CN108832977A - The sparse nonopiate access implementing method in the extensive airspace MIMO - Google Patents
The sparse nonopiate access implementing method in the extensive airspace MIMO Download PDFInfo
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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
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- 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
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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
Abstract
The invention belongs to wireless communication technology fields, disclose the sparse nonopiate access implementing method in the extensive airspace MIMO.Antenna for base station is uniformly distributed, and antenna is mapped as the cluster of angle domain by different extended corners；Angle domain channel matrix of the acquisition base station to user；Space Sparse Code sub-matrix is constructed, determines power distribution matrix, and send data；Using the subscriber signal in a certain wave beam in ZF force zero method and the available angle domain of MPA message transmission unified algorithm.The present invention substantially compresses the high-dimensional characteristic of large-scale antenna array using the angle domain characteristic of channel, promotes number of service subscribers using sparse Code Division Multiple Access, reduces transmission interference between more clusters, lifting system capacity by power adjustment.The present invention utilizes angle domain channel characteristic information in extensive mimo system, reduces channel feedback expense；Divide multiplexing technology using space Sparse Code, promote service connection quantity, provides resolving ideas for the following intensive scene access.
Description
Technical field
The invention belongs to wireless communication technology fields, more particularly to the extensive sparse nonopiate access realization side in the airspace MIMO
Method.
Background technique
Currently, the prior art commonly used in the trade is such：
Large-scale antenna array is based on multi-user beam-forming principle, by realizing in several hundred antennas of arrangement of base stations same
Data are transmitted for multiple users simultaneously in one frequency resource.This excavation to space resources, can make full use of limited frequency
Band resource, brings the tremendous increase of network capacity.Therefore, large-scale antenna array is standby as the key of next generation's 5G communication system
Selecting technology has huge application potential in terms of improving energy efficiency and the availability of frequency spectrum.At the same time, it is being based on extensive antenna
In the wireless communication system of array, how the acquisition channel state information (CSI) of precise and high efficiency is problem urgently to be resolved.With regard to when
Divide for multiplexing (TDD) system, the correlation based on uplink downlink, channel status letter can be obtained by up channel training
It ceases (CSI).Since uplink and downlink channel no longer meets correlation in frequency division multiplexing (FDD) system, using down channel training
It obtains channel state information (CSI), user estimates down channel and by CSI uplink feedback to base station.But channel training and CSI
Antenna scale is proportional in the expense and system of feedback, therefore in extensive antenna system, above-mentioned acquisition channel state information
Method bring great expense incurred.In order to solve this problem, two stages pre-coding scheme comes into being, and mainly includes in following
Hold：(1) sub-clustering is carried out according to different extended corners to users all in system, can be approximately considered with the user in cluster with phase
Same channel covariance matrices.Covariance matrix between cluster and cluster is mutually indepedent and occupies the beam space of different angle.
(2) it is based on above-mentioned sub-clustering thought, the first stage first uses switched-beam scheme to eliminate the interference between different clusters.In order to
Interference in cluster is further eliminated, second stage distinguishes different user using the precoding technique in traditional MU-MIMO system, disappears
Except the interference in cluster between beamlet.Big connection, service quality (QoS) guarantee, high-throughput, low time delay are the following 5G communication marks
Quasi- basic demand.This also means that in large-scale antenna array communication system, with many users of dense distribution in cluster,
And there are a large amount of different access requests.
In conclusion problem of the existing technology is：
Extensive mimo transmission system is by being converted to angle domain low dimensional channel characterization for extensive MIMO higher-dimension channel
To reduce channel feedback expense.On the one hand, a large amount of methods are on the basis of this low latitudes angle domain channel, using such as force zero
(ZF) and the conventional multi-user MIMO technology of least mean-square error (MMSE) realizes the orthogonal transmission of multi-user, i.e., simultaneously with frequency
The number of users of service is equal to low latitudes angle domain channel space dimension, and such method directly moves traditional multiple antennas clothes in the design
The strategy of business user, in the deployment of the following super-intensive network, a large number of users concentrates on the specific position of cell, in this scenario,
Orthogonal transmission schemes are limited to low latitudes angle domain space dimensionality, it is difficult to service a large number of users, limit the promotion of network capacity.
