CN114448481B - Large-scale MIMO precoding method based on user scheduling under Internet of vehicles - Google Patents

Large-scale MIMO precoding method based on user scheduling under Internet of vehicles Download PDF

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CN114448481B
CN114448481B CN202210068087.0A CN202210068087A CN114448481B CN 114448481 B CN114448481 B CN 114448481B CN 202210068087 A CN202210068087 A CN 202210068087A CN 114448481 B CN114448481 B CN 114448481B
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CN114448481A (en
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何世彪
杜洁汝
尹子松
廖勇
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Chongqing Institute of Engineering
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    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a large-scale MIMO precoding method based on user scheduling under the Internet of vehicles, which comprises the following steps: s1, establishing a channel model of large-scale MIMO under a vehicle networking; s2, multi-user scheduling combining channel correlation and channel self conditions is carried out, and a scheduling user set is obtained; s3, precoding based on block diagonalization is carried out according to the multi-user scheduling result, so that the purpose of eliminating interference is achieved. The precoding method based on multi-user scheduling for the Internet of vehicles can obtain larger user diversity gain by selecting the high-quality user set for service, can effectively reduce the system error rate and improves the throughput rate of the Internet of vehicles system.

Description

Large-scale MIMO precoding method based on user scheduling under Internet of vehicles
Technical field:
the invention relates to the field of wireless communication, in particular to a large-scale MIMO precoding method based on user scheduling under the Internet of vehicles.
The background technology is as follows:
for the increasing demands of Vehicle-to-evaluation (V2X) communication and the shortage of spectrum resources, the conventional multiple input multiple output (Multiple Input Multiple Output, MIMO) technology cannot meet the demands of users for communication service quality at present, and the massive MIMO technology can enhance diversity and multiplexing gain, so that the massive MIMO technology is used in the V2X field, which can provide vehicles with high data rate and high reliability communication. Meanwhile, since the V2X system has limited resources, it is very necessary to study how to increase the system transmission rate and increase the user diversity gain in multi-user joint communication.
In a V2X environment of multiple users and multiple cells, the design of a precoding algorithm capable of improving multi-user scheduling of a large-scale MIMO system has very important significance for V2X communication. The precoding method based on multi-user scheduling can be used for service by selecting a high-quality user set to obtain larger user diversity gain, and in addition, the characteristics of channel variation of different users can be utilized at a base station end to preprocess each user, so that channel interference (Co-Channel Interference, CCI) is eliminated, the larger user diversity gain is effectively obtained, and the transmission rate of the system is improved.
The invention comprises the following steps:
the invention aims at least solving the technical problems in the prior art, and particularly creatively provides a large-scale MIMO precoding method based on user scheduling under the Internet of vehicles, which ensures the channel quality of the scheduling users while meeting the minimum user interference, overcomes the operation problem of using a large number of matrix decomposition in a scheduling algorithm, effectively reduces the system error rate and improves the system throughput rate.
In order to achieve the above object of the present invention, the present invention provides a large-scale MIMO precoding method based on user scheduling in the internet of vehicles, which is characterized by comprising:
s1, establishing a channel model of large-scale MIMO under a vehicle networking;
s2, multi-user scheduling combining channel correlation and channel self conditions is carried out, and a scheduling user set is obtained;
s3, precoding based on block diagonalization is carried out according to the multi-user scheduling result, so that the purpose of eliminating interference is achieved.
