CN108832977B - Large-scale MIMO space domain sparse non-orthogonal access realization method - Google Patents

Large-scale MIMO space domain sparse non-orthogonal access realization method Download PDF

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CN108832977B
CN108832977B CN201810821384.1A CN201810821384A CN108832977B CN 108832977 B CN108832977 B CN 108832977B CN 201810821384 A CN201810821384 A CN 201810821384A CN 108832977 B CN108832977 B CN 108832977B
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CN108832977A (en
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张顺
陈春龙
李红艳
邵卫东
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Xidian University
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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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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

Abstract

The invention belongs to the technical field of wireless communication, and discloses a large-scale MIMO space domain sparse non-orthogonal access realization method. The base station antennas are uniformly distributed, and the antennas are mapped into clusters of an angle domain according to different spread angles; acquiring an angle domain channel matrix from a base station to a user; constructing a space sparse code division matrix, determining a power distribution matrix, and sending data; the user signal in a certain beam in the angle domain can be obtained by adopting a ZF zero forcing method and an MPA message passing combined algorithm. The invention greatly compresses the high-dimensional characteristic of a large-scale antenna array by utilizing the characteristic of an angle domain channel, improves the number of service users by utilizing the sparse code division multiple access technology, reduces the transmission interference among multiple clusters by power adjustment and improves the system capacity. The invention utilizes the characteristic information of the angle domain channel in a large-scale MIMO system to reduce the feedback overhead of the channel; and a space sparse code division multiplexing technology is adopted, the service connection quantity is improved, and a solution idea is provided for the access of a future intensive scene.

Description

Large-scale MIMO space domain sparse non-orthogonal access realization method
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a large-scale MIMO space domain sparse non-orthogonal access implementation method.
Background
Currently, the current state of the art commonly used in the industry is such that:
the large-scale antenna array is based on a multi-user beam forming principle, and realizes that data can be transmitted for a plurality of users simultaneously on the same frequency resource by arranging hundreds of antennas at a base station. The mining of the space resources can fully utilize the limited frequency band resources, and brings great improvement of network capacity. Therefore, the large-scale antenna array has great application potential in the aspects of improving energy efficiency and spectrum utilization rate as a key alternative technology of the next generation 5G communication system. Meanwhile, how to accurately and efficiently acquire Channel State Information (CSI) in a wireless communication system based on a large-scale antenna array is a difficult problem to be solved urgently. In the case of Time Division Duplex (TDD) systems, Channel State Information (CSI) may be obtained through uplink channel training based on the correlation of the uplink and downlink. Since the uplink and downlink channels in a Frequency Division Duplex (FDD) system no longer satisfy the correlation, the downlink channel training is adopted to obtain Channel State Information (CSI), and the user estimates the downlink channel and feeds back the CSI to the base station in an uplink manner. However, the overhead of channel training and CSI feedback is proportional to the antenna scale in the system, and therefore, in a large-scale antenna system, the above method for acquiring channel state information brings huge overhead. To solve this problem, a two-stage precoding scheme is developed, which mainly includes the following: (1) all users in the system are clustered according to different spread angles, and the users in the same cluster can be approximately considered to have the same channel covariance matrix. The covariance matrices from cluster to cluster are independent of each other and occupy different angular beam spaces. Based on the clustering idea, firstly, a pre-beam forming scheme is adopted in the first stage to eliminate the interference between different clusters. In order to further eliminate the intra-cluster interference, the second stage adopts the precoding technology in the traditional MU-MIMO system to distinguish different users, and eliminates the interference between the intra-cluster sub-beams. Large connections, quality of service (QoS) guarantees, high throughput, low latency are the basic requirements of future 5G communication standards. This also means that in a large-scale antenna array communication system, many users are densely distributed within the same cluster and there are a large number of different access requests.
