CN109728824A - A kind of LDPC code iterative decoding method based on deep learning - Google Patents
A kind of LDPC code iterative decoding method based on deep learning Download PDFInfo
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Abstract
The present invention provides a kind of LDPC code iterative decoding method based on deep learning, is related to technical field of information transmission.First coded-bit and interchannel noise are estimated using standard BP iterative decoder, the estimated value of interchannel noise and information bit is obtained with this, then it is removed with noise estimation error of the neural network DNN to standard BP decoder, obtain the more accurate estimation to interchannel noise, then obtained noise is estimated to process in channel receiving end, it is re-entered into decoder again, continuous iteration.The present invention solves the technical issues of interchannel noise influences decoding performance in the prior art.The invention has the following beneficial effects: obtaining the more accurate estimation to interchannel noise, decoding signal-to-noise ratio is improved, to improve decoding performance and reduce algorithm complexity.
Description
Technical field
The present invention relates to technical field of information transmission, more particularly, to a kind of decoding side BP-DNN based on deep learning
Method.
Background technique
With the development of technology, deep learning obtains in fields such as computer vision, natural language processing, automatic driving vehicles
To extensive use, wherein also obtaining original achievement in terms of information transmission.There are interference problems in transmission process for information, especially
It is noise jamming, so that information goes wrong in receiving end.Therefore, it is necessary to coding modes of good performance and suitable decoding
Algorithm.Chinese patent application publication No. CN106571831A, data of publication of application on April 19th, 2017, entitled " one kind is based on deep
The application for a patent for invention file of the LDPC Hard decision decoding method and decoder of degree study " discloses a kind of sentencing firmly for LDPC code
Certainly interpretation method.Include the following steps: 1, one group (X, Y) is used as one group of tape label data；2, LDPC decoding sample set is established；
3, the foundation of deep learning Decoding model；4, the pre-training of deep learning Decoding model；5, deep learning Decoding model decodes simultaneously
Output.This interpretation method using hard-decision method, first decoding performance compared to soft decoding method may under
Drop, soft decoding method is by repeatedly relatively making judgement, and hard-decision method relatively makes judgement by single.Secondly in order to make
Neural network error rate of translation reaches in text the required bit error rate, and to may result in algorithm too time-consuming, thereby increases and it is possible to can occur
The case where degree fitting, so that the performance of decoding algorithm is declined.In addition shadow of the interchannel noise to decoding performance in communication process
Sound is also very important.
Summary of the invention
In order to solve the technical issues of interchannel noise influences decoding performance in communication process in the prior art, the present invention is mentioned
For a kind of LDPC code iterative decoding method based on deep learning, the interpretation method with low complex degree, robustness utilizes nerve
The powerful computing capability of network is removed the noise estimation error of BP decoder, to promote the decoding of BP decoder
Energy.
The technical scheme is that a kind of LDPC code iterative decoding method based on deep learning: step 1: to information
Sequence X carries out LDPC code coding, and the information sequence after coding is U=XG, is training set and test set information sequence U points；Step
Rapid two: the information sequence U of training set being modulated, modulated transmission symbol S adds channel of making an uproar by interchannel noise N, obtains
To the transmission symbol Y of channel output；Step 3: transmission symbol Y obtains information sequence estimated value by BP decoderBy passing
Defeated symbol Y subtracts the estimated value of transmission symbol SObtain interchannel noise estimated valueInterchannel noise estimated valueIt is input to
Neural network obtains interchannel noise estimated value after being updatedBy updated transmission symbolBP decoding is inputted again
Device is iterated decoding, so that the data of the input terminal of BP decoder and neural network input terminal are constantly updated, constantly decodes,
Until BP-DNN decoder iteration terminates；Step 4: BP decoder is inputted with the data of test set, so that the input of BP decoder
The data of end and neural network input terminal are constantly updated, and are constantly decoded, until iteration terminates.
Preferably, carrying out BPSK modulation to the information sequence U of training set, modulated transmission accords with S.
Preferably, transmission symbol Y obtains the estimated value that transmission accords with S by the soft demodulation of BPSK
Preferably, by interchannel noise N and interchannel noise estimated valueAvailable channel residual noise value R,For the loss function of neural network, n is code word size.
Compared with prior art, the beneficial effects of the present invention are: being estimated using standard BP decoder coded-bit,
Then it is removed with noise estimation error of the neural network to BP decoder, obtains the more accurate estimation to interchannel noise.
Meanwhile decoding signal-to-noise ratio will be gradually increased in the iteration between BP iterative decoder and neural network DNN, to improve decoding performance
With reduction algorithm complexity.
Detailed description of the invention
Attached drawing 1 is flow chart of the present invention.
Specific embodiment
Below with reference to the embodiments and with reference to the accompanying drawing the technical solutions of the present invention will be further described.
Embodiment 1:
As shown in Figure 1, a kind of LDPC code iterative decoding method based on deep learning, including following methods,
Step 1: LDPC code coding is carried out to binary information sequence X.LDPC code verifies (H) matrix by it and indicates, with conventional volume
Code.H-matrix is subjected to elementary transformation, is converted to generation (G) matrix.Information sequence after then encoding is U=XG.Information sequence
U points are training set and test set.The ratio between training set and test set data are nine to one.The present embodiment is with the LDPC code of 3/4 code rate
For illustrate, check matrix H is the matrix of 24x18, and generator matrix G is also (24, the 18) LDPC code for obtaining code rate and being 3/4,24
It is information bit length for code word size, 18.
