CN110881010A - Statistical CSI-assisted multi-user NOMA downlink transmission method - Google Patents

Statistical CSI-assisted multi-user NOMA downlink transmission method Download PDF

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CN110881010A
CN110881010A CN201911117516.3A CN201911117516A CN110881010A CN 110881010 A CN110881010 A CN 110881010A CN 201911117516 A CN201911117516 A CN 201911117516A CN 110881010 A CN110881010 A CN 110881010A
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张军
汪东乾
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Nanjing University of Posts and Telecommunications
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    • H04L25/0224—Channel estimation using sounding signals
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
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Abstract

The invention provides a statistical CSI-assisted multi-user NOMA downlink transmission method, which comprises the steps that firstly, a base station estimates non-ideal channel state information by using an uplink pilot signal, and divides users into a plurality of clusters according to the non-ideal channel state information; then, calculating a regularized zero-forcing precoding sending matrix according to the non-ideal channel state information, wherein the calculation of the regularization factor is based on statistical channel state information; and finally, designing an optimal transmission power distribution factor based on the statistical channel state information. The invention maximizes the total transmission rate of all users in the system under the condition of satisfying the rate constraint of weak users in the system, and the regularization factor and the power distribution factor in the system only depend on the statistical channel state information.

Description

Statistical CSI-assisted multi-user NOMA downlink transmission method
Technical Field
The invention relates to a statistical CSI-assisted multi-user NOMA downlink transmission method, and belongs to the technical field of wireless communication.
Background
As mobile communication technology has developed today, the spectrum resources have become increasingly strained. Meanwhile, in order to meet the rapidly increasing demand for mobile services, people have begun to find new mobile communication technologies that can meet the user experience demand and improve spectrum efficiency. Under such a background, a Non-orthogonal multiple access (NOMA) technique has been proposed. The basic idea of non-orthogonal multiple access (NOMA) technology is to adopt non-orthogonal transmission at a transmitting end, actively introduce interference information and realize correct demodulation at a receiving end through a Serial Interference Cancellation (SIC) receiver. Although the complexity of the receiver using SIC technology is increased to some extent, the spectral efficiency can be improved well. By adopting the NOMA technology, a plurality of users can be served at the same time and in the same frequency resource, and the problem that the existing frequency spectrum resource is increasingly tense can be solved. Therefore, the NOMA technique is widely used in communication systems. However, how to perform beam design and power allocation becomes an urgent problem to be solved at present.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a statistical CSI (channel state information) -assisted multi-user NOMA (non-orthogonal multiple access) downlink transmission method, which can improve the total transmission rate of the system and reduce the complexity of the system implementation.
The invention provides a statistical CSI (channel state information) assisted multi-user NOMA (non-orthogonal multiple access) downlink transmission method, a system of the method comprises a multi-antenna base station and a plurality of single-antenna users, and the method comprises the following steps:
s1, in the multi-user NOMA downlink transmission system assisted by statistical channel state information, a base station estimates non-ideal channel state information by using an uplink pilot signal, and divides users into a plurality of clusters according to the non-ideal channel state information; go to step S2;
s2, calculating a regularized zero-forcing precoding sending matrix according to the non-ideal channel state information, wherein the calculation of the regularization factor is based on the statistical channel state information; go to step S3;
and S3, designing an optimal transmission power distribution factor based on the statistical channel state information.
The invention designs a multi-user NOMA downlink transmission method assisted by statistical channel state information by taking the maximization of the total throughput of a cell as a target and considering the fairness of cell users. The method is suitable for a single-cell multi-user NOMA downlink wireless transmission system, the system comprises a multi-antenna base station and a plurality of single-antenna users, and the plurality of users are divided into a plurality of clusters. The method is based on the principle of 'maximizing total rate', and designs the optimal regularized zero-forcing precoding factor and the optimal power distribution factor according to the statistical channel state information.
