CN112994762A - 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 PDFInfo
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- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
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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
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 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:whereinIs a matrix of N x N and,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 ofI is 1, 2;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) to giveFixed Q1Optimum Q2Expressed as:
B2=(IN+A21Q1)-1A21,
wherein the content of the first and second substances,andare respectively to the matrix B2Performing singular value decompositionThe 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:
wherein σ2Is a noise term that is a function of,is based on statistical CSI equivalent beliefThe expressions are as follows:
wherein, ImAnd INUnit matrixes of m × m and N × N respectively;
Wherein, VA1B1Is to the matrixAnd (3) carrying out generalized singular value decomposition to obtain a characteristic vector matrix, wherein the specific generalized singular value decomposition form is as follows:
wherein the content of the first and second substances,anda matrix of eigenvectors derived for the generalized singular values,andeigenvalue matrix, matrix D, A, obtained for generalized singular values1,B1All are statistical CSI approximate correlation matrixes, and the expressions are respectively as follows:
B1=(IN+A21Q2)-1A21,
wherein the content of the first and second substances,to Q before optimization1,A22Is an auxiliary variable, and the expression is as follows:
wherein the content of the first and second substances,the equivalent channel parameters are based on the system statistical CSI, and the expressions are respectively as follows:
wherein, InAn identity matrix of n × n; lambdaGSVDIs a diagonal matrix representing the optimal power allocation, whose diagonal elements are:
wherein the content of the first and second substances,are respectively diagonal matrixesDiagonal element of middle, μ1Is that Q1Normalization parameter, v, to meet base station transmit power limitationsiIs a matrixThe diagonal elements of (1);
(24) order toRepeating 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
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 isThe optimal transmit power for the weak user is
(32) ComputingWherein the content of the first and second substances,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.
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.
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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:whereinIs a matrix of N x N and,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 ofI is 1, 2;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:
B2=(IN+A21Q1)-1A21,
wherein the content of the first and second substances,andare respectively to the matrix B2Performing singular value decompositionThe 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:
wherein σ2Is a noise term that is a function of,the equivalent channel parameters are based on statistical CSI, and the expressions are respectively as follows:
wherein, ImAnd INUnit matrixes of m × m and N × N respectively;
Wherein the content of the first and second substances,is to the matrixAnd (3) carrying out generalized singular value decomposition to obtain a characteristic vector matrix, wherein the specific generalized singular value decomposition form is as follows:
wherein the content of the first and second substances,anda matrix of eigenvectors derived for the generalized singular values,andeigenvalue matrix, matrix D, A, obtained for generalized singular values1,B1All are statistical CSI approximate correlation matrixes, and the expressions are respectively as follows:
B1=(IN+A21Q2)-1A21,
wherein the content of the first and second substances,to Q before optimization1,A22Is an auxiliary variable, and the expression is as follows:
wherein the content of the first and second substances,the equivalent channel parameters are based on the system statistical CSI, and the expressions are respectively as follows:
wherein, InAn identity matrix of n × n; lambdaGSVDIs a diagonal matrix representing the optimal power allocation, whose diagonal elements are:
wherein the content of the first and second substances,are respectively diagonal matrixesDiagonal element of middle, μ1Is that Q1Normalization parameter, v, to meet base station transmit power limitationsiIs a matrixThe diagonal elements of (1);
(2.4) orderRepeating 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
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 isThe optimal transmit power for the weak user is
(4.2) calculation of Wherein the content of the first and second substances,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 (4)
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) and designing an optimal transmission power distribution scheme according to the covariance matrix of the transmission signals.
2. The statistical CSI-based MIMO-NOMA downlink adaptive wireless transmission method according to claim 1, wherein the step (1) is implemented as follows:
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:whereinIs a matrix of N x N and,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 ofI is 1, 2;representing the square root operation of the matrix.
3. The statistical CSI-based MIMO-NOMA downlink adaptive wireless transmission method according to claim 1, wherein 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:
B2=(IN+A21Q1)-1A21,
wherein the content of the first and second substances,andare respectively to the matrix B2Performing singular value decompositionThe 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:
wherein σ2Is a noise term that is a function of,the equivalent channel parameters are based on statistical CSI, and the expressions are respectively as follows:
wherein, ImAnd INUnit matrixes of m × m and N × N respectively;
Wherein the content of the first and second substances,is to the matrixGo on extensivelyThe specific generalized singular value decomposition form of the eigenvector matrix obtained by the nonsingular singular value decomposition is as follows:
wherein the content of the first and second substances,anda matrix of eigenvectors derived for the generalized singular values,andeigenvalue matrix, matrix D, A, obtained for generalized singular values1,B1All are statistical CSI approximate correlation matrixes, and the expressions are respectively as follows:
B1=(IN+A21Q2)-1A21,
wherein the content of the first and second substances,to Q before optimization1,A22Is an auxiliary variable, and the expression is as follows:
wherein the content of the first and second substances,the equivalent channel parameters are based on the system statistical CSI, and the expressions are respectively as follows:
wherein, InAn identity matrix of n × n; lambdaGSVDIs a diagonal matrix representing the optimal power allocation, whose diagonal elements are:
wherein the content of the first and second substances,are respectively diagonal matrixesDiagonal element of middle, μ1Is that Q1Normalization parameter, v, to meet base station transmit power limitationsiIs a matrixThe diagonal elements of (1);
4. The statistical CSI-based MIMO-NOMA downlink adaptive wireless transmission method according to claim 1, wherein the step (3) comprises the steps of:
(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),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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