On the other hand, certain methods are attempted to break the bottleneck of above-mentioned Orthogonal transmission schemes, introduce non orthogonal transmissions strategic thinking, it is intended to
The novel non orthogonal transmissions strategy such as sparse CDMA (SCMA) and mode division multiplexing (PDMA) is used in extensive MIMO
To service user.Application for this non-orthogonal scheme in extensive MIMO at present, existing method is only with random matrix
Theory etc. derives SINR, and obtains system and rate, power system capacity scheduling theory index, does not consider extensive MIMO higher-dimension channel
Effectively conversion, and do not consider such non orthogonal transmissions strategy implementation effective in extensive mimo system, only
It is based only upon ideal conditions and show that theory index is analyzed, lack effective, low complex degree nonopiate specific embodiment.
Solve the difficulty and meaning of above-mentioned technical problem：
The sparse nonopiate access implementing method difficult point in the extensive airspace MIMO is the nonopiate mapping characterization of transmitting terminal：It is optimal
Nonopiate mapping matrix obtains, optimal power allocation scheme obtains and the nonopiate demodulating algorithm design in receiving end.Design is based on
The overload of sky running time-frequency resource, effectively promotion resource utilization may be implemented in the non orthogonal transmissions mechanism of extensive mimo system, increases
Business number of users is added, provides resolving ideas for the following super-intensive scene access.
Summary of the invention
In view of the problems of the existing technology, the present invention provides the sparse nonopiate access realization sides in the extensive airspace MIMO
Method.
The sparse nonopiate access implementing method in the extensive airspace MIMO is believed based on extensive MIMO uniform linear array
Road model carries out the conversion of low latitudes angle domain to higher-dimension channel first, completes to use by low latitudes angle domain channel space characteristic
Family sub-clustering designs the nonopiate mapping matrix of transmitting terminal on the basis of the limited channel dimension of user's cluster space, combines force zero method (ZF)
Nonopiate demodulating algorithm is designed for user in receiving end with Message Passing Algorithm (MPA), extensive mimo system is obtained based on this
Nonopiate optimal transmission strategy.
Further, the sparse nonopiate access implementing method in the extensive airspace MIMO includes：In extensive mimo system
In, the equally distributed antenna in base station, antenna is mapped as the cluster of angle domain by different extended corners；Angle of the acquisition base station to user
Domain channel matrix；Space Sparse Code sub-matrix is constructed, determines power distribution matrix, and send data；Using ZF force zero method and MPA
Subscriber signal in the available angle domain of message transmission unified algorithm in a certain wave beam.
Further, the sparse nonopiate access implementing method in the extensive airspace MIMO includes the following steps：
Step 1：In extensive mimo system, there is the equally distributed antenna of M root in base station, and antenna presses different extended corners
Δθj(j=1,2 ..., J) is mapped as the cluster of J angle domain, wherein j-th of angle domainljIndicate jth
The radius of a cluster, DjDistance of the expression cluster center to base station；
Step 2：The beam set that j-th of angle domain uses isUser's collection of service is combined into Kj, each user assembled
There is N root antenna；Wherein in j-th of angle domain base station to KjThe angle domain channel matrix of a user isGjBy the channel matrix H of j-th of angle domainjIt is obtained through DFT transform；
Step 3：Construct space Sparse Code sub-matrix Fj, determine power distribution matrix Pj, and send data；Send signalWhereinIt is user data, FjIt is sparse coding mapping matrix, PjIt represents j-th
The power of angle domain distribution；
Step 4：Receiving signal isWherein njTo obeyAdditive white Gaussian noise, obtained in receiving end using force zero method,Wherein compel null matrixFor normalization factor；Define [sj]N, k=sJ, n, kFor transmission code
