CN110212960B - Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method - Google Patents

Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method Download PDF

Info

Publication number
CN110212960B
CN110212960B CN201910445642.5A CN201910445642A CN110212960B CN 110212960 B CN110212960 B CN 110212960B CN 201910445642 A CN201910445642 A CN 201910445642A CN 110212960 B CN110212960 B CN 110212960B
Authority
CN
China
Prior art keywords
matrix
slnr
user
channel
improved
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910445642.5A
Other languages
Chinese (zh)
Other versions
CN110212960A (en
Inventor
万海斌
赵金鑫
覃团发
陈海强
黎相成
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangxi University
Original Assignee
Guangxi University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangxi University filed Critical Guangxi University
Priority to CN201910445642.5A priority Critical patent/CN110212960B/en
Publication of CN110212960A publication Critical patent/CN110212960A/en
Application granted granted Critical
Publication of CN110212960B publication Critical patent/CN110212960B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Radio Transmission System (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a precoding method of an MU-MIMO system based on improved SLNR, which is characterized in that the method obtains a channel modification matrix for correcting and estimating the co-channel interference of other users in a channel matrix in an SLNR method based on the minimum mean square error of the channel modification matrix; obtaining an optimal precoding matrix for improving the SLNR index based on the channel modification matrix; the estimated channel matrix is configured to estimate incomplete channel state information. In addition, the invention also discloses a power distribution method based on the improved SLNR MU-MIMO system precoding method, wherein, the base station distributes based on the improved SLNR value of each user; the power allocated to each user k is inversely proportional to the improved SLNR value for that user k. The invention can effectively improve the defect of higher error rate of the traditional SLNR method for signal transmission under partial channel state information, and can improve the defect of higher error rate of signals received by users with poor signal-to-noise ratio in the equal power distribution technical scheme with lower cost.

