CN112994762B - MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI - Google Patents

MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI Download PDF

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CN112994762B
CN112994762B CN202110153573.8A CN202110153573A CN112994762B CN 112994762 B CN112994762 B CN 112994762B CN 202110153573 A CN202110153573 A CN 202110153573A CN 112994762 B CN112994762 B CN 112994762B
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CN112994762A (en
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张军
王斌
刘洁
蔡曙
王海荣
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Nanjing University of Posts and Telecommunications
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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
    • H04B7/0426—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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/265—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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Abstract

The invention provides a statistical CSI-based MIMO-NOMA downlink adaptive wireless transmission method, which comprises the following steps that firstly, aiming at an MIMO-NOMA wireless communication system, a base station divides users into strong users and weak users according to channel gain between the users; secondly, a transmit signal covariance matrix is designed based on the statistical CSI, and an optimal transmit power allocation scheme is designed. The sending signal covariance matrix and the power distribution scheme only depend on the statistical CSI, and the total transmission rate of all users in the system is maximized under the condition of meeting the minimum transmission rate requirement of weak users in the system. The invention effectively improves the total transmission rate of the system and the fairness of users in the system, and has important practical significance for the development of the MIMO-NOMA wireless communication system.

Description

MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a Multiple-Input Multiple-Output Non-Orthogonal Multiple Access (MIMO-NOMA) downlink adaptive wireless transmission method based on Channel State Information (CSI).
Background
In recent years, various communication technologies have been proposed and developed in order to cope with the continuously increasing number of wireless devices, to achieve higher data transmission rates, and to meet the strict requirements of the service quality of smart devices. NOMA is just one of the emerging communication technologies. Unlike the conventional Orthogonal Multiple Access (OMA) method, NOMA serves a plurality of users in a single resource block (frequency band or time slot), thereby realizing a large-scale connection. On the receiving side, the NOMA technique achieves correct demodulation by using a serial interference cancellation receiver to cancel unwanted signals. In addition, the NOMA technique prioritizes the communication performance of weak users during transmission, and individual users in the system do not have to wait for a particular time slot. Therefore, compared with the OMA method, the NOMA technique effectively solves the fairness problem among users and reduces the average time delay, and is widely applied to communication systems. However, how to design an adaptive transmission characteristic pattern and power allocation scheme based on statistical CSI is a problem that needs to be solved urgently.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a statistical CSI-based MIMO-NOMA downlink adaptive wireless transmission method, which effectively improves the total transmission rate of a system and the fairness of users in the system and has important practical significance for the development of an MIMO-NOMA wireless communication system.
The invention content is as follows: the invention provides a MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI, which specifically comprises the following steps:
(1) constructing a statistic CSI-assisted MIMO-NOMA downlink wireless transmission system, wherein the system comprises a multi-antenna base station and two multi-antenna users; the base station divides the users into strong users and weak users according to the channel gain between the base station and the users;
(2) designing an optimal transmit signal covariance matrix based on statistical CSI;
(3) and designing an optimal transmission power distribution scheme according to the covariance matrix of the transmission signals.
Further, the step (1) is realized as follows:
suppose a base station in the system has N antennas, a strong user has N antennas, and a weak user has m antennasAn antenna; the system includes two channels, which are respectively the channel H between the base station and the strong user1And channel H between base station and weak user2The two channels are modeled as:
Figure BDA0002933504650000021
wherein
Figure BDA0002933504650000022
Is a matrix of N x N and,
Figure BDA0002933504650000023
is an m × N matrix representing the scatter component and line-of-sight component of each channel, respectively; r1,T1,R2,T2Deterministic non-negative matrices, R, N x N, m x m, N x N, respectivelyiRepresenting the receiving antenna correlation matrix, TiRepresenting the transmit antenna correlation matrix, XiRepresents the random component of the channel and obeys a mean of 0 and a variance of
Figure BDA0002933504650000024
I is 1, 2;
Figure BDA0002933504650000025
representing the square root operation of the matrix.