As a further technical solution of the present invention, the specific method of step S1 is as follows:
s101, assuming that the base station has N antennas, the system has M single-antenna users, the total number of users scheduled by the system is 2K, wherein M > 2K, and 2K > N, then after all users send pilot signals to the base station, the base station estimates the non-ideal channel state information of each user, the non-ideal channel state information of the kth user can be expressed as,
Figure BDA0002274468630000021
wherein k is ∈ [1, M ∈]，
Figure BDA0002274468630000022
Is the estimated channel for user k with a vector size of 1 xn, βkRepresenting the large-scale fading coefficient, τ, of the kth userkRepresents an accuracy parameter of the channel estimation, and τk∈[0，1]，zk、qkEach represents an Nx 1 complex Gaussian random vector whose elements all follow an independent uniform distribution of 0 means and 1/N variance (.)HRepresents a conjugate transpose of a vector; go to step S102;
s102, according to the non-ideal channel state information, clustering all users by adopting the following scheme, wherein the users in the kth cluster need to meet the following conditions,
Corrk≥θk,|βk，1-βk，2|≥βk，0
Figure BDA0002274468630000031
wherein, CorrkIndicating the correlation between user 1 and user 2 channels in the kth cluster, βk，1Representing the large-scale fading coefficient of user 1 in the kth cluster, βk，2Representing the large-scale fading coefficient of user 2 in the kth cluster, βk,0Threshold, θ, representing large scale fading in the kth clusterkA threshold value representing the correlation coefficient in the kth cluster,
Figure BDA0002274468630000032
and
Figure BDA0002274468630000033
is a vector of size 1 x N,
Figure BDA0002274468630000034
indicating the estimated channel for the strong user in the kth cluster,
Figure BDA0002274468630000035
represents the estimated channel of the weak user in the kth cluster,
Figure BDA0002274468630000036
and
Figure BDA0002274468630000037
the meaning is the same, one is in the form of a row and one is in the form of a column.
The specific method for the transmitting end to design the regularized zero-forcing precoding transmission matrix based on the non-ideal channel state information in step S2 is as follows:
s201, suppose
Figure BDA0002274468630000038
And
Figure BDA0002274468630000039
vectors with the size of 1 multiplied by N respectively represent the estimated channels of strong users and weak users in the kth cluster; go to step S202;
s202, let K × N regularized zero-forcing precoding matrix G be expressed as
Figure BDA00022744686300000310
Wherein the content of the first and second substances,
Figure BDA00022744686300000311
represents all strong user non-ideal channel matrices and
Figure BDA00022744686300000312
Figure BDA00022744686300000313
to represent
Figure BDA00022744686300000314
ξ denotes the normalization parameter, α denotes the regularization factor, INRepresents an identity matrix of size N, ()-1Representing matrix inversion; go to step S203;
s203, let ξ be normalized parameters for the base station transmitting power to satisfy the constraint, and the power limit to be satisfied is
tr{GGH}≤NP,P＞0
And can be expressed as
Figure BDA00022744686300000315
Wherein G isHDenotes the conjugate transpose of the K × N regularized zero-forcing precoding matrix G, P denotes the total transmit power of the base station,
Figure BDA0002274468630000041
tr (.) represents matrix tracing.
In the above steps, the regularization factor α is designed based on the total rate maximization principle and only depends on the statistical channel state information, where the statistical channel state information is θk,βk,1,βk,2,τkAnd the like.
The specific method of step S3 is as follows:
setting the optimal transmission power distribution factor of the strong user in the kth cluster as
Figure BDA0002274468630000042
The optimal transmission power distribution factor of the weak user is
Figure BDA0002274468630000043
Then the following formula is adopted to calculate
Figure BDA0002274468630000044
Figure BDA0002274468630000045
Wherein R isk,0Minimum transmission rate, theta, to be satisfied by weak users in the kth clusterkRepresents the correlation between the strong user and the weak user in the kth cluster, and rho is the signal-to-noise ratio
Figure BDA0002274468630000046
(P represents total transmission power of the station,. sigma.)2Representing the noise power),
Figure BDA0002274468630000047
Figure BDA0002274468630000048
τkis a channel estimation parameter andk∈[0,1]α denotes the regularization parameters of the regular zero-forcing precoding,
Figure BDA0002274468630000049
large scale fading factor, e, representing all strong userskRepresenting a scalar quantity to be calculated, η is a K multiplied by 1 vector quantity, theta is a K multiplied by K matrix quantity, go to step S302;
further, the elements in the k-th row and l-th column of the matrix Θ can be calculated as follows,
the elements in matrix theta at row k and column l can be calculated as follows,
Figure BDA00022744686300000410
wherein e iskRepresenting a scalar quantity that needs to be calculated.
Further, the formula for the k element in vector η is as follows:
Figure BDA00022744686300000411
wherein e iskRepresenting a scalar quantity that needs to be calculated.
Further, ekThe calculation can be carried out by a fixed point iteration method, and the specific steps are as follows:
step (a) is initialized first, let t equal to 1, for all
Figure BDA0002274468630000051
(k∈[1,K]) Assigned a value of 1, i.e.
