CN103117839A - Pre-coding method under non-accurate channel information of multi-user multiple-input-multiple-output system - Google Patents

Pre-coding method under non-accurate channel information of multi-user multiple-input-multiple-output system Download PDF

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CN103117839A
CN103117839A CN2013100825762A CN201310082576A CN103117839A CN 103117839 A CN103117839 A CN 103117839A CN 2013100825762 A CN2013100825762 A CN 2013100825762A CN 201310082576 A CN201310082576 A CN 201310082576A CN 103117839 A CN103117839 A CN 103117839A
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李辉
戴旭初
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University of Science and Technology of China USTC
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Abstract

The invention discloses a pre-coding method under non-accurate channel information of a multi-user multiple-input-multiple-output system. The pre-coding method is characterized in that errors between non-accurate estimation channel state matrixes obtained by a practical channel state matrix and a base station is modeled to an additive complex gaussian random process, the structure of a pre-coded matrix is formed by pseudo-inverse multiplying regularization of a total estimation channel matrix from the base station to all users with a block diagonal matrix, the block diagonal matrix is calculated according to the minimum mean square error, the obtained pre-coded matrix pre-codes data flow and transmits the data flow out from an antenna of the base station, all the users multiplies self receiving signals with corresponding decoding matrixes of the users to convert a channel to a plurality of single-input single-output sub channels, and all the sub channels are subjected to data detection to estimate original data. The pre-coding method considers influence of channel estimation errors during calculation of the pre-coded matrix and the decoding matrix, solves the problem that the channel errors generate the plateau effect in a pre-coding system, and ensures transmission effectiveness of the pre-coding system.