Word, [Fj]N, k=fJ, n, kFor the beam selection factor,For power allocation factor, therefore n-th of beam signal isgJ, n, kIt is GJ, kN-th column；It is obtained n-th in j-th of angle domain using MPA Message Passing Algorithm
The signal of k-th of user of a beam signal is：
Further, the building space Sparse Code sub-matrix FjMethod include：
(1) according to KjAnd dfInitializationAnd dv。KjIt is service user,It is beam set, dfIt is simple beam maximum service
Number of users, dvIt is that single user maximum occupies numbers of beams；
(2) base station constant power on the wave beam that each user occupies sends training frames, and user feeds back to base station after receiving,
Base station foundationEstimation obtains the letter of user under the transmission power
Make an uproar ratio ζJ, n, k.Wherein interference in clusterIt is interfered between cluster
(3) ifAndThe maximum user k of signal-to-noise ratio is selected for wave beam n.It is that wave beam n currently divides
With number of users,It is that user k currently occupies numbers of beams；
(4) corresponding Sparse Code is set and divides the F in multiplex matricesN, k=1；
(5) when on simple beam service number of users be greater than dfWhen, the unusable wave beam of subsequent other users；When single user accounts for
It is greater than d with wave beamvWhen, wave beam can not be distributed for the user again, with the sparsity of holding matrix；
(6) (3) and (4) are repeated until without workable wave beam or without user to be allocated.
Further, the determining power distribution matrix PjMethod includes：
(1) single cluster power P=P is initialized0, the number of iterations t=0, power iteration step length δ, tolerance factor ε；
(2) according to formulaCalculate cluster juPower, whereinα, beta, gamma are the glugs under the conditions of KKT
Bright day multiplier；
(3) fixed tuft juPower it is constant, in PsumUnder conditions of-P, cluster j is calculated by (2)dDistribution power and fixed tuft
jdPower；
(4) by formulaCalculate the signal-to-noise ratio of user in different clusters；WhereinIf the smallest user's noise in each cluster
The difference of ratio is less than tolerance factor ε, and the power distribution based on fairness is completed, and terminates power distribution；Otherwise, P=P0+ δ, t=t
+ 1, continue the process of (3), until the number of iterations exceeds.
Another object of the present invention is to provide a kind of application extensive sparse nonopiate accesses in the airspace MIMO to realize
The wireless communication system of method.
In conclusion advantages of the present invention and good effect are：
Based on extensive MIMO uniform linear array channel model, low latitudes angle domain is carried out to higher-dimension channel first and is turned
Change, user's sub-clustering is completed by low latitudes angle domain channel space characteristic, reduces and is interfered between channel feedback expense and cluster；With with
The nonopiate mapping matrix of transmitting terminal is designed on the basis of the limited channel dimension of family cluster space, combines force zero method (ZF) and message transmission is calculated
Method (MPA) designs nonopiate demodulating algorithm in receiving end for user, and show that extensive mimo system is nonopiate optimal based on this
Transmission strategy effectively promotes service connection quantity, provides resolving ideas for the following intensive scene access.
Detailed description of the invention
Fig. 1 is the sparse nonopiate access implementing method flow chart in the extensive airspace MIMO provided in an embodiment of the present invention.
Fig. 2 is the application scenarios of the sparse nonopiate access implementing method in the extensive airspace MIMO provided in an embodiment of the present invention
Schematic diagram.
Fig. 3 is the schematic diagram of embodiment provided in an embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
Present invention relates particularly to the sparse nonopiate access implementing methods in the extensive airspace MIMO, can be applied to extensive MIMO
Wireless network, eliminate cluster between interfere, meet intensive multiple access demand, lifting system overall performance.
Application principle of the invention is described in detail with reference to the accompanying drawing.