Description

Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method
Technical Field
The invention relates to the technical field of wireless communication, in particular to a precoding method and a power distribution method of an MU-MIMO system based on improved SLNR.
Background
The popularity of multimedia applications such as intelligent terminals and social networks has led to a significant increase in the demands of mobile cellular networks for system capacity and reliability. A multi-User Multiple Input Multiple Output (MU-MIMO) system is widely used because it can effectively increase channel capacity. MU-MIMO systems provide significant improvements in system performance by increasing multiplexing and diversity gains without increasing bandwidth and transmission power.
In the MU-MIMO downlink model, one of the main objectives in designing an optimal linear MU-MIMO precoding scheme is to optimize the signal-to-noise ratio, however this problem is challenging due to its coupling. To avoid the coupling problem, the prior art generally uses a Zero Forcing (ZF) precoding scheme, which can eliminate the co-channel interference of each user. In addition, based on the concept of Signal to Leakage and noise Ratio (SLNR), the SLNR method is introduced as an optimization index of linear precoding design. The SLNR index can also convert a coupling optimization problem into a complete decoupling optimization problem, and is easy to solve. Unlike the ZF scheme, the SLNR precoding scheme does not require any limitation on the number of base station antennas, and also takes the influence of noise into consideration when designing beamforming vectors for all users. However, both the conventional ZF scheme and the SLNR scheme face a problem of high error rate. In addition, the current technical scheme of equal power distribution is adopted to transmit data for users covered by the base station, so that the error rate of signals received by users with poor signal-to-noise ratio is higher.
Therefore, those skilled in the art are dedicated to develop an improved SLNR-based MU-MIMO system precoding method and a power allocation method, which can effectively improve the defect of high error rate of the conventional SLNR method for signal transmission under incomplete channel state information, and improve the defect of high error rate of signals received by users with poor signal-to-noise ratio by using a medium power allocation technical scheme in the above scenario at a low cost.
Disclosure of Invention
In view of the above drawbacks of the prior art, the technical problem to be solved by the present invention is to improve the conventional SLNR method, which has a high signal transmission error rate under partial channel state information, and to improve the medium power allocation technical scheme in the above scenario at a low cost, so that the error rate of the signal received by the user with poor signal-to-noise ratio is high.
In order to achieve the above object, the present invention provides an improved SLNR-based MU-MIMO system precoding method, which obtains a channel modification matrix for correcting and estimating co-channel interference of other users in a channel matrix in an SLNR method based on a minimum mean square error of the channel modification matrix; obtaining an optimal precoding matrix for improving the SLNR index based on the channel modification matrix; the estimated channel matrix is configured to estimate incomplete channel state information.
Further, the estimated channel matrix for user k in the SLNR method satisfies:
Figure GDA0003151845610000021
wherein,
Figure GDA0003151845610000022
the estimated channel matrix for the user k, E is an estimated error matrix; hkA channel matrix containing complete channel state information for the user k; SLNRkThe signal-to-leakage-and-noise ratio of the user k is obtained; w is akA precoding matrix for the user k; k is the total number of users under the same base station with the user K; mkReceiving the number of antennas for the user k; sigmakFor the channel matrix HkMean square error of medium gaussian noise.
Further, the estimation error matrix E is a zero-mean circularly symmetric complex gaussian variable.
Further, based on the channel modification matrix and the estimated channel matrix
Figure GDA0003151845610000023
Obtaining an equivalent channel matrix after the error of the estimated channel matrix correction; the equivalent channel matrix satisfies:
Figure GDA0003151845610000024
wherein F is the channel modification matrix; h'kThe equivalent channel matrix for user k; and | | F | | non-conducting phosphor21. Further, the channel modification matrix F is:
F＝argmin E[||Hk-H'k||2]
wherein E [ ] is the expected calculation; and | | is the norm calculation of the matrix.
Further, the F is solved by making the gradient of the mean square error of the F equal to zero.
Further, the F of the user k requires a homogenization process, and a mean square variance σ' based on the F after the homogenization process satisfies:
Figure GDA0003151845610000025
wherein λ isiIs a non-zero eigenvalue of the full channel covariance matrix; sigmaEIs the mean square error of the estimation error matrix E;
the full channel covariance matrix HcovSatisfies the following conditions:
Figure GDA0003151845610000026
further, based on the equivalent channel matrix, solving an improved SLNR; the improved SLNR satisfies:
Figure GDA0003151845610000027
wherein, SLNR'kIs the improved SLNR; i isNAn identity matrix of NxN; σ' is the normalized minimum mean square error of the channel modification matrix;
Figure GDA0003151845610000028
to expand the channel matrix, an
Figure GDA0003151845610000029
Further, the optimal precoding matrix
Figure GDA00031518456100000210
Comprises the following steps:
Figure GDA00031518456100000211
where max EV is the maximum eigenvector calculation.
In addition, the invention also discloses a power distribution method of MU-MIMO based on improved SLNR, wherein a base station distributes based on the improved SLNR value of each user; the power allocated to each user k is inversely proportional to the improved SLNR value for that user k, as follows:
Figure GDA0003151845610000031
wherein, SLNR'kAn improved SLNR value or said optimal precoding matrix calculated for a method based on any of claims 1-9; p is a radical oftThe total transmission power of the base station where the user k is located; p is a radical ofkThe power allocated for the user k.
Compared with the prior art, the invention has the beneficial technical effects that:
1) under the condition of incomplete channel state information, the influence of an estimation error matrix is effectively reduced through a channel modification matrix obtained based on the minimum mean square error, and the error rate of a system can be effectively reduced;
