CN112702070B - A decoding optimization method for distributed joint source channel coding system - Google Patents
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Abstract
一种分布式联合信源信道编码系统的译码优化方法，涉及通信数据传输。步骤：进行LDPC码联合译码器操作初始化工作，对每一个分布式信源，计算各个变量节点的信道初始似然信息，未发送部分的由相关信源对应部分的后验LLR值与空间相关性计算获得；对LDPC码的变量节点和校验节点进行LLR值计算，并将变量节点的后验LLR值作为BCJR译码器的外信息，用于更新下一次迭代未发送部分；计算BCJR译码器前向推测和后向推测的信息量，并将两者相加输出作为LDPC码联合译码器的外信息；对LDPC码联合译码的后验LLR值进行硬判决操作，得到译码码字；对译码结果进行判定。实现信息的交互，提高系统译码性能。
A decoding optimization method for a distributed joint source channel coding system relates to communication data transmission. Steps: Initialize the operation of the LDPC code joint decoder, calculate the initial channel likelihood information of each variable node for each distributed source, and obtain the unsent part by calculating the a posteriori LLR value of the corresponding part of the relevant source and the spatial correlation; calculate the LLR value of the variable node and the check node of the LDPC code, and use the a posteriori LLR value of the variable node as the external information of the BCJR decoder to update the unsent part of the next iteration; calculate the amount of information of the forward speculation and backward speculation of the BCJR decoder, and add the two to output as the external information of the LDPC code joint decoder; perform hard decision operation on the a posteriori LLR value of the LDPC code joint decoding to obtain the decoding codeword; and judge the decoding result. Realize information interaction and improve the system decoding performance.
Description
Technical Field
The invention relates to communication data transmission, in particular to a decoding optimization method of a distributed joint information source channel coding system based on LDPC codes.
Background
In wireless sensor networks and video mobile communication, most of the transmitting end nodes have the characteristics of dense distribution, large data set quantity, limited storage and calculation capacity and the like. The distributed source coding (Distributed Source Coding, DSC) adopts a mode of independent coding and joint decoding, all nodes which do not communicate are separated and coded, the coded information is sent to a central node for joint decoding, the operation complexity is transferred to a decoding end, the calculated amount of the node coding end is reduced, and the power consumption limited requirement of a distributed scene is met. In 1973, d.slepian and J.Wolf(D.Slepian and J.Wolf.Noiseless coding of correlated information sources[J].IEEE Transactions on Information Theory,1973,19(4):471-480.) proposed and demonstrated that separate coding could achieve the same compression efficiency as joint coding, and this theoretical limit that compression could achieve was called the Slepian-Wolf limit.
Currently, popular DSC algorithms are realized based on channel codes, such as Turbo codes, low density parity check codes (Low DENSITY PARITY CHECK, LDPC), polar (Polar) codes and the like, and the channel codes have certain anti-interference capability and can ensure the accuracy of information source decoding. Wyner has demonstrated in 1974 that for discrete correlated sources, the performance of the channel coding employed reaches the shannon limit, and that DSC compression efficiency designed using this channel code can approach the Slepian-Wolf limit. There are two modes of realizing DSC by using channel codes, one is a DSC scheme based on a syndrome, takes the division of cosets as a basic idea, has optimal compression performance under an ideal channel by searching for a coset output sequence in decoding, and the other is a DSC scheme based on check bits, wherein the coding scheme considers noisy channels, performs joint decoding by using a supervision code element generated by coding and related information of a distributed source, and is more in accordance with a practical complex communication application scene compared with the scheme based on the syndrome.
The distributed joint source Channel Coding (Distributed Joint Source-Channel Coding, DJSCC) combines the Channel Coding and DSC, has the functions of compression and error resistance, and is more suitable for practical application scenes. The DJSCC technique can be classified into asymmetric compression and asymmetric (including symmetric) compression according to the compression coding scheme of the distributed source. For asymmetric compression, all distributed sources realize compression effect, and can adaptively adjust the code rate of the source under the condition that the total rate is kept unchanged, thereby ensuring the balance of rate distribution among nodes. Currently, more research is based on asymmetry DJSCC, and relatively little research effort is associated with "asymmetry".
In addition to the feedback information of the channel in the distributed scene, the discrete correlation information sources have correlation in time-space, and the statistical characteristics can be used for optimizing the modulation technology and the coding and decoding scheme to obtain additional performance gains. Conventional distributed correlation source schemes only mine the spatial correlation of the source, and many scholars study the time domain characteristics of the source by establishing markov correlation sources in order to further mine the time correlation of the distributed source.