The large-scale MIMO precoding method based on user scheduling under the Internet of vehicles is characterized in that the S1 comprises the following steps:
due to the high-speed motion of the vehicle, the channel characteristics show time-varying, doppler effect is generated, and then the Doppler effect is introduced into the channel model to be combined with a conventional millimeter wave channel, and then the time-varying geometric channel model of the t-th time slot can be expressed as
Figure GDA0004150442440000021
wherein ,(·)H Representing a conjugate transpose of the matrix or vector;
Figure GDA0004150442440000022
is a normalization correction factor; n (N) t 、N r The number of transmitting antennas of a transmitting end and the number of receiving antennas of each vehicle user are respectively represented; n (N) c Representing the number of clusters in a channel；N ray The number of sub-channels in each cluster is represented; scalar alpha il (t) represents the gain of the ith path in the ith cluster, following the complex Gaussian distribution +.>
Figure GDA0004150442440000023
Figure GDA0004150442440000024
and />
Figure GDA0004150442440000025
Representing array steering vectors for receiving and transmitting antennas, respectively, i.e
Figure GDA0004150442440000026
Figure GDA0004150442440000027
wherein ,(·)T Representing a transpose of the vector;
Figure GDA0004150442440000028
and />
Figure GDA0004150442440000029
Respectively representing the arrival angle and the departure angle of the first path in the ith cluster; lambda is the millimeter wave wavelength; d is the distance between antenna elements;
assuming an average power allocation, when the system adopts a block diagonalization (Block Diagonalization, BD) precoding algorithm at the transmitting end, for the R users finally selected, the channel capacity available to the kth user is
Figure GDA00041504424400000210
Wherein B is the channel bandwidth; p is the total power of the emission; sigma (sigma) 2 Is additive white gaussian noise; det (·) represents matrix rankA formula (I); i N Representing an identity matrix of dimension N.
The large-scale MIMO precoding method based on user scheduling under the Internet of vehicles is characterized in that the S2 comprises the following steps:
s2-1, according to the channel correlation theory, two matrixes H i and Hj Assembled splice matrix H i,j ＝[H i ,H j ]The higher the level of orthogonality of the column vectors of (2) the matrix H i and Hj The lower the correlation between them; first matrix H i and Hj Vectorizing, wherein the matrix vectorizing method comprises the following steps of
Figure GDA0004150442440000031
Figure GDA0004150442440000032
The pearson coefficients of the correlation of the channel matrix, which are quantized using matrix, can be expressed as
Figure GDA0004150442440000033
Wherein, the color ij (v i ,v j )∈[-1,1]The absolute value of the matrix H i and Hj Pearson correlation coefficient of (b); coor ij (v i ,v j ) The larger the absolute value, the higher the channel correlation between user i and user j, and thus the more serious the interference between the two users, and vice versa; therefore, the color ij (v i ,v j ) The smaller the absolute value of the two matrices H i and Hj The lower the spatial correlation between them.
S2-2, greedy algorithm is implemented by calculating noise enhancement factor l k Selecting a system that can guarantee the current system
Figure GDA0004150442440000034
The largest user can find +.>
Figure GDA0004150442440000035
Is determined by the channel gain of each user in set S; thus, the noise enhancement factor of the user k Also a main factor affecting the system capacity, the scheduling factor measuring the channel's own condition according to the noise enhancement factor is denoted +.>
Figure GDA0004150442440000036
wherein
Figure GDA0004150442440000037
||·|| 2 Representing the 2 norms of the matrix; />
Figure GDA00041504424400000310
Is a unit matrix;
according to S2-1 and S2-2, ρ is reduced k coor ks As a criterion for the multi-user scheduling algorithm herein, all user selections ρ are traversed k coor ks The smallest user is used as the dispatching user until the quantity of the dispatching users reaches R, and an optimal dispatching user set is obtained, so that a channel of the dispatching user set is used as an effective transmission channel of the system, and a better system user gain is obtained, wherein a color is obtained ks Representing the pearson coefficients between the kth user in the set of candidate users and the set of selected users s.