Step 2: in order to make the information after coding be suitble to transmit in the channel, the training set of information sequence U is adjusted
System, the present embodiment select BPSK modulation.Transmission symbol S is obtained through ovennodulation.It transmits symbol S and passes through AWGN (Gauss additivity white noise
Sound) channel obtains output symbol Y, Y=S+N.N is the interchannel noise of awgn channel.
Step 3: the output symbol Y received is first carried out the soft demodulation of BPSK by BP decoder receiving end.By data preparation
For matrix Yi=[Y0,Y1,…,Y22,Y23], subscript i is data group number, is determined by information bit length k, shares 218Group.It will obtain
Data YiBP decoder is passed sequentially through to be decoded.The estimated value that transmission accords with S is obtained by the soft demodulation of BPSKPass through
BP decodes to obtain the estimated value with information sequence XThe estimated value of S is accorded with by transmission?
BP decoder output can obtain interchannel noise estimated valueWithFor basic input unit, it is input to neural network
(DNN) in.In neural network, by propagated forward algorithm and back-propagation algorithm, interchannel noise estimated valueIn output layer
The lower classification of sigmoid function effect, obtain updated interchannel noise estimated value
Obtaining updated interchannel noise estimated valueAfterwards, available residual noise R.Residual noiseThat is channel
The difference for the interchannel noise estimated value that real noise and neural network obtain.In order to promote the decoding performance of BP decoder, need compared with
Small residual noise R.R is smaller, channel estimation noiseCloser channel real noise N,Closer to real channel transmission symbol
Number S, in this way, influence of the interchannel noise to decoding performance is lowered in BP decoder.Therefore, selection willAs nerve
The loss function of network, neural network gradually reduces loss function value by back-propagation algorithm, so that R → 0,
With this come achieve the purpose that reduce residual noise R.N is code word size, and the present embodiment n is 24.This is arrived, first round decoding terminates.
Since the second wheel iterative decoding, in BP decoder input terminal, updated estimated value of making an uproar is cut with output symbol YThat is the input of BP decoder is updated to(subscript i indicates that i-th updates, and 0≤i≤m, m are circulation total degree),
The present embodiment, setting BP-DNN decoder iteration total degree are 10 times.Input is updated into YiBP is decoded as new round decoding
The input of device constantly decodes so that the data of the input terminal of BP decoder and neural network input terminal are constantly updated, meanwhile, BP
Decoding signal-to-noise ratio will be gradually increased in iteration between DNN, so that decoding performance is improved, until BP-DNN decoder iteration knot
Beam.
Step 4: the training to neural network after step 3, is being completed, is finally testing mind with test set data
Through network decoding performance.BP decoder is inputted with the data of test set, so that the input terminal of BP decoder and neural network input
The data at end are constantly updated, and are constantly decoded, until iteration terminates.Simultaneously in test phase, can attempt change must iteration time
Number m, carrys out test b P-DNN decoding performance with this.It is consistent with training neural network process to test neural network decoding performance, only not
The data for the data of training set being changed to test set are crossed, are repeated no more.
Claims (4)
1. a kind of LDPC code iterative decoding method based on deep learning, which is characterized in that including following methods: step 1: right
Information sequence X carries out LDPC code coding, and the information sequence after coding is U=XG, is training set and test information sequence U points
Collection；Step 2: being modulated the information sequence U of training set, and modulated transmission symbol S adds letter of making an uproar by interchannel noise N
Road obtains the transmission symbol Y of channel output；Step 3: transmission symbol Y obtains information sequence estimated value by BP decoderThe estimated value of transmission symbol S is subtracted by transmission symbol YObtain interchannel noise estimated valueInterchannel noise estimated valueIt is input to after neural network is updated and obtains interchannel noise estimated valueBy updated transmission symbolIt is defeated again
Enter BP decoder, be iterated decoding, so that the data of the input terminal of BP decoder and neural network input terminal are constantly updated, no
Disconnected decoding, until BP-DNN decoder iteration terminates；Step 4: BP decoder is inputted with the data of test set, so that BP is decoded
The input terminal of device and the data of neural network input terminal are constantly updated, and are constantly decoded, until iteration terminates.
2. a kind of LDPC code iterative decoding method based on deep learning according to claim 1, it is characterised in that: to instruction
The information sequence U for practicing collection carries out BPSK modulation, and modulated transmission accords with S.
3. a kind of LDPC code iterative decoding method based on deep learning according to claim 1, it is characterised in that: transmission
Symbol Y obtains the estimated value that transmission accords with S by the soft demodulation of BPSK
4. a kind of LDPC code iterative decoding method based on deep learning according to claim 1, it is characterised in that: by believing
Road noise N and interchannel noise estimated valueAvailable channel residual noise value R, For neural network
Loss function, n are code word size.
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| CN114337884A (en) * | 2022-01-06 | 2022-04-12 | 兰州大学 | Phase noise compensation and channel decoding joint design method based on deep learning |
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