CN112737650A - MIMO system transmitting antenna selection method based on machine learning - Google Patents

MIMO system transmitting antenna selection method based on machine learning Download PDF

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CN112737650A
CN112737650A CN202011600578.2A CN202011600578A CN112737650A CN 112737650 A CN112737650 A CN 112737650A CN 202011600578 A CN202011600578 A CN 202011600578A CN 112737650 A CN112737650 A CN 112737650A
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antenna combination
equal
performance
receiving end
antenna
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CN112737650B (en
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杨小凤
巫钊
覃斌毅
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Yulin Normal 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
    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a machine learning-based MIMO system transmitting antenna selection method, which relates to the technical field of MIMO and solves the technical problems of high complexity and low efficiency of the existing antenna selection algorithm, and the method comprises the following steps: s1, collecting a plurality of training channel matrixes HmAnd is combined with HmNormalizing to obtain Xm(ii) a S2, evaluating each training channel matrix HmAbout
Figure DDA0002870901530000011
The receiving end signal-to-noise ratio (or bit error rate, spectrum efficiency, energy efficiency and the like) performance of each antenna combination is obtained, the antenna combination serial number with the optimal receiving end performance is obtained, N is the total number of transmitting antennas, and K is the number of selected antennas; s3, constructing J two types of classifiers, and calculating and selecting each classifierProbability of J (J is more than or equal to 1 and less than or equal to J) th antenna combination
Figure DDA0002870901530000012
If p isjAnd if the antenna combination is more than or equal to 0.5, selecting the jth antenna combination. The invention can effectively reduce the inference deviation and improve the performance of the signal-to-noise ratio (or bit error rate, spectrum efficiency, energy efficiency and the like) of the receiving end, and has the advantages of lower algorithm complexity, less required training input data and high efficiency.

Description

MIMO system transmitting antenna selection method based on machine learning
Technical Field
The invention relates to the technical field of MIMO, in particular to a method for selecting a transmitting antenna of an MIMO system based on machine learning.
Background
Signals transmitted and received by each antenna in a MIMO (Multi-Input Multi-Output) system need to be processed through one radio frequency link, and as the number of antennas increases, the configuration of the radio frequency link increases, which increases the hardware cost of the MIMO system. The antenna selection algorithm can reduce the complexity and economic cost of equipment and ensure the diversity and multiplexing gains of the MIMO system by selecting the part of the antenna subsets with the best performance from the available antennas for transmitting and receiving signals.
The optimal algorithm is an exhaustive method, which selects the optimal subset by calculating the performance such as channel capacity or bit error rate of all possible antenna subset combinations, and the computational complexity of the algorithm increases dramatically as the number of antennas increases. In real-time channels, the antenna selection algorithm needs to adapt quickly to the environment where the channel state changes frequently, so that some low-complexity antenna selection algorithms are needed.
The MOLISCH provides a rapid selection algorithm based on channel correlation, so that the calculation complexity is greatly reduced, and the channel capacity loss is large; LU et al propose a rapid selection algorithm based on priority, but the multi-parameter and cross-mutation operation of genetic algorithms leaves the algorithm to be improved in terms of balance of complexity and performance.
The common disadvantages of the prior art methods are: (1) the algorithm has higher complexity and low efficiency; (2) the performance of the algorithm is greatly affected by the initial value setting.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art, and aims to provide a machine learning-based MIMO system transmitting antenna selection method with low algorithm complexity and high efficiency.
The technical scheme of the invention is as follows: a MIMO system transmitting antenna selection method based on machine learning comprises the following steps:
s1, collecting a plurality of training channel matrixes HmM is more than or equal to 1 and less than or equal to M, and HmNormalizing to obtain Xm；
S2, evaluating each training channel matrix HmAbout
Figure BDA0002870901510000021
The receiving end performance of each antenna combination obtains the antenna combination serial number with the optimal receiving end performance, N is the total number of transmitting antennas, and K is the number of selected antennas;
s3, constructing J two types of classifiers, wherein each classifier calculates the probability of selecting the jth antenna combination (J is more than or equal to 1 and less than or equal to J)
Figure BDA0002870901510000022
If p isjIf the antenna combination is more than or equal to 0.5, selecting the jth antenna combination,
wherein the decision function fj(X)＝AjKTKernel function K ═ K (X, X)i)]，K(X,Xi)＝exp{-‖X-Xi‖2}。
As a further improvement, the coefficient AjThe specific calculation steps are as follows:
s31, in the 1 st cycle of training the jth classifier
Figure BDA0002870901510000023
D＝{Xm(m＝1,2,…,M)},
For XnE, calculating an objective function by using the C \ C:
Figure BDA0002870901510000024
wherein y ═ y1,…ym,…yM]TIf training the channel matrix HmThe antenna combination serial number corresponding to the receiving end with the optimal performance is j, then ym1, otherwise ym＝0；
K1＝[K(Xm,Xp)],Xm∈D,Xp∈C∪{Xn},
K2＝[K(Xp,Xq)],Xq∈C∪{Xn},
In the k-th cycle:
Figure BDA0002870901510000025
W＝diag[pj(Xm)(1-pj(Xm))],
Figure BDA0002870901510000026
p＝[pj(X1),pj(X2),…pj(XM)]T；
s32, finding so that H (X)n) Minimum size
Figure BDA0002870901510000031
Order to
Figure BDA0002870901510000035
Figure BDA0002870901510000032
S33, calculating H obtained by two continuous circulationskRelative rate of change of
Figure BDA0002870901510000033
If R is<δ, stopping the cycle; otherwise, steps S31, S32 are repeated.
Further, δ is 0.001.
Further, the performance of the receiving end is any one of a signal-to-noise ratio, a bit error rate, a spectrum efficiency or an energy efficiency.
Advantageous effects
Compared with the prior art, the invention has the advantages that:
the invention realizes the multi-class classification for transmitting antenna selection by designing the two-class classifier, can effectively reduce the inference deviation and improve the signal-to-noise ratio (or bit error rate, spectrum efficiency, energy efficiency and the like) performance of a receiving end, and has the advantages of lower algorithm complexity, less required training input data and high efficiency.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will be further described with reference to specific embodiments shown in the drawings.
Referring to fig. 1, a method for selecting transmit antennas of a MIMO system based on machine learning includes the following steps:
s1, collecting a plurality of training channel matrixes HmM is more than or equal to 1 and less than or equal to M, and HmNormalizing to obtain Xm；
S2, evaluating each training channel matrix HmAbout
Figure BDA0002870901510000034
The receiving end performance of each antenna combination obtains the antenna combination serial number with the optimal receiving end performance, N is the total number of transmitting antennas, and K is the number of selected antennas;
s3, constructing J two types of classifiers, and calculating the probability of each classifier selecting the jth (J is more than or equal to 1 and less than or equal to J) antenna combination
Figure BDA0002870901510000041
If p isjIf the antenna combination is more than or equal to 0.5, selecting the jth antenna combination,
wherein the decision function fj(X)＝AjKTKernel function K ═ K (X, X)i)]，K(X,Xi)＝exp{-‖X-Xi‖2}。
Coefficient AjThe specific calculation steps are as follows:
s31, in the 1 st cycle of training the jth classifier
Figure BDA0002870901510000042
D＝{Xm(m＝1,2,…,M)},
For XnE, calculating an objective function by using the C \ C:
Figure BDA0002870901510000043
wherein y ═ y1,…ym,…yM]TIf training the channel matrix HmThe antenna combination serial number corresponding to the receiving end with the optimal performance is j, then ym1, otherwise ym＝0；
K1＝[K(Xm,Xp)],Xm∈D,Xp∈C∪{Xn},
K2＝[K(Xp,Xq)],Xq∈C∪{Xn},
In the k-th cycle:
Figure BDA0002870901510000044
W＝diag0pj(Xm)(1-pj(Xm))],
Figure BDA0002870901510000045
p＝[pj(X1),pj(X2),…pj(XM)]T；
s32, finding so that H (X)n) Minimum size
Figure BDA0002870901510000046
Order to
Figure BDA0002870901510000049
Figure BDA0002870901510000047
S33, calculating H obtained by two continuous circulationskRelative rate of change of
Figure BDA0002870901510000048
If R is<δ, stopping the cycle; otherwise, steps S31, S32 are repeated.
In this embodiment, δ is 0.001. The performance of the receiving end is any one of signal-to-noise ratio, bit error rate, spectrum efficiency or energy efficiency.
The invention realizes the multi-class classification for transmitting antenna selection by designing the two-class classifier, can effectively reduce the inference deviation and improve the signal-to-noise ratio (or bit error rate, spectrum efficiency, energy efficiency and the like) performance of a receiving end, and has the advantages of lower algorithm complexity, less required training input data and high efficiency.
The above is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that several variations and modifications can be made without departing from the structure of the present invention, which will not affect the effect of the implementation of the present invention and the utility of the patent.