As shown in Figure 1, the sparse nonopiate access implementing method in the extensive airspace MIMO provided in an embodiment of the present invention includes
Following steps：
S101：In extensive mimo system, base station：Equally distributed antenna, antenna are mapped as by different extended corners
The cluster of angle domain；
S102：Angle domain channel matrix of the acquisition base station to user；
S103：Space Sparse Code sub-matrix is constructed, determines power distribution matrix, and send data；
S104：Using the use in a certain wave beam in ZF force zero method and the available angle domain of MPA message transmission unified algorithm
Family signal.
Application principle of the invention is further described with reference to the accompanying drawing.
The sparse nonopiate access implementing method in the extensive airspace MIMO provided in an embodiment of the present invention specifically includes following step
Suddenly：
Step 1：In extensive mimo system, there is the equally distributed antenna of M root in base station, and antenna presses different extended corners
Δθj(j=1,2 ..., J) is mapped as the cluster of J angle domain, wherein j-th of angle domainljIndicate jth
The radius of a cluster, DjDistance of the expression cluster center to base station.
Step 2：The beam set that j-th of angle domain uses isUser's collection of service is combined into Kj, each user assembled
There is N root antenna.Wherein in j-th of angle domain base station to KjThe angle domain channel matrix of a user isGjBy the channel matrix H of j-th of angle domainjIt is obtained through DFT transform.
Step 3：Space Sparse Code sub-matrix F is constructed using algorithm 1j, power distribution matrix P is determined using algorithm 2j, and
Send data.Send signalWhereinIt is user data, FjIt is that sparse coding reflects
Penetrate matrix, PjRepresent the power of j-th of angle domain distribution.
Step 4：Receiving signal isWherein njTo obeyAdditive white Gaussian noise, obtained in receiving end using force zero method,Wherein compel null matrixFor normalization factor.Define [sj]N, k=sJ, n, kFor transmission code
Word, [Fj]N, k=fJ, n, kFor the beam selection factor,For power allocation factor, therefore n-th of beam signal isgJ, n, kIt is GJ, kN-th column.Using available j-th of the angle of ZF and MPA Message Passing Algorithm
The signal of k-th of user of n-th of beam signal is in domain：
In a preferred embodiment of the invention：Algorithm 1 (constant power distribution beam selection)
1. according to KjAnd dfInitializationAnd dv。KjIt is service user,It is beam set, dfIt is that simple beam maximum service is used
Amount, dvIt is that single user maximum occupies numbers of beams.
2. base station constant power on the wave beam that each user occupies sends training frames, user feeds back to base station after receiving,
Base station foundationEstimation obtains the letter of user under the transmission power
Make an uproar ratio ζJ, n, k.Wherein interference in clusterIt is interfered between cluster
If 3.AndThe maximum user k of signal-to-noise ratio is selected for wave beam n.It is that wave beam n is currently distributed
Number of users,It is that user k currently occupies numbers of beams.
4. the F that corresponding Sparse Code divides in multiplex matrices is arrangedN, k=1.
5. when service number of users is greater than d on simple beamfWhen, the unusable wave beam of subsequent other users；When single user accounts for
It is greater than d with wave beamvWhen, wave beam can not be distributed for the user again, with the sparsity of holding matrix.
6. repeating step 3 and step 4 until without workable wave beam or without user to be allocated.
In a preferred embodiment of the invention：Algorithm 2 (heuristic power distribution algorithm)
1. initializing single cluster power P=P0, the number of iterations t=0, power iteration step length δ, tolerance factor ε.
2. according to formulaCalculate cluster juPower, whereinα, beta, gamma are the glugs under the conditions of KKT
Bright day multiplier.
3. fixed tuft juPower it is constant, in PsumUnder conditions of-P, cluster j is calculated by step 2dDistribution power and fixed tuft
jdPower.
4. by formulaCalculate the signal-to-noise ratio of user in different clusters.WhereinIf the smallest user's noise in each cluster
The difference of ratio is less than tolerance factor ε, it is believed that the power distribution based on fairness is completed, and terminates power distribution.Otherwise, P=P0+
δ, t=t+1 continue the process of step 3, until the number of iterations exceeds.
It is explained in detail below with reference to the application effect of the invention to comparison.