Figure BDA0002274468630000052
Wherein
Figure BDA0002274468630000053
Performing step (b) with the t-th iteration value representing e _ k;
step (b) subjecting the product of step (a) to
Figure BDA0002274468630000054
Substituting the following formula to calculate
Figure BDA0002274468630000055
Figure BDA0002274468630000056
Wherein, βk,1Representing the large-scale fading coefficients of strong users in the kth cluster, and α representing the regularization parameters of the regular zero-forcing precoding;
(c) calculated from the formula of step (b)
Figure BDA0002274468630000057
And further whether it satisfies the following condition or not,
Figure BDA0002274468630000058
wherein epsilon represents a threshold value for judging the convergence degree of the algorithm, if the condition is not satisfied, let t be t +1, and execute step (b) again; if satisfied, the final result can be obtained
Figure BDA0002274468630000059
k＝1,2,......,K。
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) the invention considers the non-ideal channel state information, better fits the practical application scene, and utilizes the regularized zero-forcing pre-coding to carry out pre-coding design, which is superior to the traditional zero-forcing pre-coding;
(2) the invention provides an expression of the optimal power distribution factor based on the statistical channel state information, and can adapt to the rapidly changing channel due to the design based on the statistical channel state information, and has higher practical value compared with the traditional method.
In conclusion, the invention maximizes the total transmission rate of all users in the system under the condition of satisfying the rate constraint of strong users in the system, and the regularization factor and the power distribution factor in the system only depend on the statistical channel state information.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection authority of the present invention is not limited to the following embodiments.
The embodiment provides a statistical channel state information assisted multi-user NOMA downlink transmission method, wherein a system of the method comprises a multi-antenna base station and a plurality of single-antenna users, and the plurality of users are divided into a plurality of clusters; the method comprises the following steps:
s1, in the multi-user NOMA downlink transmission system assisted by the statistical channel state information, the base station estimates the non-ideal channel state information by using the uplink pilot signal, and divides the users into a plurality of clusters according to the non-ideal channel state information. The specific method comprises the following steps:
s101, assuming that the base station has N antennas, the system has M single-antenna users, the total number of users scheduled by the system is 2K, wherein M > 2K, and 2K > N, then after all users send pilot signals to the base station, the base station estimates the non-ideal channel state information of each user, the non-ideal channel state information of the kth user can be expressed as,
Figure BDA0002274468630000061
wherein k is ∈ [1, M ∈]，
Figure BDA0002274468630000062
Is the estimated channel for user k with a vector size of 1 xn, βkRepresenting the large-scale fading coefficient, τ, of the kth userkRepresents an accuracy parameter of the channel estimation, and τk∈[0，1]，zk、qkEach represents an Nx 1 complex Gaussian random vector whose elements all follow an independent uniform distribution of 0 means and 1/N variance (.)HRepresents a conjugate transpose of a vector;
s102, according to the non-ideal channel state information, clustering all users by adopting the following scheme, wherein the users in the kth cluster need to meet the following conditions,
Corrk≥θk,|βk，1-βk，2|≥βk，0
Figure BDA0002274468630000071
wherein, CorrkIndicating the correlation between user 1 and user 2 channels in the kth cluster, βk，1Representing the large-scale fading coefficient of user 1 in the kth cluster, βk，2Representing the large-scale fading coefficient of user 2 in the kth cluster, βk,0Threshold, θ, representing large scale fading in the kth clusterkA threshold value representing the correlation coefficient in the kth cluster,
Figure BDA0002274468630000072
and
Figure BDA0002274468630000073
is a vector of size 1 x N,
Figure BDA0002274468630000074
indicating the estimated channel for the strong user in the kth cluster,
Figure BDA0002274468630000075
indicating the estimated channel of the weak user in the kth cluster.
