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Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC)

Adaptive decoding of LDPC codes based on cross-entropy

Tso-Cho Chen

Published Jul 1, 2011• 0 citations
Publisher

Abstract

An adaptive decoding scheme for low-density parity-check (LDPC) codes is proposed for accelerating the convergence of message-passing algorithms. The evolution of adaptive weighting factor with the number of decoding iterations and stopping criterion are based on the cross-entropy between two consecutive a posteriori probabilities distribution of the soft output of decoder. Information-theoretic support and extensive simulations are provided to demonstrate the efficiency of the proposed algorithm for LDPC decoding. The proposed adaptive decoding algorithm can reduce significantly the average number of iterations of decoder while having a comparable or even better performance than conventional message-passing algorithms.

DOI
10.1109/csqrwc.2011.6037266

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An adaptive decoding scheme for low-density parity-check (LDPC) codes is proposed for accelerating the convergence of message-passing algorithms. The evolution of adaptive weighting factor with the number of decoding iterations and stopping criterion are based on the cross-entropy between two consecutive a posteriori probabilities distribution of the soft output of decoder. Information-theoretic support and extensive simulations are provided to demonstrate the efficiency of the proposed algorithm for LDPC decoding. The proposed adaptive decoding algorithm can reduce significantly the average number of iterations of decoder while having a comparable or even better performance than conventional message-passing algorithms.
A new adaptive and early stopping scheme for low-density parity-check (LDPC) decoding is proposed for accelerating the convergence of message-passing algorithms. The new algorithm is based on the cross-entropy between two consecutive a posteriori probabilities distribution of the soft output of decoder. Information-theoretic support and extensive simulations are provided to demonstrate the efficiency of the proposed adaptive algorithm and early stopping criterion. The proposed scheme can significantly reduce the average number of iterations of decoder while having a comparable or even better performance than conventional message-passing algorithms.
A weighted belief propagation decoding algorithm for low-density parity-check decoding is proposed to accelerate the convergence of decoding and reduce the number of decoding iterations. We propose an early stopping criterion which is based on the average weighted reliability measure of the hard output of decoder to detect undecodable blocks in an early stage of the decoding process. In addition, an adaptive weighted iteration scheme is proposed to increase the convergence speed of belief propagation (BP) decoding and therefore improve the error performance. The evolution of the adaptive weighting factor with the number of decoding iterations is based on the relative entropy between two consecutive ”a posteriori” probabilities distribution at decoder output. Simulation results show that the proposed algorithm can achieve about 0.2-0.4 dB coding gain improvement on average while reducing up to about 50%-85% iterations for decoding at low to medium E(subscript b)/ N(subscript 0) , compared with the conventional BP based algorithms.
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Abstract: In this paper, a fast-convergence algorithm is proposed for the decoding of low-density parity-check (LDPC) codes. By putting more weight on the message update from check node to variable node in current iteration, the proposed method speeds up the decoding significantly. Simulation results show that compared with the standard layered min-sum decoding algorithm, the proposed scheme may reduce about 1/6 iteration numbers for LDPC codes used in DVB-S2, with hardware complexity overhead less than 2%. Key–Words: LDPC code, fast convergence decoding algorithm, layered decoding, min-sum algorithm, hardware implementation 1
In this paper, we propose a new sequential message-passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This new decoding algorithm shows better bit error rate(BER) performance than that of the conventional message-passing decoding algorithm, especially for small number of iterations. Analytical results tell us that as the number of partitioned subsets of check nodes increases, the BER performance becomes better. We also derive the recursive equations for mean values of messages at variable nodes by using density evolution with Gaussian approximation. Simulation results also confirm the analytical results.