Description

Method for precoding under the non-accurate channel information of multiuser mimo system
Technical field
The invention belongs to the multi-antenna technology field of radio communication, be specifically related in multi-user's multiple-input and multiple-output (multi-inputmulti-output, MIMO) system down link the pre-coding system method for designing based on least mean-square error.
Background technology
Multiuser MIMO is base station frequently same and a plurality of telex network simultaneously of many antennas of configuration, and each user can configure single antenna, also can configure many antennas.In down link, the base station sends data to a plurality of users, and each user can receive the interference from other users, when the user configures many antennas, multi-user interference (MUI) is arranged not only, also has the interference (ISI) between a plurality of data flow of user oneself.Due to not cooperation between the user, in order to reduce MUI and ISI, usually in the base station, transmitted signal is carried out precoding." international IEEE-radio communication transactions " (IEEETransactions on Wireless Communications, vol.7:953-961,2008) regular block diagonalization (RBD) method has been proposed, this is the MMSE method in one or more antenna situation of a kind of user of being applicable to, but the method just suppresses rather than eliminates multi-user interference fully, and it is to obtain optimum pre-coding matrix by minimizing total interference and noise power sum.
In real system, because the radio transmission medium is random, variation at any time, it is often very difficult and expensive that transmitting terminal wants to obtain accurate instantaneous channel condition information (CSI), so transmitting terminal can only obtain non-accurate instantaneous channel condition information usually.Have the two class basic modes of having obtained of transmitting terminal channel condition information now: based on reciprocity with based on feedback.Use the channel information of backward channel based on the method for reciprocity, need to pass the channel information of forward channel back transmitting terminal by special feedback channel based on the method for feedback.In the system based on channel reciprocity, channel estimation errors, antenna calibration error and revolution delay etc. have limited the accuracy of transmitting terminal CSI; In the system based on feedback, the factors such as the quantization error relevant to Limited Feedback, feedback transmission time delay, user and antenna scheduling delay have all reduced the accuracy of transmitting terminal CSI.Channel errors has platform effect to the impact of pre-coding system, namely as long as there is error in channel, even error is very little, system with speed just can not be along with signal to noise ratio increases without limitation under high s/n ratio, but be tending towards a constant, and channel errors is larger, and this constant is less.
Summary of the invention
The objective of the invention is to propose the method for precoding under the non-accurate channel information of a kind of multiuser mimo system, with the solution accurate platform effect of channel errors to pre-coding system performance during CSI by no means.
Method for precoding under the non-accurate channel information of multiuser mimo system of the present invention, base station for many antennas of a configuration, send data in same time slot, same frequency range to a plurality of users, the user configures one or more antenna, the base station is first done precoding to data flow and is then sent on antenna, the average transmitting power of base station is no more than Power Limitation, and each user receives signal to it independently respectively and decodes and detect; It is characterized in that: establishing the antenna for base station number is N T, number of users is K, j user's antenna number is N j, user's main aerial number is N R, sending the data flow vector is x, the base station to total channel matrix of all users is H = H 1 T H 2 T . . . H K T T , H wherein j, j=1 ..., K is the channel matrix of base station to j user, the transposition of subscript " T " representing matrix; Non-accurate estimation channel matrix with the base station acquisition
Figure BDA00002917159100021
And the error modeling that exists between actual channel H is the multiple Gaussian random process of an additivity, namely Wherein Δ represents the channel errors matrix, and its each element is independently obeyed zero-mean, variance is
Figure BDA00002917159100023
Multiple gaussian random distribute, and establish channel errors matrix Δ and estimate channel matrix
Figure BDA00002917159100024
Data flow vector x and noise vector w are separate; To be the base station be multiplied by a block diagonal matrix to the regularization pseudoinverse of all users' estimation channel matrix to the structure of pre-coding matrix P, and namely pre-coding matrix P can be expressed as
Figure BDA00002917159100025
The ratio of noise equivalent power and total transmitted power wherein
Figure BDA00002917159100026
Figure BDA00002917159100027
The total noise variances of all users, P TBe the highest average transmitting power of base station limits, β is the power control ratio in pre-coding matrix; Block diagonal matrix
Figure BDA00002917159100028
Submatrix B wherein jCorresponding to j user, j=1 ..., K, size be N j* N jSubmatrix; I is unit matrix, the conjugate transpose of subscript " H " representing matrix, subscript " 1 " representing matrix contrary;
Concrete operation step is as follows:
The first step is according to minimum mean square error criterion computing block diagonal matrix B: first calculate intermediary matrix
Figure BDA00002917159100029
Get successively the N on intermediary matrix A diagonal j* N jSubmatrix A j, j=1 ..., K makes Eigenvalues Decomposition to it
