List decoding

About: List decoding is a research topic. Over the lifetime, 7251 publications have been published within this topic receiving 151182 citations.

Parallel Weighted Bit-Flipping Decoding

Abstract: A parallel weighted bit-flipping (PWBF) decoding algorithm for low-density parity-check (LDPC) codes is proposed. Compared to the best known serial weighted bit-flipping decoding, the PWBF decoding converges significantly faster but with little performance penalty. For decoding of finite-geometry LDPC codes, we demonstrate through examples that the proposed PWBF decoding converges in about 5 iterations with performance very close to that of the standard belief-propagation decoding.

Life Above Threshold: From List Decoding to Area Theorem and MSE

Abstract: We consider communication over memoryless channels using low-density parity-check code ensembles above the iterative (belief propagation) threshold. What is the computational complexity of decoding (i.e., of reconstructing all the typical input codewords for a given channel output) in this regime? We define an algorithm accomplishing this task and analyze its typical performance. The behavior of the new algorithm can be expressed in purely information-theoretical terms. Its analysis provides an alternative proof of the area theorem for the binary erasure channel. Finally, we explain how the area theorem is generalized to arbitrary memoryless channels. We note that the recently discovered relation between mutual information and minimal square error is an instance of the area theorem in the setting of Gaussian channels.

On combining Chase-2 and GMD decoding algorithms for nonbinary block codes

Abstract: In this letter, a class of algorithms that combines Chase-2 and GMD (generalized minimum distance) decoding algorithms is presented for nonbinary block codes. This approach provides additional trade-offs between error performance and decoding complexity. Reduced-complexity versions of the algorithms with practical interests are then provided and simulated.

An efficient maximum likelihood decoding algorithm for generalized tail biting convolutional codes including quasicyclic codes

Abstract: The complexity of the algorithm is shown to be asymptotically equal to that of the Viterbi algorithm and is very close for practical noisy channels. The latter is shown by means of computer simulation. The algorithm can be applied directly in an environment where soft-decision decoding is required or preferred. However, depending on the environment, some simplifications may be possible and/or necessary, resulting in suboptimal algorithms. Codes suitable for use with the algorithm should have short total memory length. >

On some new approaches to practical Slepian-Wolf compression inspired by channel coding

Abstract: We introduce three new innovations for compression using LDPCs for the Slepian-Wolf problem. The first is a general iterative Slepian-Wolf decoding algorithm that incorporates the graphical structure of all the encoders and operates in a 'turbo-like' fashion. The second innovation introduces source-splitting to enable low-complexity pipelined implementations of Slepian-Wolf decoding at rates besides corner points of the Slepian-Wolf region. This innovation can also be applied to single-source block coding for reduced decoder complexity. The third approach is a linear programming relaxation to maximum-likelihood sequence decoding that exhibits the ML-certificate property. This can be used for decoding a single binary block-compressed source as well as decoding at vertex points for the binary Slepian-Wolf problem. All three of these innovations were motivated by recent analogous results in the channel coding domain.