The large-scale MIMO precoding method based on user scheduling under the Internet of vehicles is characterized in that the S3 comprises the following steps:
the core of the precoding method based on block diagonalization is that a precoding matrix F is obtained by carrying out SVD decomposition twice, wherein the precoding matrix of a user k is
Figure GDA0004150442440000038
wherein />
Figure GDA0004150442440000039
The space is respectively expressed as a channel matrix zero space obtained after the first SVD decomposition and the second SVD decomposition;
representing the channel matrix as
Figure GDA0004150442440000041
Then the complement matrix for user k
Figure GDA0004150442440000042
Is that
Figure GDA0004150442440000043
For a pair of
Figure GDA0004150442440000044
SVD decomposition is performed to obtain
Figure GDA0004150442440000045
wherein ,
Figure GDA0004150442440000046
and />
Figure GDA0004150442440000047
Are respectively->
Figure GDA0004150442440000048
Before->
Figure GDA0004150442440000049
And (6) back->
Figure GDA00041504424400000410
Right singular value vector, ">
Figure GDA00041504424400000411
Is->
Figure GDA00041504424400000412
Is a zero space of (2);
order the
Figure GDA00041504424400000413
Equivalent channel matrix for system>
Figure GDA00041504424400000414
SVD decomposition is carried out to obtain
Figure GDA00041504424400000415
Order the
Figure GDA00041504424400000416
Obtaining a precoding matrix of a user k and a combiner as follows
Figure GDA00041504424400000417
W k ＝U k
The main purpose of the block diagonalization precoding algorithm is to find the optimal precoding matrix for user K (k=1,..once, K) to satisfy it
Figure GDA00041504424400000418
Thereby achieving the purpose of completely eliminating interference.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the characteristic that the block diagonalized precoding is sensitive to the channel correlation is utilized, the channel correlation among users is measured by adopting the pearson coefficient after matrix vectorization, and meanwhile, the scheduling factor for measuring the channel quality is defined according to the user noise enhancement factor, so that the channel correlation is minimized, and meanwhile, the channel with high quality is ensured to be selected, thereby realizing multi-user scheduling. The method can obtain higher user channel gain, effectively reduces the error rate of the system, and is applicable to V2X communication.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
fig. 1 is a flow chart of multi-user scheduling of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The invention provides a large-scale MIMO precoding method based on user scheduling in the Internet of vehicles, which comprehensively considers two factors of channel correlation and channel self conditions, ensures to select a high-quality channel while meeting the minimum channel correlation, thereby realizing multi-user scheduling, and then adopts BD precoding to eliminate data stream interference after scheduling.
The invention is described in detail with reference to fig. 1, and mainly comprises the following steps:
step S101, start.
Step S102, inputting channel matrix H, user number K, user receiving antenna number N r The base station may schedule the number of users R, the current iteration number i=1.
Step S103, establishing a set of users to be selected s= {1,2,..once, K }, the set of users to be selected
Figure GDA0004150442440000051
Step S104, traversing the user set S to be selected, and selecting
Figure GDA0004150442440000052
Step S105, updating the set of candidate users, the set of selected users, and the number of selected users s=s- { S 1 }、L＝L+{s 1 M=1, wherein,
Figure GDA0004150442440000053
H k is the channel matrix for the kth user.
Step S106, calculating the channel capacity of the selected user
Figure GDA0004150442440000061
Step S107, if the iteration number i < R, the following steps are performed, otherwise, the process jumps to S111.
Step S108, traversing each user in the candidate user set S, and calculating the scheduling factor rho of each user k And the pearson coefficient cor between the user and the selected set of users S kL 。
Pearson coefficient cor between the user and the selected set of users S kL Scheduling factor ρ for each user k The calculation modes are respectively as follows:
according to the theory of channel correlation, two matrices H i and Hj Assembled splice matrix H i,j ＝[H i ,H j ]The higher the level of orthogonality of the column vectors of (2) the matrix H i and Hj The lower the correlation between them; first matrix H i and Hj Vectorizing, wherein the matrix vectorizing method comprises the following steps of
Figure GDA0004150442440000062
Figure GDA0004150442440000063
The pearson coefficients of the correlation of the channel matrix, which are quantized using matrix, can be expressed as
Figure GDA0004150442440000064
Wherein, the color ij (v i ,v j )∈[-1,1]The absolute value of the matrix H i and Hj Pearson correlation coefficient of (b); coor ij (v i ,v j ) The larger the absolute value, the higher the channel correlation between user i and user j, and thus the more serious the interference between the two users, and vice versa; therefore, the color ij (v i ,v j ) The smaller the absolute value of the two matrices H i and Hj The lower the spatial correlation between them.