Disclosure of Invention
The invention aims to provide a decoding optimization method of a distributed joint information source channel coding system, which is based on LDPC codes, fully digs the time-space correlation of distributed relevant information sources and improves the decoding accuracy of the system.
To facilitate an understanding of the following specific formulas of the present invention, meanings of english abbreviations, mathematical symbols and labels will be first given:
1) v j denotes the j-th variable node;
2) c i denotes an i-th check node;
3) L S,ch (j) represents a log-likelihood ratio (LLR) value of the channel initial state of the distributed source S corresponding to the variable node v j;
4) L S,c (i, j) represents LLR values transmitted by the distributed source S check node c i to the variable node v j;
5) L S,v (j, i) represents LLR values transferred by the distributed source S variable node v j to the check node c i;
6) L S,v (j) is posterior LLR value of distributed source S variable node v j
7) L S,Bv (j) represents the information content of the BCJR decoder node v j of the distributed source S
8)Representing a check node set connected to variable node v j;
9) Representing the removal of a check node set in which check node c i is connected to variable node v j;
10) Representing a set of variable nodes connected to check node c i;
11) A variable node set indicating that the removed variable node v j is connected to the check node c i;
12 R S (j) represents channel receiving information of the jth variable node of the distributed source S;
13 σ 2 represents the channel noise variance,
14 Sign () represents a sign-taking operation;
15 Tan () represents a tangent cosine function;
16 Tan -1 () represents the inverse of the tangent cosine function;
17 Γ S,t represents a variable node set sent by a distributed source S;
18 Γ S,nt represents a set of variable nodes that the distributed source S does not send;
19 ρ represents the spatial correlation of the correlated sources;
20 P ij represents the probability of state i transitioning to state j to describe the time dependence inherent to the source.
The invention comprises the following steps:
1) Performing LDPC code joint decoder operation initialization work, and calculating channel initial likelihood information of each variable node for each distributed information source, wherein posterior LLR values and spatial correlation calculation of corresponding parts of related information sources of an undelivered part are obtained;
2) LLR values of variable nodes in the LDPC code are calculated;
3) Calculating LLR values of check nodes in the LDPC code;
4) Calculating the posterior LLR value of a variable node in the LDPC code;
5) Respectively calculating the information quantity of forward speculation and backward speculation of the BCJR decoder;
6) Calculating the information quantity of each variable node of the BCJR decoder;
7) Performing hard decision operation on codeword bits according to positive and negative conditions of the posterior LLR values of the variable nodes in the step 4), judging the codeword to be 1 if the codeword is negative, and judging the codeword to be 0 if the codeword is negative;
8) Judging the decoding result, stopping decoding and outputting the decoding sequence if the decoding sequence meets the check equation or reaches the maximum iteration number, otherwise, returning to the step 1) to update the channel likelihood information of the non-transmitted part of each distributed information source, and continuing the decoding operation.
In step 1), the method for initializing the joint decoding operation of the LDPC code includes, for each distributed source, calculating, for each distributed source, channel initial likelihood information L ch of each variable node, where a posterior LLR value and a spatial correlation of a non-transmitted portion corresponding to the relevant source are calculated, assuming that the system has two markov relevant sources S 1 and S 2, taking source S 1 as an example, where the specific operation of calculating the channel initial likelihood information is performed according to the following formula:
The channel likelihood information L ch of the untransmitted part of each distributed information source is obtained by calculating the posterior LLR value and the spatial correlation of the corresponding part of the related information source during each decoding iteration, and the more accurate the posterior LLR value is along with the increase of the decoding iteration times, the more accurate the channel likelihood information L ch of the untransmitted part updated by the posterior LLR value is, so that the performance gain of the spatial correlation is obtained more accurately.
In step 2), the LLR values of the variable nodes in the LDPC code are calculated, and the specific operation is performed according to the following formula:
In step 3), the calculation of the LLR values of the check nodes in the LDPC code is performed according to the following formula:
in step 4), the posterior LLR value of the variable node in the LDPC code is calculated, and the specific operation is performed according to the following formula:
in step 5), the information amounts of forward speculation and backward speculation of the BCJR decoder are calculated respectively, and the specific operation is performed according to the following formula;
The method comprises the steps of transmitting posterior LLR values of LDPC code joint decoding to a BCJR decoder as input information, namely, as external information of the BCJR decoder, realizing information interaction of the decoder, and simultaneously utilizing time correlation carried by a Markov information source, namely, state transition relation of front and rear code elements of an information sequence, taking the influence of the front and rear code elements on a current code element as information quantity of forward estimation and backward estimation respectively, and adding the information quantity and the information quantity to output the information as the external information of the LDPC code joint decoder to obtain performance gain of the time correlation.