And S2, calculating a regularized zero-forcing precoding sending matrix according to the non-ideal channel state information, wherein the calculation of the regularization factor is based on the statistical channel state information. The sending end designs a regularized zero-forcing precoding sending matrix based on the non-ideal channel state information. The specific method comprises the following steps:
s201, suppose
Figure BDA0002274468630000076
And
Figure BDA0002274468630000077
vectors with the size of 1 multiplied by N respectively represent the estimated channels of strong users and weak users in the kth cluster; go to step S202;
s202, let K × N regularized zero-forcing precoding matrix G be expressed as
Figure BDA0002274468630000078
Wherein the content of the first and second substances,
Figure BDA0002274468630000079
represents all strong user non-ideal channel matrices and
Figure BDA00022744686300000710
Figure BDA00022744686300000711
to represent
Figure BDA00022744686300000712
ξ denotes the normalization parameter, α denotes the regularization factor, INRepresents an identity matrix of size N, ()-1Representing matrix inversion;
s203, let ξ be normalized parameters for the base station transmitting power to satisfy the constraint, and the power limit to be satisfied is
tr{GGH}≤NP,P＞0
And can be expressed as
Figure BDA0002274468630000081
Wherein G isHDenotes the conjugate transpose of the K × N regularized zero-forcing precoding matrix G, P denotes the total transmit power of the base station,
Figure BDA0002274468630000082
tr (.) represents matrix tracing.
The regularization factor α is designed based on the principle of total rate maximization and depends only on statistical channel state information, where the statistical channel state information is θk,βk,1,βk,2,τkAnd the like.
And S3, designing an optimal transmission power distribution factor based on the statistical channel state information. The specific method comprises the following steps:
setting the optimal transmission power distribution factor of the strong user in the kth cluster as
Figure BDA0002274468630000083
Optimal transmit power allocation for weak usersA factor of
Figure BDA0002274468630000084
Then the following formula is adopted to calculate
Figure BDA0002274468630000085
Figure BDA0002274468630000086
Wherein R isk,0Minimum transmission rate, theta, to be satisfied by weak users in the kth clusterkRepresents the correlation between the strong user and the weak user in the kth cluster, and rho is the signal-to-noise ratio
Figure BDA0002274468630000087
(P represents total transmission power of the station,. sigma.)2Representing the noise power),
Figure BDA0002274468630000088
Figure BDA0002274468630000089
τkis a channel estimation parameter andk∈[0,1]α denotes the regularization parameters of the regular zero-forcing precoding,
Figure BDA00022744686300000810
large scale fading factor, e, representing all strong userskRepresenting a scalar quantity to be calculated, η is a K × 1 vector, Θ is a K × K matrix, the elements in the kth row and the l column of the matrix Θ can be calculated as follows,
Figure BDA00022744686300000811
wherein e iskRepresenting a scalar quantity that needs to be calculated.
The formula for the k element in vector η is as follows:
Figure BDA0002274468630000091
wherein e iskRepresenting a scalar needing to be calculated, and ek can be calculated by a fixed point iteration method, and the specific steps are as follows:
step (a) is initialized first, let t equal to 1, for all
Figure BDA0002274468630000092
(k∈[1,K]) Assigned a value of 1, i.e.
Figure BDA0002274468630000093
Performing step (b);
step (b) subjecting the product of step (a) to
Figure BDA0002274468630000094
Substituting the following formula to calculate
Figure BDA0002274468630000095
Figure BDA0002274468630000096
Wherein, βk,1Representing the large-scale fading coefficients of strong users in the kth cluster, and α representing the regularization parameters of the regular zero-forcing precoding;
(c) calculated from the formula of step (b)
Figure BDA0002274468630000097
And further whether it satisfies the following condition or not,
Figure BDA0002274468630000098
wherein epsilon represents a threshold value for judging the convergence degree of the algorithm, if the condition is not satisfied, let t be t +1, and execute step (b) again; if satisfied, the final result can be obtained
Figure BDA0002274468630000099
k＝1,2,……,K。
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 (7)

1. A statistical CSI-assisted multi-user NOMA downlink transmission method is characterized in that a system of the method comprises a multi-antenna base station and a plurality of single-antenna users; the method comprises the following steps:
s1, the base station estimates the non-ideal channel state information by using the uplink pilot signal, and divides the user into a plurality of clusters according to the non-ideal channel state information; go to step S2;
s2, calculating a regularized zero-forcing precoding sending matrix according to the non-ideal channel state information, wherein the calculation of the regularization factor is based on the statistical channel state information; go to step S3;
and S3, designing an optimal transmission power distribution factor based on the statistical channel state information.