In summary, the problems of the prior art are as follows:
the massive MIMO transmission system reduces channel feedback overhead by converting massive MIMO high dimensional channels into angular domain low dimensional channel characterizations. On one hand, a large number of methods use the low latitude angle domain channel as a reference, and adopt the traditional multi-user MIMO technology such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) to realize the orthogonal transmission of multiple users, namely, the number of users of the same frequency service is equal to the space dimension of the low latitude angle domain channel, the method directly moves the strategy of the traditional multi-antenna service users in the design, in the future ultra-dense network deployment, a large number of users are concentrated in the specific position of a cell, under the scene, the orthogonal transmission scheme is limited by the space dimension of the low latitude angle domain, is difficult to serve a large number of users, and limits the promotion of the network capacity. On the other hand, some methods try to break the bottleneck of the above orthogonal transmission scheme, introduce a non-orthogonal transmission strategy idea, and try to adopt a novel non-orthogonal transmission strategy such as sparse code division multiple access (SCMA) and mode division multiplexing (PDMA) to serve users in massive MIMO. At present, for the application of the non-orthogonal scheme in the large-scale MIMO, the existing method only adopts a random matrix theory and the like to deduce SINR, and obtains theoretical indexes such as system and rate, system capacity and the like, does not consider the effective conversion of the large-scale MIMO high-dimensional channel, does not consider the practical and effective implementation scheme of the non-orthogonal transmission strategy in the large-scale MIMO system, obtains the theoretical index analysis only based on ideal conditions, and lacks an effective and low-complexity non-orthogonal specific implementation scheme.
The difficulty and significance for solving the technical problems are as follows:
the difficulty of the large-scale MIMO space domain sparse non-orthogonal access realization method is represented by the non-orthogonal mapping of a transmitting end: obtaining an optimal non-orthogonal mapping matrix, obtaining an optimal power distribution scheme and designing a non-orthogonal demodulation algorithm of a receiving end. A non-orthogonal transmission mechanism based on a large-scale MIMO system is designed, so that overload of space-time-frequency resources can be realized, the resource utilization rate is effectively improved, the number of service users is increased, and a solution is provided for access of a future ultra-dense scene.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a large-scale MIMO space domain sparse non-orthogonal access realization method.
The large-scale MIMO airspace sparse non-orthogonal access implementation method is based on a large-scale MIMO uniform linear array channel model, firstly, low-latitude angle domain conversion is carried out on a high-dimensional channel, user clustering is completed through the spatial characteristics of the low-latitude angle domain channel, a non-orthogonal mapping matrix of a sending end is designed by taking the space limited channel dimension of a user cluster as the reference, a non-orthogonal demodulation algorithm is designed for a user at a receiving end by combining a zero forcing method (ZF) and a message transfer algorithm (MPA), and a non-orthogonal optimal transmission strategy of a large-scale MIMO system is obtained based on the non-orthogonal demodulation algorithm.
Further, the implementation method of the large-scale MIMO spatial domain sparse non-orthogonal access comprises the following steps: in a large-scale MIMO system, antennas are uniformly distributed in a base station, and the antennas are mapped into clusters of an angle domain according to different spread angles; acquiring an angle domain channel matrix from a base station to a user; constructing a space sparse code division matrix, determining a power distribution matrix, and sending data; the user signal in a certain beam in the angle domain can be obtained by adopting a ZF zero forcing method and an MPA message passing combined algorithm.
Further, the method for realizing the large-scale MIMO space domain sparse non-orthogonal access comprises the following steps:
the method comprises the following steps: in a large-scale MIMO system, a base station has M uniformly distributed antennas, and the antennas are spread according to different spread angles delta thetaj(J ═ 1, 2.. said., J) maps to a cluster of J angular domains, where the J-th angular domain
Figure BDA0001741479120000031
ljDenotes the radius of the jth cluster, DjRepresents the distance from the cluster center to the base station;
step two: the j-th angular domain uses a beam set of
Figure BDA0001741479120000032
The user set of the service is KjEach user is equipped with N antennas; wherein the base station goes from the jth angle domain to the KthjThe angle domain channel matrix of each user is
Figure BDA0001741479120000033
GjFrom the channel matrix H of the j-th angular domainjObtaining the signal by DFT conversion;