Further, the step (2) comprises the steps of:
(21) let Q1And Q2The covariance matrix is a matrix with the size of NxN and respectively represents the covariance matrix of the sending signals of the strong user and the weak user;
(22) given Q1Optimum Q2Expressed as:
Figure BDA0002933504650000026
wherein, B2And
Figure BDA0002933504650000027
is an auxiliary variable, and the specific expression is as follows:
B2＝(IN+A21Q1)-1A21,
Figure BDA0002933504650000028
wherein the content of the first and second substances,
Figure BDA0002933504650000029
and
Figure BDA00029335046500000210
are respectively to the matrix B2Performing singular value decomposition
Figure BDA00029335046500000211
The obtained eigenvector matrix and eigenvalue matrix, mu2Is that Q2Normalization parameter to meet base station transmit power constraints, (.)HConjugate transpose of the representation matrix, (.)+Represents the maximum value of the data in parentheses compared with 0 (·)-1Representing the inversion of a matrix, A21Is an auxiliary variable, and the expression is as follows:
Figure BDA00029335046500000212
wherein σ2Is a noise term that is a function of,
Figure BDA00029335046500000213
the equivalent channel parameters are based on statistical CSI, and the expressions are respectively as follows:
Figure BDA0002933504650000031
Figure BDA0002933504650000032
wherein, ImAnd INUnit matrixes of m × m and N × N respectively;
(23) order to
Figure BDA0002933504650000033
Designing optimal Q1：
Figure BDA0002933504650000034
Wherein, VA1B1Is to the matrix
Figure BDA0002933504650000035
And (3) carrying out generalized singular value decomposition to obtain a characteristic vector matrix, wherein the specific generalized singular value decomposition form is as follows:
Figure BDA0002933504650000036
Figure BDA0002933504650000037
wherein the content of the first and second substances,
Figure BDA0002933504650000038
and
Figure BDA0002933504650000039
a matrix of eigenvectors derived for the generalized singular values,
Figure BDA00029335046500000310
and
Figure BDA00029335046500000311
eigenvalue matrix, matrix D, A, obtained for generalized singular values1,B1All are statistical CSI approximate correlation matrixes, and the expressions are respectively as follows:
Figure BDA00029335046500000312
Figure BDA00029335046500000313
B1＝(IN+A21Q2)-1A21,
wherein the content of the first and second substances,
Figure BDA00029335046500000314
to Q before optimization1，A22Is an auxiliary variable, and the expression is as follows:
Figure BDA00029335046500000315
wherein the content of the first and second substances,
Figure BDA00029335046500000316
the equivalent channel parameters are based on the system statistical CSI, and the expressions are respectively as follows:
Figure BDA00029335046500000317
Figure BDA00029335046500000318
Figure BDA0002933504650000041
Figure BDA0002933504650000042
wherein, InAn identity matrix of n × n; lambdaGSVDIs a diagonal matrix representing the optimal power allocation, whose diagonal elements are:
Figure BDA0002933504650000043
wherein the content of the first and second substances,
Figure BDA0002933504650000044
are respectively diagonal matrixes
Figure BDA0002933504650000045
Diagonal element of middle, μ1Is that Q1Normalization parameter, v, to meet base station transmit power limitationsiIs a matrix
Figure BDA0002933504650000046
The diagonal elements of (1);
(24) order to
Figure BDA0002933504650000047
Repeating the steps (22) and (23) until the total transmission rate of the system is converged to obtain the optimal covariance matrix of the sending signals
Figure BDA0002933504650000048
Further, the step (3) includes the steps of:
(31) assuming that the total transmission power of the base station is P, the optimal transmission power of the strong user is
Figure BDA0002933504650000049
The optimal transmit power for the weak user is
Figure BDA00029335046500000410
(32) Computing
Figure BDA00029335046500000411
Wherein the content of the first and second substances,
Figure BDA00029335046500000412
P1,min＝0,P1,maxp according to*Calculating the transmission rate of weak usersRate R2If R is2≤R0Let P1,max＝P*(ii) a Otherwise, let P1,min＝P*；
(33) Then repeat (32) until P1,max-P1,minEpsilon is less than or equal to, and the final converged P is obtained*，R0Is the minimum transmission rate required for the user to communicate normally, and epsilon is a parameter indicating the convergence condition.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. the invention considers the space correlation of the transmitting antenna and the receiving antenna when designing the covariance matrix of the transmitting signal, and obtains the optimal solution of the covariance matrix of the two transmitting signals through iterative optimization, so that the total transmission rate of the system can be improved to the maximum extent; 2. the design scheme of the covariance matrix and the power distribution of the transmitted signals provided by the invention only utilizes statistical CSI; the statistical CSI is easier to obtain than the instantaneous CSI, so that the cost of a communication system can be effectively reduced, the method has strong feasibility and can be applied to actual communication scenes.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a MIMO-NOMA downlink wireless transmission system.