2) the channel self-adaptive power distribution method provided by the invention effectively improves the information receiving quality of the user with poor signal-to-noise ratio in the scene, and reduces the information receiving error rate of the user.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
Fig. 1 is a block diagram of a MU-MIMO system in accordance with a preferred embodiment of the present invention.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
In the present invention, for the matrices A, AHRepresents the conjugate transpose thereof; tr (A) represents the trace of the matrix, Re { A } represents the index portion of tr (A), | A | represents the complex modulus of its matrix, | A | | | takes the F-norm of the matrix, ILDenotes an identity matrix of L.times.L, E [, ]]The expected value.
The invention considers the downlink MU-MIMO environment of a base station, the base station adopts N transmitting antennas to communicate with K users, each user can be configured with a plurality of antennas; mkDenotes the number of receiving antennas for K (K is 1, 2, …, K) th user K, and satisfies
Figure GDA0003151845610000032
Lk(Lk≤Mk) Representing the number of data streams for user k.
Example one
Fig. 1 is a block diagram of a MU-MIMO system applied in the present embodiment.
In a conventional multi-user transmission strategy, the transmission data information of the base station can be expressed as:
Figure GDA0003151845610000033
wherein,
Figure GDA0003151845610000041
the transmission vector symbols representing user k,
Figure GDA0003151845610000042
precoding matrix representing user k
And simultaneously satisfy the constraint conditions
Figure GDA0003151845610000043
E{xxH}＝IN，||wk||2＝1。
The signal received by the user may be expressed as:
Figure GDA0003151845610000044
wherein n iskRepresents an additive white Gaussian noise sample with a mean of 0 and a variance of σk 2(ii) a MU-MIMO channel for user k
Figure GDA0003151845610000045
Is a standard independent identically distributed rayleigh fading channel. M of user kkxN channel matrix HkExpressed as:
Figure GDA0003151845610000046
the conventional SLNR precoding method can be expressed as:
Figure GDA0003151845610000047
wherein, SLNRkIs the signal to leakage noise ratio of said user k.
The traditional SLNR precoding method is based on SLNR of all users kkIndependent precoding matrix wkThe coupling problem encountered in SINR precoding is overcome. However, in an actual communication scenario, due to the influence of estimation errors, channel state information of a downlink channel of a base station is not ideal and is not complete, and thus the bit error rate is still considerable.
In the actual transmission process, the channel matrix H containing complete channel information is used for quantizing noise and interference of other users in the same channelkAnd the estimation error matrix of the incomplete channel state information is estimated by using a channel estimation matrix:
Figure GDA0003151845610000048
Figure GDA0003151845610000049
where E is the estimated error matrix,
Figure GDA00031518456100000410
estimating a channel matrix for the user k; and the estimated error matrix E is the variance of sigmaEThe zero mean value of (a) is circularly symmetric to a complex gaussian variable.
The invention adopts a Minimum Mean Square Error (MMSE) method based on the eigenvector to improve the estimation channel matrix, thereby effectively reducing the influence of the estimation Error matrix.
The equivalent channel matrix improved based on the estimated channel matrix satisfies the following conditions:
Figure GDA00031518456100000411
wherein F is the channel modification matrix; h'kThe equivalent channel matrix for user k. Considering the constraint condition, I F I calculation should be performed2＝1。
The channel modification matrix F is:
F＝argmin E[||Hk-H'k||2]
solving the F by making the gradient of the mean square error of the F equal to zero, wherein the concrete process is as follows:
σ2＝E[||Hk-HkF+EF||2]
＝E[tr[(Hk-HkF+EF)H(Hk-HkF+EF)]]
＝tr(Hk HHk)-2Re{tr(Hk HHkF)}+tr(FHHk HHkF)+E[tr(FHEHEF)]
and because:
Figure GDA00031518456100000412
so that it is possible to obtain:
Figure GDA00031518456100000413
gradient of the mean square error of the F:
Figure GDA0003151845610000051
let the above equation equal zero, the optimal solution for F can be obtained:
Figure GDA0003151845610000052
Foptis a global minimum, i.e. the minimum under the base station.
To satisfy the power constraint, FoptRequiring a homogenization treatment and based on said F after the homogenization treatmentoptThe mean square deviation σ' of (a) satisfies:
Figure GDA0003151845610000053
wherein λ isiIs a non-zero eigenvalue of the full channel covariance matrix; the full channel covariance matrix HcovSatisfies the following conditions:
Figure GDA0003151845610000054
further, in case that the channel state information is considered to be incomplete, the improved SLNR may be expressed as:
Figure GDA0003151845610000055
SLNR'kis the improved SLNR;
Figure GDA0003151845610000056
an extended channel matrix is obtained for the user k after considering the incomplete channel state information improvement, and:
Figure GDA0003151845610000057
the optimal coding matrix for improved SLNR precoding is available with the generalized reiliant:
Figure GDA0003151845610000058
where max ev is the maximum eigenvector calculation.
Thus, an improved channel estimation matrix and an optimal precoding matrix are obtained.
Example two
The existing precoding method generally adopts a scheme that a transmitting end distributes equal power to each user to distribute power for the users. However, in an actual wireless communication system, the Channel State Information (CSI) quality of each user is generally different. For a MIMO system, the average error rate of all users is determined by the worst error rate of the users. The improvement significance for users with better signal-to-noise ratio is not great, so that the equal power distribution scheme for improving the distribution power to reduce the bit error rate is not economical.
Therefore, the present invention also provides a power allocation method, which increases the transmission power to the worst user by increasing, so as to improve the overall channel quality. The specific method comprises the following steps: the base station assigns based on the improved SLNR value for each user; the power allocated to each user k is inversely proportional to the improved SLNR value for that user k, as follows:
Figure GDA0003151845610000059
wherein, SLNR'kImproved SLNR values or said optimal precoding moments calculated for said improved SLNR based MU-MIMO precoding methodArraying; p is a radical oftThe total transmission power of the base station where the user k is located; p is a radical ofkThe power allocated for the user k.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (4)