In step 6), the information quantity of each variable node of the BCJR decoder is calculated, and the specific operation is carried out according to the following formula:
In step 7), hard decision operation is performed on the codeword bits according to the positive and negative cases of the posterior LLR values of the variable nodes in step 4), which may be that, for the jth variable node, if The codeword is discriminated as 1, otherwise, as 0.
The beneficial effects of the invention are as follows:
And the information quantity of each variable node of the BCJR decoder is used as external information of the LDPC code joint decoding to participate in the LDPC code joint decoding, so that the decoding accuracy is improved. The LDPC code joint decoder continuously updates likelihood information L ch of a non-transmitted part by utilizing posterior LLR value and space correlation of the last iteration of a related information source when decoding iteration is carried out each time, so that performance gain of the space correlation is obtained more accurately, meanwhile, the BCJR decoder digs time correlation of the information source by calculating information quantity of forward speculation and backward speculation, and the two decoders mutually provide external information for each other, so that information interaction is realized, time-space correlation of a distributed Markov related information source is fully mined, and decoding performance of a system is improved.
Drawings
Fig. 1 is a decoding block diagram of a two-phase Guan Xinyuan concatenated LDPC code joint decoder and BCJR decoder.
Fig. 2 shows the information transfer between a two-phase Guan Xinyuan concatenated LDPC code joint decoder and a BCJR decoder.
Fig. 3 is a graph showing the comparison of BER performance curves of a BP decoding method, a concatenated SP-BCJR decoding method and the present invention method with spatial correlation ρ=0.04, different time correlation.
FIG. 4 is a graph showing BER performance curves of the cascade BP-BCJR decoding method and the method of the present invention, wherein the transition probability is 0.7, and the correlation is different.
Detailed Description
In order to make the technical means, the creation features, the achievement of the purpose and the effect of the present invention easy to understand, the present invention will be further described with reference to the accompanying drawings.
The invention discloses a decoding optimization method of an asymmetric distributed joint source channel coding system based on LDPC codes by Markov related information sources, which mainly comprises the steps of performing LDPC code joint decoder operation initialization work on each distributed information source, calculating channel initial likelihood information L ch of each variable node, wherein posterior LLR values of non-transmitted parts of the non-transmitted parts are obtained by the calculation of the posterior LLR values and the spatial correlation of the corresponding parts of the related information sources, performing LLR value calculation on the variable nodes and check nodes of the LDPC codes, using the posterior LLR values of the variable nodes as external information of a BCJR decoder, simultaneously updating the L ch of the non-transmitted part of the next iteration, calculating the forward speculated information quantity and the backward speculated information quantity of the BCJR decoder, adding the two information quantities to output the information as the external information of the LDPC code joint decoder, performing hard decision decoding operation on the posterior LLR values of the LDPC code joint decoding to obtain decoding code words, and judging the result.
The assumed communication system without loss of generality has two correlated sources S 1 and S 2, wherein the source probability is P [ S 1＝0]＝P[S2 =0 ] =1/2, the time correlation in the source is modeled as a first order markov model, the spatial correlation between the two is defined as ρ, and the BSC channel with the virtual transition probability of P [ S 2≠S1|S1 ] =ρ is usually modeled, wherein the smaller the value of ρ is, the stronger the spatial correlation is. Assuming that the information sequences of the information sources S 1 and S 2 are M bits in length, the check bits with the lengths of P 1 and P 2 are generated after LDPC coding, the total code lengths after coding are n 1＝M+P1 and n 2＝M+P2 respectively, and the corresponding coding code rates are R 1＝M/n1 and R 2＝M/n2. According to the conventional DJSCC system based on check bits, the distributed source transmits part of the information bits and check bits to realize the compression and error correction functions simultaneously, namely, the source S 1 transmits only the front am part of the information bits and check bits, wherein α is a constant between 0 and 1, and the source S 2 transmits the back (1- α) k part of the information bits and check bits. Thus, the compression ratios of the distributed sources S 1 and S 2 are respectivelyAnd (3) with
Fig. 1 shows a decoding optimization structure of a distributed joint source channel coding system based on an LDPC code for two related markov sources, and the information transmission situation is shown in fig. 2, and the specific operation steps are as follows:
step 1, initializing LDPC code joint decoding operation, and calculating channel likelihood information of variable node parts transmitted by distributed sources S 1 and S 2 respectively And (3) withThe non-transmitted part is obtained by the posterior LLR value and the spatial correlation calculation of the corresponding part of the related information source, taking the information source S 1 as an example, and the specific operation of calculating the initial likelihood information of the channel is carried out according to the following formula:
Wherein, The channel likelihood information of the variable node part transmitted in each iteration is from the channel and is kept unchanged, and the channel likelihood information of the variable node part not transmitted is updated by the posterior LLR value of the last iteration of the corresponding part of the related information source by utilizing the spatial correlation.
