit antennas of the base station is greater, the MSE performance improvement brought by the optimized pilots is greater.
From the simulation, it can be seen that at high SNR, i.e. SNR greater than 15dB, the MSE of the channel estimation can be effectively reduced by using the optimized pilot sequence regardless of the change of the number of pilots or the change of the transmitting antennas of the base station.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A pilot frequency optimization method for FDD large-scale MIMO channel estimation based on compressed sensing is characterized in that the pilot frequency optimization method comprises the following steps:
step 1), establishing a channel model Y ═ HX + N;
wherein,in order to be a matrix of channels,in order to be a pilot matrix, the pilot matrix,in order to receive the matrix of signals,is the additive gaussian noise of the channel and,representing a complex vector space;
step 2), let Making the channel model correspond to the compressed sensing model to obtain the channel model corresponding to the compressed sensing model
Wherein,is a unitary matrix of the matrix,is an angle domain channel matrix, P is the signal-to-noise ratio of the pilot symbol, (+)HRepresenting the conjugate transpose of a matrix or vector; PT is the signal-to-noise ratio of transmitting T pilot symbols; the symbol of angular frequency is denoted by the superscript omega, which is used to illustrate HωIs the representation of the channel matrix H in the angle domain;representing a transformed form of the channel matrix,represents a transformed form of the pilot matrix,representing a transformed version of the received signal at the receiving end;
step 3), initializing the total iteration times IteroptThe current iteration number q is 1, and a pilot matrix in the first iteration The elements of the matrix satisfying a normal distribution, i.e.i＝1，2…T，j＝1，2…M，xi，jIs a pilot matrixAn element of (1);
step 4), solving the current gram matrix Is a current pilot matrix;
step 5), calculating a reduced gram matrix according to a preset reduction coefficient gamma
Wherein i, j is 1, 2 … M, gijIs a matrix GqThe elements are selected from the group consisting of,is a matrixAn element of (1);
step 6), using singular value decompositionReducing rank, reserving the first T maximum singular values, and obtaining matrix according to the first T singular values
Step 7) ofDecomposition of square root to obtain ZqLet us orderObtaining a pilot matrix in the next iteration;
step 8), adding 1 to the current iteration times q;
step 9), repeatedly executing the steps 4) to 8) until the current iteration number q is equal to the total iteration number Iteropt；
Step 10), inputting an optimized matrix
2. The pilot optimization method for massive MIMO channel estimation under FDD according to claim 1, wherein the predetermined reduction coefficient γ is 0.95.
3. The pilot optimization method for massive MIMO channel estimation under FDD based on compressed sensing of claim 1, wherein the total number of iterations IteroptThe number of the treatment was 800 times.
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| CN106453163A (en) * | 2016-10-11 | 2017-02-22 | 电子科技大学 | Massive MIMO (Multiple Input Multiple Output) channel estimation method |
| CN106452534A (en) * | 2016-11-23 | 2017-02-22 | 南京邮电大学 | Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing |
| CN106911443B (en) * | 2017-02-23 | 2019-11-26 | 北京交通大学 | Pilot tone optimum design method in compressed sensing based M2M communication system |
| CN107171774A (en) * | 2017-04-14 | 2017-09-15 | 南京邮电大学 | The pilot frequency optimization method of extensive mimo system compressed sensing channel estimation |
| CN107483089B (en) * | 2017-08-15 | 2020-09-01 | 南京林业大学 | Pilot system of multi-antenna broadcast system and design method |
| CN109842580B (en) * | 2017-11-28 | 2021-02-12 | 华为技术有限公司 | Channel estimation method and related equipment |
| CN108400948B (en) * | 2018-01-25 | 2020-01-14 | 西安交通大学 | Environment self-adaptive perception wireless communication channel estimation and signal reconstruction method |
| CN108880774B (en) * | 2018-07-11 | 2021-01-01 | 郑州航空工业管理学院 | Frequency division duplex multi-user large-scale multi-antenna system and downlink pilot signal length design method thereof |
| CN113676226B (en) * | 2020-05-15 | 2023-03-14 | 维沃移动通信有限公司 | Pilot information symbol transmission method, channel estimation method, communication device, and medium |
| CN112953603B (en) * | 2020-12-31 | 2022-06-10 | 杭州电子科技大学 | Pilot frequency optimization design method based on equiangular compact frame theory |
| CN112953865A (en) * | 2021-04-15 | 2021-06-11 | 中国科学院微小卫星创新研究院 | Channel estimation method for large-scale multi-input multi-output system |
| CN113890797B (en) * | 2021-09-30 | 2024-04-19 | 哈尔滨工业大学 | Channel estimation method based on short packet communication transmission process |
| CN114338300B (en) * | 2021-12-02 | 2024-03-12 | 重庆两江卫星移动通信有限公司 | Pilot frequency optimization method and system based on compressed sensing |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102834731A (en) * | 2010-02-08 | 2012-12-19 | 美国博通公司 | Method and system of beamforming a broadband signal through a multiport network |
| CN104040922A (en) * | 2012-01-27 | 2014-09-10 | 日本电信电话株式会社 | Wireless device and training signal transmission method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160295596A1 (en) * | 2015-03-31 | 2016-10-06 | Huawei Technologies Canada Co., Ltd. | Joint Radio-Frequency/Baseband Self-Interference Cancellation Methods and Systems |
-
2016
- 2016-05-12 CN CN201610312810.XA patent/CN105978674B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102834731A (en) * | 2010-02-08 | 2012-12-19 | 美国博通公司 | Method and system of beamforming a broadband signal through a multiport network |
| CN104040922A (en) * | 2012-01-27 | 2014-09-10 | 日本电信电话株式会社 | Wireless device and training signal transmission method |
Non-Patent Citations (2)
| Title |
|---|
| 《OFDM系统中信道压缩估计技术研究》;康鑫;《中国优秀硕士学位论文》;20150831;全文 |
| Closed-Loop Compressive CSIT Estimation in FDD Massive MIMO Systems With 1 Bit Feedback;Vinent K.N.Lau等;《IEEE TRANSACTIONS ON SIGNAL PROCESSING 》;20160415;第64卷(第8期);全文 |
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