step three: construction of a spatial sparse code division matrix FjDetermining a power distribution matrix PjConcurrent with each otherSending data; transmitting a signal
Figure BDA0001741479120000034
Wherein
Figure BDA0001741479120000035
Is user data, FjIs a sparse coding mapping matrix, PjRepresents the power allocated by the jth angle domain;
step four: receive a signal of
Figure BDA0001741479120000036
Wherein n isjTo comply with
Figure BDA0001741479120000037
The additive white Gaussian noise is obtained at the receiving end by adopting a zero forcing method,
Figure BDA0001741479120000041
wherein the zero forcing matrix
Figure BDA0001741479120000042
Is a normalization factor; definition of [ s ]j]n，k＝sj，n，kFor transmitting code words, [ F ]j]n，k＝fj，n，kA factor is selected for the beam or beams,
Figure BDA0001741479120000043
a factor is allocated to the power, so that the nth beam signal is
Figure BDA0001741479120000044
gj，n，kIs Gj，kThe nth column of (1); the signal of the kth user which obtains the nth beam signal in the jth angular domain by adopting an MPA message transfer algorithm is as follows:
Figure BDA0001741479120000045
further, the construction of the space sparse code division matrix FjThe method comprises the following steps:
(1) according to KjAnd dfInitialization
Figure BDA0001741479120000046
And dv。KjIs a service user who is a subscriber to the service,
Figure BDA0001741479120000047
is a set of beams, dfIs the maximum number of service users of a single beam, dvIs the maximum number of occupied beams by a single user;
(2) the base station transmits training frames with equal power on the wave beam occupied by each user, the users feed back to the base station after receiving the training frames, and the base station bases on the training frames
Figure BDA0001741479120000048
Estimating to obtain the signal-to-noise ratio zeta of the user under the sending powerj，n，k. In which inter-cluster interference
Figure BDA0001741479120000049
Inter-cluster interference
Figure BDA00017414791200000410
(3) If it is not
Figure BDA00017414791200000411
And is
Figure BDA00017414791200000412
The user k with the largest signal-to-noise ratio is selected for beam n.
Figure BDA00017414791200000413
Is the number of currently allocated users for beam n,
Figure BDA00017414791200000414
is the number of beams currently occupied by user k;
(4) setting F in corresponding sparse code division multiplexing matrixn，k＝1；
(5) When a single beam is onThe number of service users is more than dfThen, the beam is not available to other subsequent users; when the single user occupies the wave beam more than dvThen, the beam can not be distributed to the user any more so as to keep the sparsity of the matrix;
(6) repeating (3) and (4) until there are no usable beams or no users to be allocated.
Further, the determining a power allocation matrix PjThe method comprises the following steps:
(1) initializing single cluster power P ═ P0The iteration time t is 0, the power iteration step delta and the tolerance factor epsilon;
(2) according to the formula
Figure BDA0001741479120000051
Computing cluster juOf which wherein
Figure BDA0001741479120000052
α, β, γ are lagrange multipliers under KKT conditions;
(3) fixed cluster juIs constant at PsumUnder the condition of-P, calculating the cluster j from (2)dAllocate power and fix cluster jdThe power of (d);
(4) by the formula
Figure BDA0001741479120000053
Calculating the signal-to-noise ratio of users in different clusters; wherein
Figure BDA0001741479120000054
If the difference of the minimum user signal-to-noise ratios in each cluster is smaller than the tolerance factor epsilon, the power distribution based on the fairness is completed, and the power distribution is finished; otherwise, P ═ P0And + delta, t is t +1, and the process of (3) is continued until the iteration number is exceeded.
The invention also aims to provide a wireless communication system applying the large-scale MIMO space domain sparse non-orthogonal access implementation method.
In summary, the advantages and positive effects of the invention are:
based on a large-scale MIMO uniform linear array channel model, firstly, low-latitude angle domain conversion is carried out on a high-dimensional channel, user clustering is completed through the spatial characteristics of the low-latitude angle domain channel, and channel feedback overhead and inter-cluster interference are reduced; a non-orthogonal mapping matrix of a sending end is designed by taking the limited channel dimension of a user cluster space as a reference, a non-orthogonal demodulation algorithm is designed for a user at a receiving end by combining a zero forcing method (ZF) and a Message Passing Algorithm (MPA), and a non-orthogonal optimal transmission strategy of a large-scale MIMO system is obtained on the basis, so that the service connection quantity is effectively increased, and a solution idea is provided for future dense scene access.
Drawings
Fig. 1 is a flowchart of a large-scale MIMO spatial domain sparse non-orthogonal access implementation method provided by an embodiment of the present invention.
Fig. 2 is a schematic view of an application scenario of a large-scale MIMO spatial domain sparse non-orthogonal access implementation method provided by an embodiment of the present invention.