Step 2, calculating LLR values of variable nodes in the LDPC code, wherein the specific operation is carried out according to the following formula;
Wherein, And outputting the information quantity of the node v j of the BCJR decoder for the last iteration as the external information of the node corresponding to the variable of the LDPC code.
Step 3, calculating LLR values of check nodes in the LDPC code, wherein the specific operation is carried out according to the following formula;
Step 4, calculating posterior LLR values of variable nodes in the LDPC code, and taking the posterior LLR values as external information and decision soft information of a BCJR decoder, wherein the specific operation is carried out according to the following formula;
Step 5, considering the influence of the front code element and the rear code element on the current code element, respectively calculating the information quantity of forward speculation and backward speculation of the BCJR decoder, wherein the specific operation is carried out according to the following formula;
Step 6, adding the information quantity of forward speculation and backward speculation of the BCJR decoder to obtain the information quantity of each node of the BCJR decoder, feeding back the information quantity to the LDPC code joint decoder as the external information of the LDPC code joint decoder of the next iteration, and performing specific operation according to the following formula;
step 7, hard decision operation is carried out on the code word bits according to the positive and negative conditions of the posterior LLR values of the variable nodes in the step 4, namely, if the jth variable node is The codeword is discriminated as 1, otherwise, as 0;
And 8, judging the decoding result, stopping decoding if the decoding sequence meets a check equation or reaches the maximum iteration number, outputting the decoding sequence, otherwise, returning to the step 1 to update the channel likelihood information of the non-transmitted part of each distributed information source, and continuing the decoding operation.
The invention aims at the BER performance contrast of the traditional space-related belief propagation (Belief Propagation, abbreviated as BP) decoding algorithm and the cascading BP-BCJR decoding algorithm on the AWGN channel aiming at different time-space correlations, and corresponding simulation parameters are that two related Markov information sources, an AR4JA code with 1/2 code rate, the frame length is 1000 bits, and the parameter alpha=1/2, namely two phases Guan Xinyuan respectively send the information bits of the first half and the second half, thereby realizing symmetrical compression.
Fig. 3 shows BER performance comparisons of a conventional spatial correlation BP decoding, a concatenated BP-BCJR decoding, and a decoding optimization method according to the present invention for distributed sources S 1 with spatial correlation ρ=0.04, and different temporal correlations. The traditional space correlation BP decoding only calculates the initial LLR value of the untransmitted part through the space correlation before decoding iteration, and no participation of time-space correlation exists, and two distributed sources are independently and iteratively decoded, so that the performance of other two cascade decoding algorithms is obviously not good. The cascade BP-BCJR decoding introduces a BCJR decoder to mine the inherent time correlation of a Markov source in the traditional space correlation BP decoding, and obvious performance gain is obtained. The decoding optimization method fully exploits the time-space correlation of the Markov correlation information source and has optimal performance. When BER is in the order of magnitude of 10 -5, the provided decoding optimization method has about 0.25dB of performance gain when the transition probability is 0.7 and about 0.2dB of performance gain when the transition probability is 0.8 compared with the cascading BP-BCJR decoding method.
Fig. 4 shows the same transition probability P 01＝P10 =0.7, different spatial correlation, and performance comparison of the concatenated BP-BCJR decoding with the decoding optimization method proposed by the present invention. When BER is in order of magnitude of 10 -5, the decoding optimization method provided by the invention has about 0.5dB and about 0.25dB of performance gain in spatial correlation rho=0.12 and rho=0.04 respectively compared with the cascading BP-BCJR decoding method.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. The invention relates to a decoding optimization method of an asymmetric distributed combined information source channel coding system based on LDPC codes, wherein in the decoding process, LDPC code combined decoders utilize spatial correlation interaction posterior LLR values, BCJR decoders mine the inherent time correlation of distributed information sources, and two types of decoders realize information interaction. The invention can be said to fully exploit the time-space correlation of distributed correlation sources, bringing additional performance gains to the system.
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