The greedy algorithm is implemented by calculating the noise enhancement factor l k Selecting a system that can guarantee the current system
Figure GDA0004150442440000065
The largest user can find +.>
Figure GDA0004150442440000066
Is determined by the channel gain of each user in set S; thus, the noise enhancement factor of the user k Also a main factor affecting system capacity, a scheduling factor measuring channel conditions according to noise enhancement factors is expressed as
Figure GDA0004150442440000067
wherein ,
Figure GDA0004150442440000068
||·|| 2 representing the 2 norms of the matrix; />
Figure GDA0004150442440000069
Is a unit matrix;
step S109, selecting the satisfaction of
Figure GDA0004150442440000071
And calculates the system capacity C at that time m ；
Step S110, if the current system capacity C m ＞C max Continuing to execute the following steps, otherwise jumping to the step S111;
step S111, updating the candidate user set s=s- { S i Selected user set
Figure GDA0004150442440000072
C max ＝C m User adjustment number m=m+1, i=i+1
Step S111 ends.
In this embodiment, the channel matrix of the scheduled user set can be obtained by performing the above steps
Figure GDA0004150442440000073
And further, a block diagonalization-based precoding method can be used for subsequent processing.
In this embodiment, the core of the block diagonalization precoding method is to perform SVD decomposition twice to obtain a precoding matrix F, and then the precoding matrix of the user k can be expressed as
Figure GDA0004150442440000074
wherein />
Figure GDA0004150442440000075
Represented as the channel matrix null space obtained after the first and second SVD decomposition, respectively. />
Channel matrix of the scheduling user set to be obtained
Figure GDA0004150442440000076
Represented as
Figure GDA0004150442440000077
Then the complement matrix for user k
Figure GDA0004150442440000078
Is that
Figure GDA0004150442440000079
For a pair of
Figure GDA00041504424400000710
SVD decomposition is performed to obtain
Figure GDA00041504424400000711
wherein ,
Figure GDA00041504424400000712
and />
Figure GDA00041504424400000713
Respectively are/>
Figure GDA00041504424400000714
Before->
Figure GDA00041504424400000715
And (6) back->
Figure GDA00041504424400000716
Right singular value vector, ">
Figure GDA00041504424400000717
Is->
Figure GDA00041504424400000718
Is used in the field of the optical system,
order the
Figure GDA00041504424400000719
Equivalent channel matrix for system>
Figure GDA00041504424400000720
SVD decomposition is carried out to obtain
Figure GDA00041504424400000721
Order the
Figure GDA00041504424400000722
Obtaining a precoding matrix of a user k and a combiner as follows
Figure GDA00041504424400000723
W k ＝U k
The main purpose of using a block diagonalization precoding algorithm is to find the optimal precoding matrix for user K (k=1,..once, K) to satisfy it
Figure GDA00041504424400000724
Thereby achieving the purpose of completely eliminating interference in the present embodiment.
So far, the result of eliminating interference by the precoding method in the embodiment is obtained.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.

Claims (1)

1. A large-scale MIMO precoding method based on user scheduling under the Internet of vehicles is characterized by comprising the following steps:
s1, establishing a channel model of large-scale MIMO under the vehicle networking, comprising the following steps:
due to the high speed motion of the vehicle, the channel characteristics show time-varying, doppler effect is generated, and then the Doppler effect is introduced into the channel model to be combined with a conventional millimeter wave channel, and then the time-varying geometric channel model of the t time slot can be expressed as
Figure QLYQS_1
wherein ,(·)H Representing a conjugate transpose of the matrix or vector;
Figure QLYQS_2
is the normalization correction factorA seed; n (N) t 、N r The number of transmitting antennas of a transmitting end and the number of receiving antennas of each vehicle user are respectively represented; n (N) c Representing the number of clusters in the channel; n (N) ray The number of sub-channels in each cluster is represented; scalar alpha il (t) represents the gain of the ith path in the ith cluster, subject to complex Gaussian distribution
Figure QLYQS_3
Figure QLYQS_4
and />
Figure QLYQS_5
Representing array steering vectors for receiving and transmitting antennas, respectively, i.e
Figure QLYQS_6
Figure QLYQS_7
wherein ,(·)T Representing a transpose of the vector;
Figure QLYQS_8
and />
Figure QLYQS_9