Figure BDA000029171591000210
J piece B with block diagonal matrix B jBe taken as
Figure BDA000029171591000211
U wherein jSubmatrix A jEigenvectors matrix, Λ jSubmatrix A jEigenvalue matrix;
Second step, calculate the power control ratio β in pre-coding matrix:
β = P T | | H ^ H ( H ^ H ^ H + γI ) - 1 B | | 2
Wherein || || 2The second order norm of representing matrix;
The 3rd goes on foot, and block diagonal matrix B is multiplied by the regularization pseudoinverse of overall estimate channel matrix
Figure BDA000029171591000213
With power control ratio β, obtain pre-coding matrix P, the base station utilizes this pre-coding matrix P carry out precoding and send from antenna sending data flow;
The 4th step, j, j=1 ..., K, individual user receives the decoding matrix G of this user on signal times with it j, j=1 ..., K, this decoding matrix G jComputational methods be: according to submatrix A j, j=1 ..., K, Eigenvalues Decomposition
Figure BDA00002917159100031
Decoding matrix G with j user jBe taken as
Figure BDA00002917159100032
And be respectively j user's antenna number N with the channel equivalence jThe parallel subchannel of individual single-input single-output;
In the 5th step, every sub-channels of each user is carried out respectively scalar data detect.
it is the multiple Gaussian random process of additivity that method for precoding under the non-accurate channel information of multiuser mimo system of the present invention has been taked the error modeling between the non-accurate estimation channel state matrix of actual channel state matrix and base station acquisition, to be the base station be multiplied by a block diagonal matrix to the regularization pseudoinverse of the total estimation channel matrix of all users to the structure of this pre-coding matrix, calculate block diagonal matrix according to minimum mean square error criterion, the pre-coding matrix that utilization obtains is done precoding to data flow and is sent from antenna for base station, each user is converted into a plurality of single-input single-output subchannels with the decoding matrix that self received signal vector is multiplied by this user with channel, every sub-channels is counted estimate initial data according to detection.Because the present invention is modeled as the multiple Gaussian random process of additivity with channel estimation errors, considered the impact of channel estimation errors when calculating pre-coding matrix and decoding matrix, improved channel errors and produced the problem of platform effect in pre-coding system, when not increasing the system algorithm expense, improve the robustness of the pre-coding system that has channel estimation errors, guaranteed the validity of this type systematic transmission.
Description of drawings
Fig. 1 is for adopting multiuser MIMO downlink pre-coding system schematic diagram of the present invention;
Fig. 2 is the flow chart of generation, transmission, reception and the processing of precoded signal in system;
Fig. 3 is the flow chart of computing block diagonal matrix method;
Fig. 4 is the emulation comparison diagram that " and speed " changes with signal to noise ratio;
Embodiment
Embodiment 1:
The present embodiment is the base station for many antennas of a configuration, send data in same time slot, same frequency range to a plurality of users, when the user configures one or more antennas, in multi-user's multiple-input and multiple-output (MIMO) system down link based on the concrete example explanation of the method for precoding of least mean-square error.
Fig. 1 has provided the present embodiment multiuser MIMO downlink pre-coding system schematic diagram.
As shown in fig. 1: the base station of many antennas of a configuration sends data in same time slot, same frequency range to a plurality of users, and the user configures one or more antennas.If the base station is furnished with N TThe root transmitting antenna is respectively the first transmitting antenna t 1..., N TTransmitting antenna
Figure BDA00002917159100033
Number of users is K, j(j=1 ..., K) individual user is furnished with N jThe root reception antenna is respectively j user the first reception antenna r j,1..., j user N jReception antenna All users have N RThe root reception antenna, the base station to total channel matrix of all users is H = H 1 T . . . H j T . . H K T T , H wherein j(j=1 ..., K) be the base station to j user's channel matrix, the transposition of subscript " T " representing matrix sends to j(j=1 ..., K) individual user's data flow vector x jBe N j* 1 column vector, total transmission data flow vector x = x 1 T . . . x j T . . x K T T For
Figure BDA00002917159100037
Column vector, base station can only obtain a non-accurate estimation channel matrix
Figure BDA00002917159100038
And having error between actual channel H, is the multiple Gaussian random process of an additivity with this error modeling, namely
Figure BDA00002917159100041
Wherein Δ represents the channel errors matrix, and its each element is independently obeyed a zero-mean, variance is
Figure BDA00002917159100042
Multiple gaussian random distribute.The base station is first done precoding to data flow and is then sent on antenna, and the average transmitting power of base station must not surpass the Power Limitation P of self T, i.e. E{||Px|| 2}≤P T, wherein E{} represents expectation.Each user independently processes it and receives signal, and j user's decoding matrix is G jFor the degree of freedom of utilizing each user that the reception combined signal on self all antenna is processed, the structure of pre-coding matrix is taken as the base station to the total estimation channel matrix of all users
Figure BDA00002917159100043
The regularization pseudoinverse be multiplied by a block diagonal matrix, can suppress the interference between the multi-user like this and not suppress interference between inner each antenna of each user, pre-coding matrix