1. A MU-MIMO system precoding method based on improved SLNR is characterized in that a least mean square error method based on eigenvector is adopted to improve and estimate a channel matrix, and the specific steps comprise:
the improved equivalent channel matrix based on the estimated channel matrix satisfies
Figure FDA0003151845600000011
Where F is the channel modification matrix, H'kFor the equivalent channel matrix for user k,
Figure FDA0003151845600000012
the estimated channel matrix for the user k;
the channel modification matrix F is:
F＝argminE[||Hk-H’k||2]，
wherein E [ ] is the expected calculation; calculating | | as a norm of the matrix;
solving the F by making the gradient of the mean square error of the F equal to zero, wherein the concrete process is as follows:
σ2＝E[||Hk-HkF+EF||2]
＝E[tr[(Hk-HkF+EF)H(Hk-HkF+EF)]]
＝tr(Hk HHk)-2Re{tr(Hk HHkF)}+tr(FHHk HHkF)+E[tr(FHEHEF)]，
where E is the estimation error matrix, HkA channel matrix containing complete channel information for the user k;
and because:
Figure FDA0003151845600000013
wherein M iskThe number of receive antennas for the user k,
Figure FDA0003151845600000014
is the mean square error of the estimation error matrix E;
this gives:
Figure FDA0003151845600000015
gradient of the mean square error of the F:
Figure FDA0003151845600000016
let the gradient of the mean square error of F be equal to zero, the optimal solution of F can be obtained:
Figure FDA0003151845600000017
wherein FoptIs a global minimum;
based on the F after homogenization treatmentoptThe mean square deviation σ' of (a) satisfies:
Figure FDA0003151845600000018
wherein λ isiIs a non-zero eigenvalue of the full channel covariance matrix;
the full channel covariance matrix HcovSatisfies the following conditions:
Figure FDA0003151845600000019
the improved SLNR can be expressed as:
Figure FDA00031518456000000110
SLNR′kis the improved SLNR, INAn identity matrix of NxN; σ' is the minimum mean square error of the channel modification matrix normalization,
Figure FDA00031518456000000111
extended channel matrix, w, for user k after taking incomplete channel state information into accountkA precoding matrix for the user k, and:
Figure FDA00031518456000000112
obtaining the optimal coding matrix of the improved SLNR pre-coding by utilizing the generalized Rayleigh:
Figure FDA0003151845600000021
wherein maxEV is the maximum feature vector calculation;
thus, an improved estimated channel matrix and an optimal precoding matrix are obtained.
2. The improved SLNR-based MU-MIMO system precoding method of claim 1, wherein the estimated channel matrix for user k satisfies:
Figure FDA0003151845600000022
3. the improved SLNR-based MU-MIMO system precoding method of claim 2, wherein the estimation error matrix E is a zero-mean circularly symmetric complex gaussian variable.
4. A MU-MIMO power distribution method based on improved SLNR is characterized in that a base station distributes based on the improved SLNR value of each user; the power allocated to each user k is inversely proportional to the improved SLNR value for that user k, as follows:
Figure FDA0003151845600000023
wherein, SLNR'kAn improved SLNR value or said optimal precoding matrix calculated for a method according to any of claims 1-3; p is a radical oftThe total transmission power of the base station where the user k is located; p is a radical ofkThe power allocated for the user k.
CN201910445642.5A 2019-05-27 2019-05-27 Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method Active CN110212960B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910445642.5A CN110212960B (en) 2019-05-27 2019-05-27 Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910445642.5A CN110212960B (en) 2019-05-27 2019-05-27 Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method