As shown in figure 3, the present invention will be described in detail：Service has 6 users, workable wave beam in some angle domain
There are 4, simple beam maximum service number of users is 3, and it is 2 that single user maximum, which occupies numbers of beams,.
Step 1 obtains angle domain channel matrix G
Base station is fed back to obtain to the channel matrix H of user using up channel in base station, and H obtains G via DFT transform,
Step 2 calculates sparse multiplex matrices F
Beam selection is distributed using 1 constant power of algorithm.Base station constant power on the wave beam that each user occupies sends training
Frame, user feed back to base station, base station foundation after receivingJ=ju, jdEstimate
Meter obtains the snr value ζ of user under the transmission power1,1ζ1,2ζ1,3...ζ4,6, choosing wherein maximum value is ζ1,1, by F1,1It is set to
1.It repeats the above process, Sparse Code under 6 user, 4 wave beam can be obtained and divide multiplex matrices as follows：
Step 3 determines power distribution matrix P
It is assumed that system total power is 1, power distribution matrix P is determined using algorithm 2, and carry out data transmission
Step 4 is in receiving end using ZF and MPA algorithm mixing demodulation user data
Signal in simple beam is obtained using ZF force zero method, single user is obtained out of simple beam using MPA Message Passing Algorithm
Code word：
rN, k=zN, kgN, kdN, k。
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (6)
1. the extensive sparse nonopiate access implementing method in the airspace MIMO, which is characterized in that the extensive airspace MIMO is sparse
Nonopiate access implementing method is based on extensive MIMO uniform linear array channel model, carries out low latitudes to higher-dimension channel first
Angle domain conversion completes user's sub-clustering by low latitudes angle domain channel space characteristic, with user's cluster space limited channel dimension
On the basis of design the nonopiate mapping matrix of transmitting terminal, joint force zero method (ZF) and Message Passing Algorithm (MPA) receiving end be use
Nonopiate demodulating algorithm is designed at family, obtains the nonopiate optimal transmission strategy of extensive mimo system based on this.
2. the sparse nonopiate access implementing method in the extensive airspace MIMO as described in claim 1, which is characterized in that described big
The sparse nonopiate access implementing method in the airspace scale MIMO includes：
In extensive mimo system, antenna for base station is uniformly distributed, and antenna is mapped as the cluster of angle domain by different extended corners；It obtains
Take base station to the angle domain channel matrix of user；Space Sparse Code sub-matrix is constructed, determines power distribution matrix, and send number
According to；Using the subscriber signal in a certain wave beam in ZF force zero method and the available angle domain of MPA message transmission unified algorithm.
3. the sparse nonopiate access implementing method in the extensive airspace MIMO as described in claim 1, which is characterized in that described big
The sparse nonopiate access implementing method in the airspace scale MIMO includes the following steps：
Step 1：In extensive mimo system, there is the equally distributed antenna of M root in base station, and antenna presses different extended corner Δ θj(j
=1,2 ..., J) it is mapped as the cluster of J angle domain, wherein j-th of angle domainljIndicate j-th of cluster
Radius, DjDistance of the expression cluster center to base station；
Step 2：The beam set that j-th of angle domain uses isUser's collection of service is combined into Kj, each user assembled have N root
Antenna；Wherein in j-th of angle domain base station to KjThe angle domain channel matrix of a user isGjBy the channel matrix H of j-th of angle domainjIt is obtained through DFT transform；
Step 3：Construct space Sparse Code sub-matrix Fj, determine power distribution matrix Pj, and send data；Send signalWhereinIt is user data, FjIt is sparse coding mapping matrix, PjIt represents j-th
The power of angle domain distribution；
Step 4：Receiving signal isWherein njTo obey
