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IEEE Transactions on Information Theory

On Achievable Rates and Complexity of LDPC Codes Over Parallel Channels: Bounds and Applications

Igal Sason, Gil Wiechman

Published Jan 23, 2007• 21 citations
Publisher

Abstract

A variety of communication scenarios can be modeled by a set of parallel channels. Upper bounds on the achievable rates under maximum-likelihood (ML) decoding, and lower bounds on the decoding complexity per iteration of ensembles of low-density parity-check (LDPC) codes are presented. The communication of these codes is assumed to take place over statistically independent parallel channels where the component channels are memoryless, binary-input, and output-symmetric. The bounds are applied to ensembles of punctured LDPC codes where the puncturing patterns are either random or possess some structure. Our discussion is concluded by a diagram showing interconnections between the new theorems and some previously reported results.

DOI
10.1109/tit.2006.889462

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A variety of communication scenarios can be modeled by a set of parallel channels. Upper bounds on the achievable rates under maximum-likelihood (ML) decoding, and lower bounds on the decoding complexity per iteration of ensembles of low-density parity-check (LDPC) codes are presented. The communication of these codes is assumed to take place over statistically independent parallel channels where the component channels are memoryless, binary-input, and output-symmetric. The bounds are applied to ensembles of punctured LDPC codes where the puncturing patterns are either random or possess some structure. Our discussion is concluded by a diagram showing interconnections between the new theorems and some previously reported results.
This paper considers the achievable rates and decoding complexity of low-density parity-check (LDPC) codes over statistically independent parallel channels. The paper starts with the derivation of bounds on the conditional entropy of the transmitted codeword given the received sequence at the output of the parallel channels; the component channels are considered to be memoryless, binary-input, and output-symmetric (MBIOS). These results serve for the derivation of an upper bound on the achievable rates of ensembles of LDPC codes under optimal maximum-likelihood (ML) decoding when their transmission takes place over parallel MBIOS channels. The paper relies on the latter bound for obtaining upper bounds on the achievable rates of ensembles of randomly and intentionally punctured LDPC codes over MBIOS channels. The paper also provides a lower bound on the decoding complexity (per iteration) of ensembles of LDPC codes under message-passing iterative decoding over parallel MBIOS channels; the bound is given in terms of the gap between the rate of these codes for which reliable communication is achievable and the channel capacity. The paper presents a diagram which shows interconnections between the theorems introduced in this paper and some other previously reported results. The setting which serves for the derivation of the bounds on the achievable rates and decoding complexity is general, and the bounds can be applied to other scenarios which can be treated as different forms of communication over parallel channels.
The paper presents bounds on the achievable rates and the decoding complexity per iteration of low-density parity-check (LDPC) codes. It is assumed that the communication of these codes takes place over statistically independent parallel channels where these channels are memoryless, binary-input and output-symmetric. The bounds are applied to punctured LDPC codes. A diagram concludes our discussion by showing interconnections between the theorems in this paper and some previously reported results
We derive both upper and lower bounds on the decoding error probability of maximum-likelihood (ML) decoded low-density parity-check (LDPC) codes. The results hold for any binary-input symmetric-output channel. Our results indicate that for various appropriately chosen ensembles of LDPC codes, reliable communication is possible up to channel capacity. However, the ensemble averaged decoding error probability decreases polynomially, and not exponentially. The lower and upper bounds coincide asymptotically, thus showing the tightness of the bounds. However, for ensembles with suitably chosen parameters, the error probability of almost all codes is exponentially decreasing, with an error exponent that can be set arbitrarily close to the standard random coding exponent.
We derive bounds on the error probability of ML decoded LDPC (low density parity check) codes, for any binary-input symmetric-output channel. For appropriately chosen ensembles of LDPC codes, reliable communication is possible up to channel capacity. The lower and upper bounds coincide asymptotically, indicating a polynomially decreasing ensemble averaged error probability. For ensembles with suitably chosen parameters, the error probability of almost all codes is exponentially decreasing. Furthermore, the error exponent can be set arbitrarily close to the standard random coding exponent.
This paper first introduces an improved decoding algorithm for low-density parity-check (LDPC) codes over binary-input-output-symmetric memoryless channels. Then some fundamental properties of punctured LDPC codes are presented. It is proved that for any ensemble of LDPC codes, there exists a puncturing threshold. It is then proved that for any rates R1and R2satisfying 0121to R2resulting in asymptotically good codes for all rates R1lesRlesR2. Specifically, this implies that rates arbitrarily close to one are achievable via puncturing. Bounds on the performance of punctured LDPC codes are also presented. It is also shown that punctured LDPC codes are as good as ordinary LDPC codes. For BEC and arbitrary positive numbers R121lesRlesR2is shown. Based on the above observations, a method is proposed to design good punctured LDPC codes over a broad range of rates. Finally, it is shown that the results of this paper may be used for the proof of the existence of the capacity-achieving LDPC codes over binary-input-output-symmetric memoryless channels
This correspondence studies the performance of the iterative decoding of low-density parity-check (LDPC) code ensembles that have linear typical minimum distance and stopping set size. We first obtain a lower bound on the achievable rates of these ensembles over memoryless binary-input output-symmetric channels. We improve this bound for the binary erasure channel. We also introduce a method to construct the codes meeting the lower bound for the binary erasure channel. Then, we give upper bounds on the rate of LDPC codes with linear minimum distance when their right degree distribution is fixed. We compare these bounds to the previously derived upper bounds on the rate when there is no restriction on the code ensemble.