Publications (2)

Publication Number Publication Date
CN110212960A CN110212960A (en) 2019-09-06
CN110212960B true CN110212960B (en) 2021-09-14

Family

ID=67788721

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910445642.5A Active CN110212960B (en) 2019-05-27 2019-05-27 Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method

Country Status (1)

Country Link
CN (1) CN110212960B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111988069B (en) * 2020-07-16 2021-11-26 东南大学 Large-scale MIMO generalized eigenvector structure precoding solving method and device

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101984571A (en) * 2010-11-09 2011-03-09 北京邮电大学 Pre-coding method for multi-user MIMO system
CN102185683A (en) * 2010-07-29 2011-09-14 东南大学 Signal-to-leakage-and-noise ratio (SLNR) rule statistic-based MIMO (Multiple Input Multiple Output) multi-user downlink transmission method
CN102340336A (en) * 2010-07-20 2012-02-01 普天信息技术研究院有限公司 User pairing method of MU-MIMO based on SLNR
CN108551359A (en) * 2018-03-20 2018-09-18 西安电子科技大学 High-effect multi-user association method for precoding based on leakage and device

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102545984A (en) * 2012-01-10 2012-07-04 北京邮电大学 Linear and nonlinear comprehensive precoding method and device for multi-user multiple-input multiple-output (MIMO) system
US9467871B2 (en) * 2012-09-28 2016-10-11 Qualcomm Incorporated Iterative coordinated beamforming systems and methods

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102340336A (en) * 2010-07-20 2012-02-01 普天信息技术研究院有限公司 User pairing method of MU-MIMO based on SLNR
CN102185683A (en) * 2010-07-29 2011-09-14 东南大学 Signal-to-leakage-and-noise ratio (SLNR) rule statistic-based MIMO (Multiple Input Multiple Output) multi-user downlink transmission method
CN101984571A (en) * 2010-11-09 2011-03-09 北京邮电大学 Pre-coding method for multi-user MIMO system
CN108551359A (en) * 2018-03-20 2018-09-18 西安电子科技大学 High-effect multi-user association method for precoding based on leakage and device

Also Published As

Publication number Publication date
CN110212960A (en) 2019-09-06

Similar Documents

Publication Publication Date Title
CN102859894B (en) The method of feeding back channel state information and user equipment (ue) device in a wireless communication system
US8467729B2 (en) Method and apparatus for eliminating multi-user interference in multi-antenna system
CN102035615B (en) MIMO-based downlink data transmission method, device and system
US9042476B2 (en) Methods and a device for multi-resolution precoding matrix indicator feedback
US8761283B2 (en) MIMO channel matrix feedback in OFDM systems
KR101408938B1 (en) Apparatus and method for beamforming based on generalized eigen analysis in a multiple input multiple output wireless communication system
TWI474687B (en) Method and system for a delta quantizer for mimo pre-coders with finite rate channel state information feedback
US20070211813A1 (en) MIMO precoding in the presence of co-channel interference
US9094251B2 (en) Multiple input multiple output communication method and system for exchanging coordinated rank information for neighbor cell
KR100956327B1 (en) Method and system for rate reduction pre-coding matrices
CN101626265A (en) Method for realizing downlink beam forming in wireless communication system
CN103117787A (en) Self-adaptive bit distributing method and self-adaptive bit distributing device in cooperative multi-aerial system
CN107707284B (en) Mixed precoding method based on channel statistic codebook quantization feedback
CN107094038B (en) Method, device and system for adjusting power of antenna system
CN101917218A (en) MIMO multi-user system downlink transmission method and system for reducing feedback information
CN107659348B (en) SLNR and THP mixed adaptive precoding design method
CN101582707B (en) A kind of power distribution method and base station thereof
CN110212960B (en) Improved SLNR (Signal to noise ratio) -based MU-MIMO (Multi-user multiple input multiple output) system precoding method and power distribution method
KR20100110196A (en) Apparatus and method for error minimization of cell boundary users in multi-call communication system
CN104113399B (en) User choosing method based on Matrix condition number in multi-user MIMO system
US8767880B2 (en) Processing data in a receiver circuit and receiver circuit
CN101989867B (en) Cooperative communication method and system, base station and mobile terminal device
CN105306115B (en) A kind of mimo system transmitting terminal method for precoding based on mercury water filling
CN108242950B (en) D2D-based CSI feedback method in FDD Massive MIMO network
CN102013906B (en) Multi-mode transmission method based on mailmonitor normalization beamforming and rate control

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant