CN107332598A - A kind of precoding of mimo system joint and antenna selecting method based on deep learning - Google Patents
A kind of precoding of mimo system joint and antenna selecting method based on deep learning Download PDFInfo
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- CN107332598A CN107332598A CN201710495044.XA CN201710495044A CN107332598A CN 107332598 A CN107332598 A CN 107332598A CN 201710495044 A CN201710495044 A CN 201710495044A CN 107332598 A CN107332598 A CN 107332598A
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- 238000013135 deep learning Methods 0.000 title claims abstract description 29
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- 238000013136 deep learning model Methods 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
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- 238000013461 design Methods 0.000 abstract description 6
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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
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
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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
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0602—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using antenna switching
- H04B7/0608—Antenna selection according to transmission parameters
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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
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0602—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using antenna switching
- H04B7/0608—Antenna selection according to transmission parameters
- H04B7/061—Antenna selection according to transmission parameters using feedback from receiving side
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Abstract
The invention discloses a kind of mimo system joint precoding based on deep learning and antenna selecting method, comprise the following steps：The training dataset required for deep learning is produced by existing antenna selecting method first；Then, deep learning model is set up, deep learning model is trained using training data and preserves；Then day line options are completed using the deep learning model preserved；Optimal Precoding Design finally is carried out to selected MIMO subsystems.The present invention designs the precoding of mimo system joint and day line options using depth learning technology, can realize relatively low computation complexity in the case where obtaining better systems signal to noise ratio.
Description
Technical field
The present invention relates to mobile communication technology field, and in particular to a kind of mimo system based on deep learning, which is combined, to prelist
Code and antenna selecting method.
Background technology
In recent years, people increasingly increase to the demand of radio communication, and existing wireless system is gradually difficult to meet demand,
The research of wireless system (5G) of future generation is more urgent.One of key technology as Next-Generation Wireless Communication Systems, on a large scale
The research of multi-input multi-output system (massive MIMO) has attracted increasing researcher.Extensive mimo antenna quantity
Radio frequency link quantity can be increased by increasing, and this significantly increases system cost and complexity.Solving one of the problem has efficacious prescriptions
Case is exactly a day line options.The technology significantly reduces system cost and multiple on the premise of the most of advantages of mimo system are retained
Miscellaneous degree.However, antenna selection problem is a NP-hard problem in itself, solving the problem needs to rely on traversal search method, no
Globally optimal solution can not then be obtained.And traversal search method can make it that calculate time complexity increases with antenna amount index,
It is difficult to calculate optimal antenna selecting plan within coherence time in extensive MIMO, therefore the antenna of reduction time complexity
Selection algorithm is particularly important in extensive MIMO.Meanwhile, in recent years, with the development of deep learning (DL) algorithm, nerve net
Network receives very big concern again.There are some researches show deep neural network has very high recognition performance simultaneously in pattern classification
And the prediction process of network is very efficient because pertaining only to matrix multiple and simple nonlinear operation.Inspired by this, the present invention
Plan antenna selection problem and be modeled as a pattern classification problem, then utilize the existing antenna selecting party of deep neural network study
The behavior of method, and then utilize the day line options of deep learning real-time performance mimo system.
The content of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a kind of mimo system joint based on deep learning
Precoding and antenna selecting method, the inventive method consider NP-hard problems and traversal in mimo system day line options and searched
The problem of rope computation complexity is too high, it is ensured that realize quick day line selection in the case where obtaining better systems signal to noise ratio
Select.