Figure BDA00002917159100044
Block diagonal matrix wherein B jRespectively corresponding to j(j=1 ..., K) individual user's size is N j* N jMatrix,
Figure BDA00002917159100046
The total noise variances of all users, P TThe highest average transmitting power I that is base station limits is unit matrix, the conjugate transpose of subscript " H " representing matrix, and subscript " 1 " representing matrix contrary,
Figure BDA00002917159100048
The total noise variances of all users, P TBe the highest average transmitting power of base station limits, β is power control ratio.
Fig. 2 has provided the flow chart of generation, transmission, reception and the processing of precoded signal in the present embodiment system.Whole communication process based on the method for precoding of least mean-square error in multi-user's multiple-input and multiple-output of the present invention (MIMO) system down link comprises the steps:
Computing block diagonal matrix step 200 is according to minimum mean square error criterion computing block diagonal matrix B;
Rated output control coefrficient step 202, according to the restriction of the average transmitting power of base station, by following formula rated output control coefrficient β:
β = P T | | H ^ H ( H ^ H ^ H + γI ) - 1 B | | 2
Wherein || || 2The second order norm of representing matrix;
Precoding step 204 is multiplied by the overall estimate channel matrix with block diagonal matrix B The regularization pseudoinverse
Figure BDA000029171591000411
With power control ratio β, obtain pre-coding matrix P, utilize pre-coding matrix P to do precoding to total transmission data flow vector x, with the N that obtains T* 1 data vector Px is from the N of base station TSend on the root antenna;
Parallelization channel step 206, j(j=1 ..., K) individual user receives signal y with it j=H jPx+n jBe multiplied by a decoding matrix G j, n wherein jIt is j user's noise vector.And be N with the channel equivalence jThe parallel subchannel of individual single-input single-output;
Data Detection step 208 is carried out scalar data to every sub-channels of each user and is detected, thereby obtains sending to each user's data flow vector x j(j=1 ..., estimation K).
Fig. 3 has provided the method flow diagram according to minimum mean square error criterion computing block diagonal matrix B, and the detailed process of the method is:
Calculate intermediary matrix step 300, calculate intermediary matrix A=(HH H+ γ I) -1
Obtain submatrix step 302, get successively the N on intermediary matrix A diagonal j* N jSubmatrix A j(j=1 ..., K), submatrix A 1Comprise in intermediary matrix A the 1st to N 1Row and the 1st to N 1Element in row, submatrix A 2Comprise N in intermediary matrix A 1+ 1 to N 1+ N 2Row and N 1+ 1 to N 1+ N 2Element in row, etc.;
Eigenvalues Decomposition step 304, antithetical phrase matrix A respectively j(j=1 ..., K) make Eigenvalues Decomposition
Figure BDA00002917159100051
Unitary matrix U wherein jBe submatrix A jEigenvectors matrix, diagonal matrix Λ jBe submatrix A jEigenvalue matrix;
Computing block diagonal matrix step 306 is with the j piece B of block diagonal matrix B jBe taken as
Figure BDA00002917159100052
Obtain block diagonal matrix B.
According to the pre-coding matrix form that obtains, for j(j=1 ..., K) equivalence of individual user's channel is N jIndividual single-input single-output subchannel is with its decoding matrix G jBe taken as U wherein jBe submatrix A jEigenvectors matrix, the signal z on j user's reception signal times after decoding matrix jFor
z j = G j y j = ( Λ j - 1 - rI ) x j + U j H w j
W wherein jBe interference and the noise signal of equivalence, Λ jBe diagonal matrix.Like this, to the vectorial x of j user's data flow jDetection just become j user's data flow vector x jIndependently single symbol detection of each element, adopt scalar data to detect and can estimate original transmission data flow.
Fig. 4 has provided the emulation comparison diagram that " and speed " changes with signal to noise ratio.System parameter setting in the present embodiment is as follows: the antenna for base station number is 4,2 users, each user antenna number is 2, channel coefficients between the every pair of antenna is that variance is 1 multiple Gaussian random variable, the thermal noise at every antenna place is that variance is 1 additivity white complex gaussian noise, signal to noise ratio is defined as the ratio that sends gross power and the noise power at every antenna place, and channel estimation errors is variance
Figure BDA00002917159100055
Multiple gaussian random distribute, adopt the Monte-Carlo Simulation experiment.Provide performance curve L1 that the present embodiment by asterisk sign adopts the pre-coding matrix that the non-accurate channel condition information method for precoding of considering channel estimation errors obtains in Fig. 4, be the performance curve L2 that does not consider to estimate the pre-coding matrix that regular block diagonalization (RBD) method of channel errors obtains by upper triangle sign." and speed " increases along with the increase of signal to noise ratio; Under identical signal to noise ratio, the method for precoding of the non-accurate channel condition information that the present invention provides has larger " and speed " than RBD method, this is because non-accurate channel condition information method for precoding will estimate that the error modeling that channel matrix exists is the multiple Gaussian Profile of additivity, consider the impact that channel estimation errors brings when calculating method for precoding, therefore obtained higher " and speed " performance.
In general, the precoding algorithm in the present invention is in the situation that exist channel estimation errors, reached than better " and speed " performance of regular block diagonalization method.

