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Jisuanji gongcheng

Parallel Maximum Likelihood Decoding Algorithm for RM Codes

Qiao Guo-lei

Published Jan 1, 2009• 0 citations
OpenAlex

Abstract

According to the error-correcting capability complementarity of Chase decoding algorithm and Ordered Statistic Decoding(OSD) algorithm,a new Chase-OSD parallel decoding algorithm for RM codes is proposed,in which,OSD algorithm processes certain MRIPs of a received sequence,while Chase-type decoding algorithm processes certain LRPs of a received sequence.If there are at least i+1 MRIPs of the ordered received sequence in error,OSD algorithm will fail.On the other hand,if the number of errors in the LRPs is large,the Chase-type algorithm will fail.A parallel decoding algorithm based on complementary characteristic is proposed.Simulation experimental results show this algorithm can achieve better decoding performance.

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According to the error-correcting capability complementarity of Chase decoding algorithm and Ordered Statistic Decoding(OSD) algorithm,a new Chase-OSD parallel decoding algorithm for RM codes is proposed,in which,OSD algorithm processes certain MRIPs of a received sequence,while Chase-type decoding algorithm processes certain LRPs of a received sequence.If there are at least i+1 MRIPs of the ordered received sequence in error,OSD algorithm will fail.On the other hand,if the number of errors in the LRPs is large,the Chase-type algorithm will fail.A parallel decoding algorithm based on complementary characteristic is proposed.Simulation experimental results show this algorithm can achieve better decoding performance.
Ordered Statistic Decoding(OSD) algorithm and Chase algorithm are investigated in this paper,based on which,a new Chase-OSD parallel decoding algorithm for medium and short lengths Low Density Parity-Check(LDPC) codes is proposed.Chasetype decoding algorithm is based on processing certain LRPs of a received sequence,while OSD algorithm is based on processing certain MRIPs of a received sequence.If there are too many errors in MRIPs,the OSD algorithm will fail,while there are too much errors in LRPs,the Chase-type algorithm will fail.A parallel concatenation decoding algorithm is proposed based on that complementary characteristic.The simulation results show that the proposed algorithm is effective.The decoding performance is improved compared with BP-OSD and BP-Chase algorithm.
In this contribution, KNIH algorithm and Ordered Statistic Decoding (OSD) algorithm are investigated, based on which, a novel KNIH-OSD parallel decoding scheme for LDPC codes is proposed. KNIH decoding algorithm processes certain LRPs of a received sequence, while OSD algorithm processes certain MRIPs of a received sequence. If there are too many errors in MRIPs, the OSD algorithm will fail, on the contrary, if there are too many errors in LRPs, the KNIH algorithm will fail. This contribution proposes a parallel decoding scheme based on the complementary characteristic of these two algorithms. Simulation results show that the proposed scheme is feasible and effective. Compared with OSD and KNIH algorithm, the decoding performance is improved.
This paper proposes a reduced complexity ordered statistics decoding (OSD) algorithm for linear block codes, the namely order skipping (OS)-OSD algorithm. An approximated correlation distance lower bound (CDLB) is derived by utilizing likelihood of the received symbols over the least reliable positions (LRPs). It enables the assessment of whether the higher-order decoding can yield a more likely codeword estimation. If not, they can be skipped. Error-correction performance of the OSOSD is analyzed. In particular, the decoding error probability of OS-OSD with order one is theoretically characterized. Our simulation results verify that the OS-OSD can achieve a significant complexity reduction over the state-of-the-art OSD without compromising the decoding performance.
A new scheme of selecting error pattern is proposed. OSD algorithm processes certain MRIPs of a received sequence, performs bits flip among MRIPs, forms error patterns, and then generates candidate codeword. The bits flip is equal probability among MRIPs in OSD algorithms, which leads to too many candidates generated, and seriously affects the implement of algorithm. This paper proposes an improved algorithm which adopts unequal probability strategy utilizing the reliability of received symbols. The simulation results show that the proposed algorithm is effective. With little performance degradation, the computational complexity of algorithm can be reduced by 60% or more for one class of LDPC codes.
In this paper, we propose an efficient decoding algorithm for short low-density parity check (LDPC) codes by carefully combining the belief propagation (BP) decoding and ordered statistics decoding (OSD) algorithms. Specifically, a modified BP (mBP) algorithm is applied for a certain number of iterations prior to OSD to enhance the reliability of the received message, where an offset parameter is utilized in mBP to control the weight of the extrinsic information in message passing. By carefully selecting the offset parameter and the number of mBP iterations, the number of errors in the most reliable positions (MRPs) in OSD can be reduced by mBP, thereby significantly improving the overall decoding performance of error rate and complexity. Simulation results show that the proposed algorithm can approach the maximum-likelihood decoding (MLD) for short LDPC codes with only a slight increase in complexity compared to BP and a significant decrease compared to OSD. Specifically, the order- (m -1) decoding of the proposed algorithm can achieve the performance of the order-m OSD.
A syndrome-based ordered statistics decoding(SOSD) algorithm for LDPC codes is proposed based on ordered statistics decoding(OSD) algorithm and syndrome-based decoding algorithm.The indexed systematic form of parity check matrix is obtained based on the least reliable independent positions(LRIPs) by performing Gaussian eliminations,which leads to that candidate codes are generated in the SOSD processing by using several binary vector additions,instead of using the re-encoding process,computation complexity in thereby reduced.A block accumulated log-likelihood ratio(LLR) strategy is further proposed to reconstruct the ordered information sequence,which can reduce the sensitivity of accumulated parameter selection to performance fluctuation.