The purpose of the present invention is achieved through the following technical solutions：A kind of mimo system joint based on deep learning
Precoding and antenna selecting method, comprise the following steps：
(1)：The training dataset required for deep neural network, the training are obtained using existing antenna selecting method
Data set includes two parts：Input (input) data set for channel matrix (H) to gather, output (output) data set is antenna
Selection set；Obtain maximum transmission power P；
(2)：Initialize deep neural network parameter：Weight w, biasing b, learning rate L, maximum frequency of training I, processing data
Size c, each layer neuron number；
(3)：Deep learning model is set up, deep learning model is trained using the training dataset obtained in step 1 and protects
Deposit；
(4)：Channel estimation is carried out using pilot frequency system and obtains channel matrix H, utilizes the deep learning mould preserved in step 3
Type, obtains antenna selection set I；
(5)：Antenna selection set I is obtained according to step 4, forming corresponding MIMO subsystems progress precoding processing is
HI=H (：, I), it is rightEigenvalues Decomposition is carried out, it is v to make the corresponding characteristic vector of eigenvalue of maximum, is takenAs prelisting
Code vector.
Compared with prior art, beneficial effect of the present invention：The present invention is produced deep by existing antenna selecting method first
The training dataset spent required for study；Then, deep learning model is set up, deep learning model is trained simultaneously using training data
Preserve；Then day line options are completed using the deep learning model preserved；Finally selected MIMO subsystems are carried out optimal
Precoding Design.The present invention designs the precoding of mimo system joint and day line options using depth learning technology, can obtain
Relatively low computation complexity is realized in the case of better systems signal to noise ratio.
Brief description of the drawings
Fig. 1 is multiple-input and multiple-output of the embodiment of the present invention (MIMO) system model figure.
Fig. 2 is deep learning (DL) flow chart of the embodiment of the present invention.
Fig. 3 is maximum signal to noise ratio of the embodiment of the present invention and antenna scale graph of a relation.
Fig. 4 is Riming time of algorithm of the embodiment of the present invention and antenna scale graph of a relation.
Embodiment
In order that the purpose of the present invention and effect are clearer, below in conjunction with the accompanying drawings to the specific embodiment party of the inventive method
Formula is described in detail.
As shown in Figure 1, it is considered to multiple-input and multiple-output (MIMO) system, the system signal includes N number of transmitting antenna from one
Base station (BS) be transferred to a mobile terminal for having M reception antenna, it is assumed that any letter between transmitting antenna and reception antenna
Road is flat fading, then M × N baseband channels complex matrix is denoted as
G=[g1 g2 ... gM]H (1)
WhereinIt is a plural row vector for including channel coefficients between N number of transmitting antenna and m-th of reception antenna, m=
1,2 ..., M.Receiver has channel matrix G information.
Feedback based on receiver, emitter selection K (from N number of) individual transmitting antenna is simultaneously carried out using total power constraint
Precoding, and transmit signalWe equivalently it can use a precoding vector v ∈ VN, submit to restraint | | v
||0=K, wherein
VN={ v ∈ CN:||v||≤1} (2)
The vector of M × 1 that the pulse matching filtering of so down coversion is received is：
WhereinIt is the coloured multiple noise vector of a zero-mean additive, its autocorrelation matrix is denoted as R.Due to y generations
Unknown signaling vector of one, the table under coloured noise interference, it is exactly Minimum Mean Square Error (MMSE) filter to maximize signal to noise ratio wave filter
Ripple device
It is output as
So output signal-to-noise ratio is exactly
WhereinBe M × N conversion after channel matrix.Equation (6) shows prior weight and precoding
The relation of vector.
Our target is K antenna for base station of selection and optimizes precoding vector v to maximize prior weight (6).
It is exactly that we find the solution v of following problem
Due to existing in problem (7) | | v | |0=K bound terms, it is a NP-hard problem to cause problem (7).Solving should
Problem needs to rely on traversal search method, otherwise can not obtain globally optimal solution.And traversal search method can cause the calculating time
Complexity is difficult to calculate optimal antenna selecting plan within coherence time with the increase of antenna amount index in MIMO.
Therefore, the present invention solves antenna selection problem using deep learning (DL).