Detailed Description
The technical scheme of the invention is clearly and completely described below with reference to the accompanying drawings.
The invention provides a MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI, which is characterized in that under the condition that the sending power of a base station is limited, the maximum total transmission rate of a system is used as a target, a statistical CSI is used for designing a sending signal covariance matrix and an optimal power distribution scheme, so that the total transmission rate of the system is maximum, the transmission performance of weak users is considered preferentially, and the fairness problem among the users is solved, as shown in figure 1, the method specifically comprises the following steps:
step 1: a statistical CSI-assisted MIMO-NOMA downlink wireless transmission system is constructed, as shown in fig. 2, and includes a multi-antenna base station and two multi-antenna users, and the base station divides the users into strong users and weak users according to the channel gain between the users.
Aiming at a statistic CSI-assisted MIMO-NOMA downlink transmission system, a base station divides users into strong users and weak users according to the channel gain between the users; suppose that a base station in the system has N antennas, a strong user has N antennas, and a weak user has m antennas; the system includes two channels, which are respectively the channel H between the base station and the strong user1And channel H between base station and weak user2The two channels are modeled as:
Figure BDA0002933504650000051
wherein
Figure BDA0002933504650000052
Is a matrix of N x N and,
Figure BDA0002933504650000053
is an m × N matrix representing the scatter component and line-of-sight component of each channel, respectively; r1,T1,R2,T2Deterministic non-negative matrices, R, N x N, m x m, N x N, respectivelyiRepresenting the receiving antenna correlation matrix, TiRepresenting the transmit antenna correlation matrix, XiRepresents the random component of the channel and obeys a mean of 0 and a variance of
Figure BDA0002933504650000054
I is 1, 2;
Figure BDA0002933504650000055
representing the square root operation of the matrix.
Step 2: and designing an optimal transmit signal covariance matrix based on the statistical CSI.
(2.1) setting Q1And Q2The covariance matrix is a matrix with the size of NxN and respectively represents the covariance matrix of the sending signals of the strong user and the weak user;
(2.2) given Q1Optimum Q2Expressed as:
Figure BDA0002933504650000061
wherein, B2And
Figure BDA0002933504650000062
is an auxiliary variable, and the specific expression is as follows:
B2＝(IN+A21Q1)-1A21,
Figure BDA0002933504650000063
wherein the content of the first and second substances,
Figure BDA0002933504650000064
and
Figure BDA0002933504650000065
are respectively to the matrix B2Performing singular value decomposition
Figure BDA0002933504650000066
The obtained eigenvector matrix and eigenvalue matrix, mu2Is that Q2Normalization parameter to meet base station transmit power constraints, (.)HConjugate transpose of the representation matrix, (.)+Represents the maximum value of the data in parentheses compared with 0 (·)-1Representing the inversion of a matrix, A21Is an auxiliary variable, and the expression is as follows:
Figure BDA0002933504650000067
wherein σ2Is a noise term that is a function of,
Figure BDA0002933504650000068
the equivalent channel parameters are based on statistical CSI, and the expressions are respectively as follows:
Figure BDA0002933504650000069
Figure BDA00029335046500000610
wherein, ImAnd INUnit matrixes of m × m and N × N respectively;
(2.3) order
Figure BDA00029335046500000611
Designing optimal Q1：
Figure BDA00029335046500000612
Wherein the content of the first and second substances,
Figure BDA00029335046500000613
is to the matrix
Figure BDA00029335046500000614
And (3) carrying out generalized singular value decomposition to obtain a characteristic vector matrix, wherein the specific generalized singular value decomposition form is as follows:
Figure BDA00029335046500000615
Figure BDA00029335046500000616
wherein the content of the first and second substances,
Figure BDA00029335046500000617
and
Figure BDA00029335046500000618
a matrix of eigenvectors derived for the generalized singular values,