Fig. 3 is a schematic diagram of an embodiment provided by an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention particularly relates to a large-scale MIMO space domain sparse non-orthogonal access implementation method which can be applied to a large-scale MIMO wireless network, eliminates inter-cluster interference, meets the requirement of intensive multi-user access and improves the overall performance of a system.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the method for implementing large-scale MIMO spatial domain sparse non-orthogonal access provided by the embodiment of the present invention includes the following steps:
s101: in a massive MIMO system, the base station: the antennas are uniformly distributed, and the antennas are mapped into clusters of an angle domain according to different spread angles;
s102: acquiring an angle domain channel matrix from a base station to a user;
s103: constructing a space sparse code division matrix, determining a power distribution matrix, and sending data;
s104: the user signal in a certain beam in the angle domain can be obtained by adopting a ZF zero forcing method and an MPA message passing combined algorithm. .
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
The implementation method of the large-scale MIMO space domain sparse non-orthogonal access provided by the embodiment of the invention specifically comprises the following steps:
the method comprises the following steps: in a large-scale MIMO system, a base station has M uniformly distributed antennas, and the antennas are spread according to different spread angles delta thetaj(J ═ 1, 2.. said., J) maps to a cluster of J angular domains, where the J-th angular domain
Figure BDA0001741479120000071
ljDenotes the radius of the jth cluster, DjIndicating the distance from the cluster center to the base station.
Step two: the j-th angular domain uses a beam set of
Figure BDA0001741479120000072
The user set of the service is KjEach user is equipped with N antennas. Wherein the base station goes from the jth angle domain to the KthjThe angle domain channel matrix of each user is
Figure BDA0001741479120000073
GjFrom the channel matrix H of the j-th angular domainjAnd obtaining the target through DFT transformation.
Step three: construction of a spatial sparse code division matrix F Using Algorithm 1jDetermining the power distribution matrix P using Algorithm 2jAnd transmits the data. Transmitting a signal
Figure BDA0001741479120000074
Wherein
Figure BDA0001741479120000075
Is user data, FjIs a sparse coding mapping matrix that is,Pjrepresenting the power allocated for the j-th angular domain.
Step four: receive a signal of
Figure BDA0001741479120000076
Wherein n isjTo comply with
Figure BDA0001741479120000077
The additive white Gaussian noise is obtained at the receiving end by adopting a zero forcing method,
Figure BDA0001741479120000078
wherein the zero forcing matrix
Figure BDA0001741479120000079
Is a normalization factor. Definition of [ s ]j]n，k＝sj，n，kFor transmitting code words, [ F ]j]n，k＝fj，n，kA factor is selected for the beam or beams,
Figure BDA00017414791200000710
a factor is allocated to the power, so that the nth beam signal is
Figure BDA00017414791200000711
gj，n，kIs Gj，kColumn n. The signal of the kth user, which can obtain the nth beam signal in the jth angular domain by adopting the ZF and MPA message passing algorithms, is:
Figure BDA00017414791200000712
in a preferred embodiment of the invention: algorithm 1 (equal power distribution beam selection)
1. According to KjAnd dfInitialization
Figure BDA00017414791200000713
And dv。KjIs a service user who is a subscriber to the service,
Figure BDA00017414791200000714
is a set of beams, dfIs the maximum number of service users of a single beam, dvIs the maximum number of occupied beams for a single user.
2. The base station transmits training frames with equal power on the wave beam occupied by each user, the users feed back to the base station after receiving the training frames, and the base station bases on the training frames
Figure BDA00017414791200000715
Estimating to obtain the signal-to-noise ratio zeta of the user under the sending powerj，n，k. In which inter-cluster interference
Figure BDA0001741479120000081
Inter-cluster interference
Figure BDA0001741479120000082
3. If it is not
Figure BDA0001741479120000083
And is
Figure BDA0001741479120000084
The user k with the largest signal-to-noise ratio is selected for beam n.
Figure BDA0001741479120000085
Is the number of currently allocated users for beam n,
Figure BDA00017414791200000810
is the number of beams currently occupied by user k.
4. Setting F in corresponding sparse code division multiplexing matrixn，k＝1。
5. When the number of service users on a single beam is larger than dfThen, the beam is not available to other subsequent users; when the single user occupies the wave beam more than dvThen, no more beams can be allocated to this user to maintain the sparsity of the matrix.