Fig. 2 gives carries out mimo system day line options flow chart using deep learning.Specifically, it can be described as follows：
A kind of precoding of mimo system joint and antenna selecting method based on deep learning, comprise the following steps：
(1)：The training dataset required for deep neural network, the training are obtained using existing antenna selecting method
Data set includes two parts：Input (input) data set for channel matrix (H) to gather, output (output) data set is antenna
Selection set；Obtain maximum transmission power P；
(2)：Initialize deep neural network parameter：Weight w, biasing b, learning rate L, maximum frequency of training I, processing data
Size c, each layer neuron number；
(3)：Deep learning model is set up, deep learning model is trained using the training dataset obtained in step 1 and protects
Deposit；
(4)：Channel estimation is carried out using pilot frequency system and obtains channel matrix H, utilizes the deep learning mould preserved in step 3
Type, obtains antenna selection set I；
(5)：Antenna selection set I is obtained according to step 4, forming corresponding MIMO subsystems progress precoding processing is
HI=H (：, I), it is rightEigenvalues Decomposition is carried out, it is v to make the corresponding characteristic vector of eigenvalue of maximum, is takenAs prelisting
Code vector.
Technical scheme is further elaborated below by instantiation.Existing antenna selecting method has
Auxiliary angle algorithm, force search and sparse Principal Component Analysis Algorithm etc., in an experiment, needed for we are obtained using auxiliary angle algorithm
Training dataset.In addition, in an experiment, we are using 8-2 (8 transmitting antennas, 2 reception antennas and selection transmitting day
Line number amount is equal to reception antenna quantity), 16-2,32-2,64-2 carries out method validation.Specifically, using following experiment parameter：
1. maximum frequency of training I spans are 50000-200000, and learning rate L spans are 0.001-
0.000001, it is 50-2000 that data batch size c spans are read every time.
2. each layer biasing b is initialized as 0.1, and each layer weight w is initialized as cutting gearbox, and wherein standard deviation is(NinFor this layer of input number of nodes).
3. deep learning uses five layer networks：Input layer, three hidden layers, output layers.Batch regularization is carried out between each layer
(batch normalization), node in hidden layer is respectively 4N, 2N, 2N (N is transmitting antenna number).
4. linear unit (relu) function is corrected in activation primitive selection, and output layer selection relu6 functions, loss function is used
Mean square deviation function, majorized function selection stochastic gradient descent function.
Fig. 3 sets forth deep learning (DL) and maximum signal to noise ratio and antenna scale relation under auxiliary angle (AA) algorithm
Figure.It can be seen that DL and AA maximum signal to noise ratio performance substantially close to.DL ensure that good system signal noise ratio.
Fig. 4 sets forth deep learning (DL) and AA Riming time of algorithm and antenna scale graph of a relation.Can from figure
To find out, AA exponentially increases with the antenna scale increase calculating time, and the DL calculating times are Millisecond.Calculated needed for DL
Time reduces several times than AA, and DL can quickly realize mimo system day line options.
The present invention is not only limited to above-mentioned embodiment, and persons skilled in the art are according to disclosed by the invention interior
Hold, the present invention can be implemented using other a variety of specific embodiments.Therefore, every design structure and think of using the present invention
Road, does some simple designs for changing or changing, both falls within the scope of the present invention.
Claims (1)
1. a kind of precoding of mimo system joint and antenna selecting method based on deep learning, it is characterised in that including as follows
Step：
(1)：The training dataset required for deep neural network, the training data are obtained using existing antenna selecting method
Collection includes two parts：Input (input) data set for channel matrix (H) to gather, output (output) data set is day line options
Set；Obtain maximum transmission power P；
(2)：Initialize deep neural network parameter：Weight w, biasing b, learning rate L, maximum frequency of training I, processing data size
C, each layer neuron number；
(3)：Deep learning model is set up, deep learning model is trained using the training dataset obtained in step 1 and preserves；
(4)：Channel estimation, which is carried out, using pilot frequency system obtains channel matrix H, using the deep learning model preserved in step 3,
Obtain antenna selection set I；
(5)：Antenna selection set I is obtained according to step 4, it is H to form corresponding MIMO subsystems and carry out precoding processingI=H
(：, I), it is rightEigenvalues Decomposition is carried out, it is v to make the corresponding characteristic vector of eigenvalue of maximum, is takenAs precoding to
Amount.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108540419A (en) * | 2018-03-21 | 2018-09-14 | 东南大学 | A kind of OFDM detection methods of the anti-inter-sub-carrier interference based on deep learning |
| CN108923828A (en) * | 2018-07-06 | 2018-11-30 | 西北工业大学 | A kind of emitting antenna selecting method of the MIMO tapping channel based on deeply study |
| CN109560846A (en) * | 2019-01-04 | 2019-04-02 | 东南大学 | A kind of three-dimensional method for precoding based on model-driven deep learning |
| CN109617584A (en) * | 2019-01-08 | 2019-04-12 | 南京邮电大学 | A kind of mimo system beamforming matrix design method based on deep learning |
| CN109660287A (en) * | 2018-12-10 | 2019-04-19 | 深圳大学 | A kind of antenna selecting method based on deep learning |
| CN109936399A (en) * | 2019-03-07 | 2019-06-25 | 西北工业大学 | A kind of insincere junction network antenna selecting method based on deep neural network |
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| CN111092641A (en) * | 2019-12-18 | 2020-05-01 | 重庆邮电大学 | Hybrid precoding design method based on millimeter wave MIMO system deep learning |
| CN111262803A (en) * | 2020-03-04 | 2020-06-09 | 广州番禺职业技术学院 | Physical layer secure communication method, device and system based on deep learning |
| CN111541472A (en) * | 2020-04-21 | 2020-08-14 | 东南大学 | Low-complexity machine learning assisted robust precoding method and device |
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| CN112636793A (en) * | 2020-12-02 | 2021-04-09 | 江苏恒宝智能***技术有限公司 | Antenna selection method and system in MIMO communication system |
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| CN108540419A (en) * | 2018-03-21 | 2018-09-14 | 东南大学 | A kind of OFDM detection methods of the anti-inter-sub-carrier interference based on deep learning |
| CN108540419B (en) * | 2018-03-21 | 2020-12-01 | 东南大学 | OFDM detection method for resisting inter-subcarrier interference based on deep learning |
| CN108923828B (en) * | 2018-07-06 | 2019-06-07 | 西北工业大学 | A kind of emitting antenna selecting method of the MIMO tapping channel based on deeply study |
| CN108923828A (en) * | 2018-07-06 | 2018-11-30 | 西北工业大学 | A kind of emitting antenna selecting method of the MIMO tapping channel based on deeply study |
| WO2020010566A1 (en) * | 2018-07-12 | 2020-01-16 | Intel Corporation | Devices and methods for link adaptation |
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| US10911113B2 (en) | 2019-01-04 | 2021-02-02 | Industrial Technology Research Institute | Communication system and codec method based on deep learning and known channel state information |
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| CN109617584B (en) * | 2019-01-08 | 2021-12-21 | 南京邮电大学 | MIMO system beam forming matrix design method based on deep learning |
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| CN111049559B (en) * | 2019-11-13 | 2022-03-11 | 东南大学 | Deep learning precoding method using position information |
| CN111049559A (en) * | 2019-11-13 | 2020-04-21 | 东南大学 | Deep learning precoding method using position information |
| CN111092641A (en) * | 2019-12-18 | 2020-05-01 | 重庆邮电大学 | Hybrid precoding design method based on millimeter wave MIMO system deep learning |
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| CN112636793A (en) * | 2020-12-02 | 2021-04-09 | 江苏恒宝智能***技术有限公司 | Antenna selection method and system in MIMO communication system |
| CN113300746A (en) * | 2021-05-24 | 2021-08-24 | 内蒙古大学 | Millimeter wave MIMO antenna and hybrid beam forming optimization method and system |
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| CN114567358A (en) * | 2022-03-03 | 2022-05-31 | 东南大学 | Large-scale MIMO robust WMMSE precoder and deep learning design method thereof |
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