Figure BDA00029335046500000619
and
Figure BDA00029335046500000620
eigenvalue matrix, matrix D, A, obtained for generalized singular values1,B1All are statistical CSI approximate correlation matrixes, and the expressions are respectively as follows:
Figure BDA0002933504650000071
Figure BDA0002933504650000072
B1＝(IN+A21Q2)-1A21,
wherein the content of the first and second substances,
Figure BDA0002933504650000073
to Q before optimization1，A22Is an auxiliary variable, and the expression is as follows:
Figure BDA0002933504650000074
wherein the content of the first and second substances,
Figure BDA0002933504650000075
the equivalent channel parameters are based on the system statistical CSI, and the expressions are respectively as follows:
Figure BDA0002933504650000076
Figure BDA0002933504650000077
Figure BDA0002933504650000078
Figure BDA0002933504650000079
wherein, InAn identity matrix of n × n; lambdaGSVDIs a diagonal matrix representing the optimal power allocation, whose diagonal elements are:
Figure BDA00029335046500000710
wherein the content of the first and second substances,
Figure BDA00029335046500000711
are respectively diagonal matrixes
Figure BDA00029335046500000712
Diagonal element of middle, μ1Is that Q1Normalization parameter, v, to meet base station transmit power limitationsiIs a matrix
Figure BDA00029335046500000713
The diagonal elements of (1);
(2.4) order
Figure BDA00029335046500000714
Repeating the steps (2.2) and (2.3) until the total transmission rate of the system is converged to obtain the optimal covariance matrix of the sending signals
Figure BDA00029335046500000715
And step 3: designing an optimal transmission power distribution scheme according to the covariance matrix of the transmission signals
(4.1) assuming that the total transmission power of the base station is P, the optimal transmission power of the strong user is
Figure BDA0002933504650000081
The optimal transmit power for the weak user is
Figure BDA0002933504650000082
(4.2) calculation of
Figure BDA0002933504650000083
Figure BDA0002933504650000084
Wherein the content of the first and second substances,
Figure BDA0002933504650000085
P1,min＝0,P1,maxp according to*Calculating the transmission rate R of weak users2If R is2≤R0Let P1,max＝P*(ii) a Otherwise, let P1,min＝P*，
(4.3) then repeating (4.2) until P1,max-P1,minEpsilon is less than or equal to, and the final converged P is obtained*，R0Is the minimum transmission rate required for the user to communicate normally, and epsilon is a parameter indicating the convergence condition.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (1)

1. A MIMO-NOMA downlink adaptive wireless transmission method based on statistical CSI is characterized by comprising the following steps:
(1) constructing a statistic CSI-assisted MIMO-NOMA downlink wireless transmission system, wherein the system comprises a multi-antenna base station and two multi-antenna users; the base station divides the users into strong users and weak users according to the channel gain between the base station and the users;
(2) designing an optimal transmit signal covariance matrix based on statistical CSI;
(3) designing an optimal transmission power distribution scheme according to the covariance matrix of the transmission signals;
the step (1) is realized by the following steps:
suppose that a base station in the system has N antennas, a strong user has N antennas, and a weak user has m antennas; the system includes two channels, which are respectively the channel H between the base station and the strong user1And channel H between base station and weak user2The two channels are modeled as:
Figure FDA0003547969480000011
wherein
Figure FDA0003547969480000012
Is a matrix of N x N and,
Figure FDA0003547969480000013
is an m × N matrix representing the scatter component and line-of-sight component of each channel, respectively; r1,T1,R2,T2Deterministic non-negative matrices, R, N x N, m x m, N x N, respectivelyiRepresenting the receiving antenna correlation matrix, TiRepresenting the transmit antenna correlation matrix, XiRepresents the random component of the channel and obeys a mean of 0 and a variance of
Figure FDA0003547969480000014
I is 1, 2;
Figure FDA0003547969480000015
square root operations representing matrices;
the step (2) comprises the following steps:
(21) let Q1And Q2The covariance matrix is a matrix with the size of NxN and respectively represents the covariance matrix of the sending signals of the strong user and the weak user;
(22) given Q1Optimum Q2Expressed as:
Figure FDA0003547969480000016
wherein, B2And
Figure FDA0003547969480000017
is an auxiliary variable, and the specific expression is as follows:
B2＝(IN+A21Q1)-1A21,
Figure FDA0003547969480000018
wherein the content of the first and second substances,
Figure FDA0003547969480000019
and
Figure FDA00035479694800000110
are respectively to the matrix B2Performing singular value decomposition
Figure FDA00035479694800000111
The obtained eigenvector matrix and eigenvalue matrix, mu2Is that Q2Normalization parameter to meet base station transmit power constraints, (.)HConjugate transpose of the representation matrix, (.)+Represents the maximum value of the data in parentheses compared with 0 (·)-1Representing the inversion of a matrix, A21Is an auxiliary variable, and the expression is as follows:
Figure FDA0003547969480000021
wherein σ2Is a noise term that is a function of,
Figure FDA0003547969480000022
the equivalent channel parameters are based on statistical CSI, and the expressions are respectively as follows:
Figure FDA0003547969480000023
Figure FDA0003547969480000024
wherein, ImAnd INUnit matrixes of m × m and N × N respectively;
(23) order to
Figure FDA0003547969480000025
Designing optimal Q1：
Figure FDA0003547969480000026
Wherein the content of the first and second substances,
Figure FDA0003547969480000027
is to the matrix
Figure FDA0003547969480000028
And (3) carrying out generalized singular value decomposition to obtain a characteristic vector matrix, wherein the specific generalized singular value decomposition form is as follows:
Figure FDA0003547969480000029
Figure FDA00035479694800000210
wherein the content of the first and second substances,
Figure FDA00035479694800000211
and
Figure FDA00035479694800000212
a matrix of eigenvectors derived for the generalized singular values,
Figure FDA00035479694800000213
and
Figure FDA00035479694800000214
eigenvalue matrix, matrix D, A, obtained for generalized singular values1,B1All are statistical CSI approximate correlation matrixes, and the expressions are respectively as follows:
Figure FDA00035479694800000215
Figure FDA00035479694800000216
B1＝(IN+A21Q2)-1A21,
wherein the content of the first and second substances,
Figure FDA0003547969480000031
to Q before optimization1，A22Is an auxiliary variable, and the expression is as follows:
Figure FDA0003547969480000032
wherein the content of the first and second substances,
Figure FDA0003547969480000033
the equivalent channel parameters are based on the system statistical CSI, and the expressions are respectively as follows:
Figure FDA0003547969480000034
Figure FDA0003547969480000035
Figure FDA0003547969480000036
Figure FDA0003547969480000037
wherein, InAn identity matrix of n × n; lambdaGSVDIs a diagonal matrix representing the optimal power allocation, whose diagonal elements are:
Figure FDA0003547969480000038
wherein the content of the first and second substances,
Figure FDA0003547969480000039
are respectively diagonal matrixes
Figure FDA00035479694800000310
Diagonal element of middle, μ1Is that Q1Normalization parameter, v, to meet base station transmit power limitationsiIs a matrix
Figure FDA00035479694800000311
The diagonal elements of (1);
(24) order to
Figure FDA00035479694800000312
Repeating the steps (22) and (23) until the total transmission rate of the system is converged to obtain the optimal covariance matrix of the sending signals
Figure FDA00035479694800000313
The step (3) comprises the following steps:
(31) suppose the total transmission power of the base station is P, and the optimal transmission power of the strong user is P1 optThe optimal transmission power of the weak user is P-P1 opt；
(32) Calculating P1 opt：P1 opt＝P*Wherein, in the step (A),
Figure FDA00035479694800000314
P1,min＝0,P1,maxp according to*Calculating the transmission rate R of weak users2If R is2≤R0Let P1,max＝P*(ii) a Otherwise, let P1,min＝P*；
(33) Then repeat (32) until P1,max-P1,minEpsilon is less than or equal to, and the final converged P is obtained*，R0Is the minimum transmission rate required for the user to communicate normally, and epsilon is a parameter indicating the convergence condition.
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