Respectively representing the arrival angle and the departure angle of the first path in the ith cluster; lambda is the millimeter wave wavelength; d is the distance between antenna elements;
assuming an average power allocation, when the system adopts a block diagonalization (Block Diagonalization, BD) precoding algorithm at the transmitting end, for the R users finally selected, the channel capacity available to the kth user is
Figure QLYQS_10
Wherein B is the channel bandwidth; p is the total power of the emission; sigma (sigma) 2 Is additive white gaussian noise; det (·) represents a matrix determinant; i N Representing an identity matrix with a dimension N;
s2, multi-user scheduling combining channel correlation and channel self conditions is carried out, and a scheduling user set is obtained, wherein the method comprises the following steps:
s2-1, according to the channel correlation theory, two matrixes H i and Hj Assembled splice matrix H i,j ＝[H i ,H j ]The higher the level of orthogonality of the column vectors of (2) the matrix H i and Hj The lower the correlation between them; first matrix H i and Hj Vectorizing, wherein the matrix vectorizing method comprises the following steps of
Figure QLYQS_11
Figure QLYQS_12
The pearson coefficients of the correlation of the channel matrix, which are quantized using matrix, can be expressed as
Figure QLYQS_13
Wherein, the color ij (v i ,v j )∈[-1,1]The absolute value of the matrix H i and Hj Pearson correlation coefficient of (b); coor ij (v i ,v j ) The larger the absolute value, the higher the channel correlation between user i and user j, and thus the more serious the interference between the two users, and vice versa; therefore, the color ij (v i ,v j ) The smaller the absolute value of the two matrices H i and Hj The lower the spatial correlation between them;
s2-2, greedy algorithm is implemented by calculating noise enhancement factor l k Can be selected toEnsuring of current systems
Figure QLYQS_14
The largest user can find +.>
Figure QLYQS_15
Is determined by the channel gain of each user in set S; thus, the noise enhancement factor of the user k Also a main factor affecting system capacity, a scheduling factor measuring channel conditions according to noise enhancement factors is expressed as
Figure QLYQS_16
wherein
Figure QLYQS_17
||·|| 2 Representing the 2 norms of the matrix; />
Figure QLYQS_18
Is a unit matrix;
according to S2-1 and S2-2, ρ is reduced k coor ks As a criterion for the multi-user scheduling algorithm herein, all user selections ρ are traversed k coor ks The smallest user is used as the dispatching user until the quantity of the dispatching users reaches R, and an optimal dispatching user set is obtained, so that a channel of the dispatching user set is used as an effective transmission channel of the system, and a better system user gain is obtained, wherein a color is obtained ks Representing the pearson coefficients between the kth user in the set of candidate users and the set of selected users s.
S3, precoding based on block diagonalization is carried out according to the multi-user scheduling result, so as to achieve the purpose of eliminating interference, and the method comprises the following steps:
the core of the precoding method based on block diagonalization is to perform two singular value decomposition (Singular Value Decomposition, SVD) to obtain a precoding matrix F, wherein the precoding matrix of a user k is
Figure QLYQS_19
wherein />
Figure QLYQS_20
The space is respectively expressed as a channel matrix zero space obtained after the first SVD decomposition and the second SVD decomposition;
representing a channel matrix as
Figure QLYQS_21
Then the complement matrix for user k
Figure QLYQS_22
Is that
Figure QLYQS_23
For a pair of
Figure QLYQS_24
SVD decomposition is performed to obtain
Figure QLYQS_25
wherein ,
Figure QLYQS_26
and />
Figure QLYQS_27
Are respectively->
Figure QLYQS_28
Before->
Figure QLYQS_29
And (6) back->
Figure QLYQS_30
Right curiosityDifferent value vector->
Figure QLYQS_31
Is->
Figure QLYQS_32
Is a zero space of (2);
order the
Figure QLYQS_33
Equivalent channel matrix for system>
Figure QLYQS_34
SVD decomposition is carried out to obtain
Figure QLYQS_35
Order the
Figure QLYQS_36
Obtaining a precoding matrix of a user k and a combiner as follows
Figure QLYQS_37
W k ＝U k
In summary, the main purpose of the block diagonalization precoding algorithm is to find the optimal precoding matrix for user K (k=1,..k.) to satisfy it
Figure QLYQS_38
Thereby achieving the purpose of completely eliminating interference. />
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