Claims (1)

1. the method for precoding under the non-accurate channel information of multiuser mimo system, base station for many antennas of a configuration, send data in same time slot, same frequency range to a plurality of users, the user configures one or more antenna, the base station is first done precoding to data flow and is then sent on antenna, the average transmitting power of base station is no more than Power Limitation, and each user receives signal to it independently respectively and decodes and detect; It is characterized in that: establishing the antenna for base station number is N T, number of users is K, j user's antenna number is N j, user's main aerial number is N R, sending the data flow vector is x, the base station to total channel matrix of all users is H = H 1 T H 2 T . . . H K T T , H wherein j, j=1 ..., K is the channel matrix of base station to j user, the transposition of subscript " T " representing matrix; Non-accurate estimation channel matrix with the base station acquisition And the error modeling that exists between actual channel H is the multiple Gaussian random process of an additivity, namely
Figure FDA00002917159000013
Wherein Δ represents the channel errors matrix, and its each element is independently obeyed zero-mean, variance is Multiple gaussian random distribute, and establish channel errors matrix Δ and estimate channel matrix
Figure FDA00002917159000015
Data flow vector x and noise vector w are separate; To be the base station be multiplied by a block diagonal matrix to the regularization pseudoinverse of all users' estimation channel matrix to the structure of pre-coding matrix P, and namely pre-coding matrix P can be expressed as
Figure FDA00002917159000016
The ratio of noise equivalent power and total transmitted power wherein
Figure FDA00002917159000017
The total noise variances of all users, P TBe the highest average transmitting power of base station limits, β is the power control ratio in pre-coding matrix; Block diagonal matrix
Figure FDA00002917159000019
Submatrix B wherein jCorresponding to j user, j=1 ..., K, size be N j* N jSubmatrix; I is unit matrix, the conjugate transpose of subscript " H " representing matrix, subscript " 1 " representing matrix contrary;
Concrete operation step is as follows:
The first step is according to minimum mean square error criterion computing block diagonal matrix B: first calculate intermediary matrix
Figure FDA000029171590000110
Get successively the N on intermediary matrix A diagonal j* N jSubmatrix A j, j=1 ..., K makes Eigenvalues Decomposition to it
Figure FDA000029171590000111
J piece B with block diagonal matrix B jBe taken as
Figure FDA000029171590000112
U wherein jSubmatrix A jEigenvectors matrix, Λ jSubmatrix A jEigenvalue matrix;
Second step, calculate the power control ratio β in pre-coding matrix:
β = P T | | H ^ H ( H ^ H ^ H + γI ) - 1 B | | 2
Wherein || || 2The second order norm of representing matrix;
The 3rd goes on foot, and block diagonal matrix B is multiplied by the regularization pseudoinverse of overall estimate channel matrix
Figure FDA00002917159000022
With power control ratio β, obtain pre-coding matrix P, the base station utilizes this pre-coding matrix P carry out precoding and send from antenna sending data flow;
The 4th step, j, j=1 ..., K, individual user receives the decoding matrix G of this user on signal times with it j, j=1 ..., K, this decoding matrix G jComputational methods be: according to submatrix A j, j=1 ..., K, Eigenvalues Decomposition
Figure FDA00002917159000023
Decoding matrix G with j user jBe taken as
Figure FDA00002917159000024
And be respectively j user's antenna number N with the channel equivalence jThe parallel subchannel of individual single-input single-output;
In the 5th step, every sub-channels of each user is carried out respectively scalar data detect.
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CN104717003A (en) * 2015-03-03 2015-06-17 重庆大学 Mobile communication channel error modeling method
CN105391481A (en) * 2015-10-27 2016-03-09 杭州电子科技大学 Low complexity decoding method for large-scale antenna system
CN105656532A (en) * 2016-01-27 2016-06-08 北京邮电大学 Information transmitting and receiving method of non-ideal state channel

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104717003A (en) * 2015-03-03 2015-06-17 重庆大学 Mobile communication channel error modeling method
CN104717003B (en) * 2015-03-03 2018-02-09 湖南省通信建设有限公司 A kind of mobile telecommunication channel error modeling method
CN105391481A (en) * 2015-10-27 2016-03-09 杭州电子科技大学 Low complexity decoding method for large-scale antenna system
CN105391481B (en) * 2015-10-27 2018-07-27 杭州电子科技大学 A kind of coding/decoding method for low complex degree in extensive antenna system
CN105656532A (en) * 2016-01-27 2016-06-08 北京邮电大学 Information transmitting and receiving method of non-ideal state channel
CN105656532B (en) * 2016-01-27 2019-06-07 北京邮电大学 A kind of information method of sending and receiving of nonideality channel

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