6. Repeating steps 3 and 4 until there are no usable beams or no users to be allocated.
In a preferred embodiment of the invention: algorithm 2 (heuristic Power distribution Algorithm)
1. Initializing single cluster power P ═ P0The iteration time t is 0, the power iteration step delta and the tolerance factor epsilon.
2. According to the formula
Figure BDA0001741479120000086
Computing cluster juOf which wherein
Figure BDA0001741479120000087
α, β, γ are lagrange multipliers under KKT conditions.
3. Fixed cluster juIs constant at PsumUnder the condition of-P, calculating the cluster j from step 2dAllocate power and fix cluster jdOf the power of (c).
4. By the formula
Figure BDA0001741479120000088
The signal-to-noise ratios of the users in different clusters are calculated. Wherein
Figure BDA0001741479120000089
And if the difference of the minimum user signal-to-noise ratios in each cluster is smaller than the tolerance factor epsilon, the power allocation based on the fairness is considered to be completed, and the power allocation is finished. Otherwise, P ═ P0And + δ, t being t +1, the process of step 3 is continued until the number of iterations is exceeded.
The following will describe the effects of the present invention in detail.
As shown in fig. 3, the present invention is described in detail: there are 6 users served in a certain angle domain, there are 4 usable beams, the maximum number of service users of a single beam is 3, and the maximum number of occupied beams of a single user is 2.
Step 1, obtaining an angle domain channel matrix G
The base station uses the feedback of the uplink channel to obtain a channel matrix H from the base station to the user, the H is converted by DFT to obtain G,
Figure BDA0001741479120000091
step 2, calculating a sparse multiplexing matrix F
And (3) selecting equal-power distribution beams by adopting an algorithm 1. The base station transmits training frames with equal power on the wave beam occupied by each user, the users feed back to the base station after receiving the training frames, and the base station bases on the training frames
Figure BDA0001741479120000092
j＝ju，jdEstimating to obtain the signal-to-noise ratio zeta of the user under the sending power1，1ζ1，2ζ1，3...ζ4，6Selecting the maximum value as zeta1，1Will F1，1Is set to 1. Repeating the above process to obtain the sparse code division multiplexing matrix under the 4 wave beams of the 6 users as follows:
Figure BDA0001741479120000093
step 3 determining a power distribution matrix P
Assuming that the total power of the system is 1, determining a power distribution matrix P by adopting an algorithm 2, and transmitting data
Figure BDA0001741479120000094
Step 4, adopting ZF and MPA algorithm to mix and demodulate user data at receiving end
Obtaining a signal in a single beam by adopting a ZF zero forcing method, and obtaining a code word of a single user from the single beam by adopting an MPA message transfer algorithm:
rn，k＝zn，kgn，kdn，k。
the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (2)

1. The large-scale MIMO airspace sparse non-orthogonal access realization method is characterized in that the large-scale MIMO airspace sparse non-orthogonal access realization method is based on a large-scale MIMO uniform linear array channel model, firstly, low latitude angle domain conversion is carried out on a high-dimensional channel, user clustering is completed through the low latitude angle domain channel space characteristic, a non-orthogonal mapping matrix of a sending end is designed by taking the limited channel dimension of the user cluster space as the reference, a non-orthogonal demodulation algorithm is designed for users at a receiving end by combining a zero forcing method (ZF) and a message transfer algorithm (MPA), and a non-orthogonal optimal transmission strategy of a large-scale MIMO system is obtained based on the non-orthogonal demodulation algorithm;
the large-scale MIMO space domain sparse non-orthogonal access realization method comprises the following steps:
in a large-scale MIMO system, base station antennas are uniformly distributed, and the antennas are mapped into clusters of an angle domain according to different spread angles; acquiring an angle domain channel matrix from a base station to a user; constructing a sparse coding mapping matrix, determining a power distribution matrix, and sending data; a ZF zero forcing method and an MPA message transmission combined algorithm are adopted to obtain a user signal in a certain beam in an angle domain;
the large-scale MIMO space domain sparse non-orthogonal access realization method comprises the following steps:
the method comprises the following steps: in a large-scale MIMO system, a base station has M uniformly distributed antennas, and the antennas are spread according to different spread angles delta thetaj(J ═ 1, 2.. said., J) maps to a cluster of J angular domains, where the J-th angular domain
Figure FDA0003125814990000011
ljDenotes the radius of the jth cluster, DjRepresents the distance from the cluster center to the base station;
step two: the j-th angular domain uses a beam set of
Figure FDA0003125814990000012
The user set of the service is KjEach user is equipped with N antennas; wherein the base station goes from the jth angle domain to the KthjThe angle domain channel matrix of each user is
Figure FDA0003125814990000013
GjFrom the channel matrix H of the j-th angular domainjObtaining the signal by DFT conversion;
step three: construction of sparse coding mapping matrix FjDetermining a power distribution matrix PjAnd transmitting the data; transmitting a signal
Figure FDA0003125814990000014
Wherein
Figure FDA0003125814990000015
Is user data, FjIs a sparse coding mapping matrix, PjRepresents the power allocated by the jth angle domain;
step four: receive a signal of
Figure FDA0003125814990000021
Wherein n isjTo comply with
Figure FDA0003125814990000022
The additive white Gaussian noise is obtained at the receiving end by adopting a zero forcing method,
Figure FDA0003125814990000023
wherein the zero forcing matrix
Figure FDA0003125814990000024
Figure FDA0003125814990000025
Is a normalization factor; definition of [ s ]j]n,k＝sj,n,kFor transmitting code words, [ F ]j]n,k＝fj,n,kA factor is selected for the beam or beams,
Figure FDA0003125814990000026
a factor is allocated to the power, so that the nth beam signal is
Figure FDA0003125814990000027
gj,n,kIs Gj,kThe nth column of (1); the signal of the kth user which obtains the nth beam signal in the jth angular domain by adopting an MPA message transfer algorithm is as follows:
Figure FDA0003125814990000028
the construction of the sparse coding mapping matrix FjThe method comprises the following steps:
(1) according to KjAnd dfInitialization
Figure FDA0003125814990000029
And dv；KjIs a service user who is a subscriber to the service,
Figure FDA00031258149900000210
is a set of beams, dfIs the maximum number of service users of a single beam, dvIs the maximum number of occupied beams by a single user;
(2) the base station transmits training frames with equal power on the wave beam occupied by each user, the users feed back to the base station after receiving the training frames, and the base station bases on the training frames
Figure FDA00031258149900000211
j＝ju,jdEstimating to obtain the signal-to-noise ratio zeta of the user under the sending powerj,n,k(ii) a In which inter-cluster interference
Figure FDA00031258149900000212
Inter-cluster interference
Figure FDA00031258149900000213
(3) If it is not
Figure FDA00031258149900000214
And is
Figure FDA00031258149900000215
Selecting a user k with the largest signal-to-noise ratio for the beam n;
Figure FDA00031258149900000216
is the number of currently allocated users for beam n,
Figure FDA00031258149900000217
is the number of beams currently occupied by user k;
(4) setting F in corresponding sparse code division multiplexing matrixn,k＝1；
(5) When the number of service users on a single beam is larger than dfThen, the beam is not available to other subsequent users; when the single user occupies the wave beam more than dvThen, the beam can not be distributed to the user any more so as to keep the sparsity of the matrix;
(6) repeating (3) and (4) until there are no usable beams or no users to be allocated;
said determining the power distribution matrix PjThe method comprises the following steps:
(1) initializing single cluster power P ═ P0The iteration time t is 0, the power iteration step delta and the tolerance factor epsilon;
(2) according to the formula
Figure FDA0003125814990000031
Computing cluster juOf which wherein
Figure FDA0003125814990000032
α, β, γ are lagrange multipliers under KKT conditions;
(3) fixed cluster juIs constant at PsumUnder the condition of-P, calculating the cluster j from (2)dAllocate power and fix cluster jdThe power of (d);
(4) by the formula
Figure FDA0003125814990000033
Calculating the signal-to-noise ratio of users in different clusters; wherein
Figure FDA0003125814990000034
If the difference of the minimum user signal-to-noise ratios in each cluster is smaller than the tolerance factor epsilon, the power distribution based on the fairness is completed, and the power distribution is finished; otherwise, P ═ P0And + delta, t is t +1, and the process of (3) is continued until the iteration number is exceeded.
2. A wireless communication system applying the massive MIMO spatial domain sparse non-orthogonal access implementation method of claim 1.
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