2. The statistical CSI-assisted multiuser NOMA downlink transmission method according to claim 1, wherein the specific method in step S1 is as follows:
s101, assuming that the base station has N antennas, the system has M single-antenna users, the total number of users scheduled by the system is 2K, wherein M > 2K, and 2K > N, then after all users send pilot signals to the base station, the base station estimates the non-ideal channel state information of each user, the non-ideal channel state information of the kth user can be expressed as,
Figure FDA0002274468620000011
wherein k is ∈ [1, M ∈]，
Figure FDA0002274468620000012
Is the estimated channel for user k with a vector size of 1 xn, βkRepresenting the large-scale fading coefficient, τ, of the kth userkRepresents an accuracy parameter of the channel estimation, and τk∈[0，1]，zk、qkEach represents an Nx 1 complex Gaussian random vector whose elements all follow an independent uniform distribution of 0 means and 1/N variance (.)HRepresents a conjugate transpose of a vector; go to step S102;
s102, according to the non-ideal channel state information, all users are clustered, and then the users in the kth cluster need to satisfy the following conditions,
Corrk≥θk,|βk，1-βk，2|≥βk，0
Figure FDA0002274468620000021
wherein, CorrkIndicating the correlation between user 1 and user 2 channels in the kth cluster, βk，1Representing the large-scale fading coefficient of user 1 in the kth cluster, βk，2Representing the large-scale fading coefficient of user 2 in the kth cluster, βk,0Threshold, θ, representing large scale fading in the kth clusterkA threshold value representing the correlation coefficient in the kth cluster,
Figure FDA0002274468620000022
and
Figure FDA0002274468620000023
is a vector of size 1 x N,
Figure FDA0002274468620000024
indicating the estimated channel for the strong user in the kth cluster,
Figure FDA0002274468620000025
indicating the estimated channel of the weak user in the kth cluster.
3. The statistical CSI-assisted multi-user NOMA downlink transmission method according to claim 2, wherein the specific method for the sender to design the regularized zero-forcing precoding transmission matrix based on the non-ideal channel state information in step S2 is as follows:
s201, suppose
Figure FDA0002274468620000026
And
Figure FDA0002274468620000027
vectors with the size of 1 multiplied by N respectively represent the estimated channels of strong users and weak users in the kth cluster; go to step S202;
s202, let K × N regularized zero-forcing precoding matrix G be expressed as
Figure FDA0002274468620000028
Wherein the content of the first and second substances,
Figure FDA0002274468620000029
represents all strong user non-ideal channel matrices and
Figure FDA00022744686200000210
Figure FDA00022744686200000211
to represent
Figure FDA00022744686200000212
ξ denotes the normalization parameter, α denotes the regularization factor, INRepresenting an identity matrix of size N; go to step S203;
s203, let ξ be normalized parameters for the base station transmitting power to satisfy the constraint, and the power limit to be satisfied is
tr{GGH}≤NP,P＞0
And can be expressed as
Figure FDA00022744686200000213
Wherein G isHDenotes the conjugate transpose of the K × N regularized zero-forcing precoding matrix G, P denotes the total transmit power of the base station,
Figure FDA00022744686200000214
tr (.) represents matrix tracing.
4. The statistical CSI-assisted multiuser NOMA downlink transmission method according to claim 3, wherein the specific method in step S3 is as follows:
setting the optimal transmission power distribution factor of the strong user in the kth cluster as
Figure FDA0002274468620000031
The optimal transmission power distribution factor of the weak user is
Figure FDA0002274468620000032
Then the following formula is adopted to calculate
Figure FDA0002274468620000033
Figure FDA0002274468620000034
Wherein R isk,0Minimum transmission rate, theta, to be satisfied by weak users in the kth clusterkRepresents the correlation between the strong user and the weak user in the kth cluster, and rho is the signal-to-noise ratio
Figure FDA0002274468620000035
P represents the total transmission power of the station, σ2Which is indicative of the power of the noise,
Figure FDA0002274468620000036
Figure FDA0002274468620000037
τkis a channel estimation parameter andk∈[0,1]α denotes the regularization parameters of the regular zero-forcing precoding,
Figure FDA0002274468620000038
large scale fading factor, e, representing all strong userskIndicating a scalar quantity to be calculated, η is a K × 1 vector and Θ is a K × K matrix, and go to step S302.
5. The statistical CSI-assisted multiuser NOMA downlink transmission method according to claim 4, wherein the k-th row and l-th column elements in the matrix Θ can be calculated as follows,
Figure FDA0002274468620000039
wherein e iskRepresenting a scalar quantity that needs to be calculated.
6. The statistical CSI-assisted multi-user NOMA downlink transmission method according to claim 5, wherein the calculation formula of the kth element in the vector η is as follows:
Figure FDA00022744686200000310
wherein e iskRepresenting a scalar quantity that needs to be calculated.