Additive white Gaussian noise, obtained in receiving end using force zero method,Wherein compel null matrixFor normalization factor；Define [sj]N, k=sJ, n, kFor transmission code
Word, [Fj]N, k=fJ, n, kFor the beam selection factor,For power allocation factor, therefore n-th of beam signal isgJ, n, kIt is GJ, kN-th column；It is obtained n-th in j-th of angle domain using MPA Message Passing Algorithm
The signal of k-th of user of a beam signal is：
4. the sparse nonopiate access implementing method in the extensive airspace MIMO as claimed in claim 3, which is characterized in that the structure
Build space Sparse Code sub-matrix FjMethod include：
(1) according to KjAnd dfInitializationAnd dv；KjIt is service user,It is beam set, dfIt is simple beam maximum service user
Number, dvIt is that single user maximum occupies numbers of beams；
(2) base station constant power on the wave beam that each user occupies sends training frames, and user feeds back to base station, base station after receiving
FoundationEstimation obtains the signal-to-noise ratio of user under the transmission power
Value ζJ, n, k；Wherein interference in clusterIt is interfered between cluster
(3) if un< dfAnd uk< dv, the maximum user k of signal-to-noise ratio is selected for wave beam n；unIt is the current distributing user of wave beam n
Number, ukIt is that user k currently occupies numbers of beams；
(4) corresponding Sparse Code is set and divides the F in multiplex matricesN, k=1；
(5) when on simple beam service number of users be greater than dfWhen, the unusable wave beam of subsequent other users；When single user occupies wave
Beam is greater than dvWhen, wave beam can not be distributed for the user again, with the sparsity of holding matrix；
(6) (3) and (4) are repeated until without workable wave beam or without user to be allocated.
5. the sparse nonopiate access implementing method in the extensive airspace MIMO as claimed in claim 3, which is characterized in that described true
Determine power distribution matrix PjMethod includes：
(1) single cluster power P=P is initialized0, the number of iterations t=0, power iteration step length δ, tolerance factor ε；
(2) according to formulaCalculate cluster juPower, whereinα, beta, gamma are the glugs under the conditions of KKT
Bright day multiplier；
(3) fixed tuft juPower it is constant, in PsumUnder conditions of-P, cluster j is calculated by (2)dDistribution power and fixed tuft jd's
Power；
(4) by formulaCalculate the signal-to-noise ratio of user in different clusters；WhereinIf the smallest user's noise in each cluster
The difference of ratio is less than tolerance factor ε, and the power distribution based on fairness is completed, and terminates power distribution；Otherwise, P=P0+ δ, t=t
+ 1, continue the process of (3), until the number of iterations exceeds.
6. a kind of using the sparse nonopiate access implementing method in the extensive airspace MIMO described in Claims 1 to 5 any one
Wireless communication system.
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| 王东明: "面向 5G 的大规模天线无线传输理论与技术", 《中国科学: 信息科学》 * |
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| CN109818157A (en) * | 2019-01-25 | 2019-05-28 | 电子科技大学 | A kind of construction method of the close coupling ultra-wideband antenna array based on irregular subarray |
| CN110138423A (en) * | 2019-04-25 | 2019-08-16 | 中国科学院上海微系统与信息技术研究所 | A kind of non-orthogonal multiplexing method |
| CN110337145A (en) * | 2019-06-05 | 2019-10-15 | 浙江大学 | User's cluster group technology and application based on angle domain millimeter wave non-orthogonal multiple access system |
| CN110337145B (en) * | 2019-06-05 | 2020-12-01 | 浙江大学 | User cluster grouping method based on angle domain millimeter wave non-orthogonal multiple access system and application |
| CN111586884A (en) * | 2020-05-12 | 2020-08-25 | 北京交通大学 | Multi-user random access method, base station and system |
| CN112600593A (en) * | 2020-12-15 | 2021-04-02 | 哈尔滨工业大学 | NOMA-based beam selection method |
| CN112787694A (en) * | 2021-01-12 | 2021-05-11 | 重庆邮电大学 | Low-complexity detection algorithm of MIMO-SCMA system based on expected propagation |
| CN114050853A (en) * | 2021-11-11 | 2022-02-15 | 哈尔滨工业大学 | Multi-user MIMO transmission method based on joint non-orthogonal codebook and precoding design |
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