Claims (4)

1. A MIMO system transmitting antenna selection method based on machine learning is characterized by comprising the following steps:
s1, collecting a plurality of training channel matrixes HmM is more than or equal to 1 and less than or equal to M, and HmNormalizing to obtain Xm；
S2, evaluating each training channel matrix HmAbout
Figure FDA0002870901500000011
The receiving end performance of each antenna combination obtains the antenna combination serial number with the optimal receiving end performance, N is the total number of transmitting antennas, and K is the number of selected antennas;
s3, constructing J two types of classifiers, wherein each classifier calculates the probability of selecting the jth antenna combination (J is more than or equal to 1 and less than or equal to J)
Figure FDA0002870901500000012
If p isjIf the antenna combination is more than or equal to 0.5, selecting the jth antenna combination,
wherein the decision function fj(X)＝AjKTKernel function K ═ K (X, X)i)]，K(X，Xi)＝exp{-||X-Xi||2}。
2. The method of claim 1, wherein the coefficient a is a function of the number of antennas selected for use in a MIMO systemjThe specific calculation steps are as follows:
s31, in the 1 st cycle of training the jth classifier
Figure FDA0002870901500000013
D＝{Xm(m＝1，2，...，M)}，
For XnE, calculating an objective function by using the C \ C:
Figure FDA0002870901500000014
wherein y ═ y1，…ym，…yM]TIf training the channel matrix HmThe antenna combination serial number corresponding to the receiving end with the optimal performance is j, then ym1, otherwise ym＝0；
K1＝[K(Xm，Xp)]，Xm∈D，Xp∈C∪{Xn}，
K2＝[K(Xp，Xq)]，Xq∈C∪{Xn}，
In the k-th cycle:
Figure FDA0002870901500000015
W＝diag[pj(Xm)(1-pj(Xm))]，
Figure FDA0002870901500000021
p＝[pj(X1),pj(X2),…pj(XM)]T；
s32, finding so that H (X)n) Minimum size
Figure FDA0002870901500000022
Order to
Figure FDA0002870901500000023
Figure FDA0002870901500000024
S33, calculating H obtained by two continuous circulationskRelative rate of change of
Figure FDA0002870901500000025
If R is<δ, stopping the cycle; otherwise, steps S31, S32 are repeated.
3. The machine-learning based MIMO system transmit antenna selection method of claim 2, wherein δ is 0.001.
4. The method of claim 1, wherein the receiver performance is any one of snr, ber, spectral efficiency or energy efficiency.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114629532A (en) * 2022-03-09 2022-06-14 玉林师范学院 Accurate and rapid MIMO system transmitting antenna selection method

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Publication number Priority date Publication date Assignee Title
US20130095774A1 (en) * 2011-09-08 2013-04-18 Drexel University Method for selecting state of a reconfigurable antenna in a communication system via machine learning
US9503164B1 (en) * 2015-06-09 2016-11-22 Hong Kong Applied Science and Technology Research Institute Company Limited Method and apparatus for channel estimation in massive MIMO systems with dynamic training design
CN108667502A (en) * 2018-04-27 2018-10-16 电子科技大学 A kind of spatial modulation antenna selecting method based on machine learning
CN109302217A (en) * 2018-12-06 2019-02-01 玉林师范学院 A kind of efficient mimo system emitting antenna selecting method
CN109660287A (en) * 2018-12-10 2019-04-19 深圳大学 A kind of antenna selecting method based on deep learning

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130095774A1 (en) * 2011-09-08 2013-04-18 Drexel University Method for selecting state of a reconfigurable antenna in a communication system via machine learning
US9503164B1 (en) * 2015-06-09 2016-11-22 Hong Kong Applied Science and Technology Research Institute Company Limited Method and apparatus for channel estimation in massive MIMO systems with dynamic training design
CN108667502A (en) * 2018-04-27 2018-10-16 电子科技大学 A kind of spatial modulation antenna selecting method based on machine learning
CN109302217A (en) * 2018-12-06 2019-02-01 玉林师范学院 A kind of efficient mimo system emitting antenna selecting method
CN109660287A (en) * 2018-12-10 2019-04-19 深圳大学 A kind of antenna selecting method based on deep learning

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114629532A (en) * 2022-03-09 2022-06-14 玉林师范学院 Accurate and rapid MIMO system transmitting antenna selection method
CN114629532B (en) * 2022-03-09 2023-03-14 玉林师范学院 Accurate and rapid MIMO system transmitting antenna selection method

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