7. The statistical CSI-assisted multi-user NOMA downlink transmission method of claim 6, wherein e iskThe calculation can be carried out by a fixed point iteration method, and the specific steps are as follows:
step (a) is initialized first, let t equal to 1, for all
Figure FDA0002274468620000041
Assigned a value of 1, i.e.
Figure FDA0002274468620000042
Performing step (b);
step (b) subjecting the product of step (a) to
Figure FDA0002274468620000043
Substituting the following formula to calculate
Figure FDA0002274468620000044
Figure FDA0002274468620000045
Wherein, βk,1Representing the large-scale fading coefficients of strong users in the kth cluster, and α representing the regularization parameters of the regular zero-forcing precoding;
(c) calculated from the formula of step (b)
Figure FDA0002274468620000046
And further whether it satisfies the following condition or not,
Figure FDA0002274468620000047
wherein epsilon represents a threshold value for judging the convergence degree of the algorithm, if the condition is not satisfied, let t be t +1, and execute step (b) again; if satisfied, the final result can be obtained
Figure FDA0002274468620000048
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Publication number Priority date Publication date Assignee Title
CN111601375A (en) * 2020-04-24 2020-08-28 北京蓝卫通科技有限公司 NOMA transmission method based on 5G system
CN112994762A (en) * 2021-02-04 2021-06-18 南京邮电大学 MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI
CN113014304A (en) * 2021-02-22 2021-06-22 南京邮电大学 Position design method for unmanned aerial vehicle-assisted relay multi-user wireless communication
CN114390658A (en) * 2020-10-22 2022-04-22 王晋良 Power distribution method and base station in non-orthogonal multiple access system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101517922A (en) * 2006-09-15 2009-08-26 华为技术有限公司 Beamforming with imperfect channel state information
CN104640220A (en) * 2015-03-12 2015-05-20 重庆邮电大学 Frequency and power distributing method based on NOMA (non-orthogonal multiple access) system
CN108882352A (en) * 2018-05-29 2018-11-23 南京邮电大学 A kind of downlink NOMA power distribution method that the weight rate based on non-perfect SIC is optimal
CN109005551A (en) * 2018-07-10 2018-12-14 南京邮电大学 A kind of multi-user's NOMA downlink power distributing method of imperfect channel state information
CN109661026A (en) * 2018-12-06 2019-04-19 南京邮电大学 A kind of D2D alternative routing system power distribution method based on NOMA
CN109905917A (en) * 2019-01-23 2019-06-18 南京邮电大学 Based on wireless resource allocation methods in the NOMA communication system for wirelessly taking energy

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101517922A (en) * 2006-09-15 2009-08-26 华为技术有限公司 Beamforming with imperfect channel state information
CN104640220A (en) * 2015-03-12 2015-05-20 重庆邮电大学 Frequency and power distributing method based on NOMA (non-orthogonal multiple access) system
CN108882352A (en) * 2018-05-29 2018-11-23 南京邮电大学 A kind of downlink NOMA power distribution method that the weight rate based on non-perfect SIC is optimal
CN109005551A (en) * 2018-07-10 2018-12-14 南京邮电大学 A kind of multi-user's NOMA downlink power distributing method of imperfect channel state information
CN109661026A (en) * 2018-12-06 2019-04-19 南京邮电大学 A kind of D2D alternative routing system power distribution method based on NOMA
CN109905917A (en) * 2019-01-23 2019-06-18 南京邮电大学 Based on wireless resource allocation methods in the NOMA communication system for wirelessly taking energy

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JUN ZHANG: "An Optimal Power Allocation Scheme in Downlink Multi-user NOMA Beamforming System with Imperfect CSI", 《IEEE》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111601375A (en) * 2020-04-24 2020-08-28 北京蓝卫通科技有限公司 NOMA transmission method based on 5G system
CN111601375B (en) * 2020-04-24 2024-05-03 北京蓝卫通科技有限公司 NOMA transmission method based on 5G system
CN114390658A (en) * 2020-10-22 2022-04-22 王晋良 Power distribution method and base station in non-orthogonal multiple access system
CN112994762A (en) * 2021-02-04 2021-06-18 南京邮电大学 MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI
CN112994762B (en) * 2021-02-04 2022-04-26 南京邮电大学 MIMO-NOMA downlink self-adaptive wireless transmission method based on statistical CSI
CN113014304A (en) * 2021-02-22 2021-06-22 南京邮电大学 Position design method for unmanned aerial vehicle-assisted relay multi-user wireless communication

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