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IEEE Transactions on Communications

Joint Channel-State Estimation and Decoding of Low-Density Parity-Check Codes on the Two-State Noiseless/Useless BSC Block Interference Channel

Published Mar 1, 2005• 0 citations
Publisher

Abstract

Joint Channel-State Estimation and Decoding of Low-Density Parity-Check Codes on the Two-State Noiseless/Useless BSC Block Interference Channel We apply the density-evolution technique to determine the thresholds of low-density parity-check (LDPC) codes when the sum-product algorithm is employed to perform joint channel-state estimation and decoding. The channel considered is the two-state noiseless/useless binary symmetric channel (BSC) block interference channel, where a block of $h$ consecutive symbols shares the same channel state, which is either a noiseless BSC (crossover probability 0) or a useless BSC (crossover probability 1/2). The channel state is selected independently and at random from block to block, according to a known prior distribution. The threshold of the joint channel-state estimation/decoding scheme when used over such a channel is shown to be greatly superior to that of a decoder that makes no attempt to estimate the channel state. These results are also confirmed by simulation. The maximum-likelihood performance of LDPC codes when used over this channel is investigated. Lower bounds on the error exponents of regular LDPC codes, when maximum-likelihood decoded, are shown to be close to the random coding channel-error exponent when the LDPC variable node degree is high.

DOI
10.1109/tcomm.2005.843408

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Joint Channel-State Estimation and Decoding of Low-Density Parity-Check Codes on the Two-State Noiseless/Useless BSC Block Interference Channel We apply the density-evolution technique to determine the thresholds of low-density parity-check (LDPC) codes when the sum-product algorithm is employed to perform joint channel-state estimation and decoding. The channel considered is the two-state noiseless/useless binary symmetric channel (BSC) block interference channel, where a block of hhh consecutive symbols shares the same channel state, which is either a noiseless BSC (crossover probability 0) or a useless BSC (crossover probability 1/2). The channel state is selected independently and at random from block to block, according to a known prior distribution. The threshold of the joint channel-state estimation/decoding scheme when used over such a channel is shown to be greatly superior to that of a decoder that makes no attempt to estimate the channel state. These results are also confirmed by simulation. The maximum-likelihood performance of LDPC codes when used over this channel is investigated. Lower bounds on the error exponents of regular LDPC codes, when maximum-likelihood decoded, are shown to be close to the random coding channel-error exponent when the LDPC variable node degree is high.
We apply the density-evolution technique to determine the thresholds of low-density parity-check (LDPC) codes when the sum-product algorithm is employed to perform joint channel-state estimation and decoding. The channel considered is the two-state noiseless/useless binary symmetric channel (BSC) block interference channel, where a block of h consecutive symbols shares the same channel state, which is either a noiseless BSC (crossover probability 0) or a useless BSC (crossover probability 1/2). The channel state is selected independently and at random from block to block, according to a known prior distribution. The threshold of the joint channel-state estimation/decoding scheme when used over such a channel is shown to be greatly superior to that of a decoder that makes no attempt to estimate the channel state. These results are also confirmed by simulation. The maximum-likelihood (ML) performance of LDPC codes when used over this channel is investigated. Lower bounds on the error exponents of regular LDPC codes, when ML decoded, are shown to be close to the random coding channel error exponent when the LDPC variable node degree is high.
Effect of channel mismatch on decoding performance of low density parity check (LDPC) codes over binary symmetric channel (BSC) under sum-product algorithm (SPA) is analyzed in this paper. Density evolution is employed in theoretical analysis to get optimal initial value for SPA. Based on the results, a modified SPA algorithm is proposed. When decoding using this modified SPA algorithm, no channel information is needed at the decoder side. This will be of significant value for time-varying channels. Theoretical claims from density evolution are confirmed by extensive simulations. Our simulations show that by fixing the initial value for all channel conditions, decoding performance can be improved. One channel state estimation algorithm is proposed in this paper based on the error detection ability of LDPC codes. Maximum number of iterations taken when decoding a LDPC code can be dynamically adjusted according to the estimated channel state. Further performance improvements can be achieved by using the dynamic iteration number adjusting scheme together with the modified sum-product algorithm
A method for estimating the performance of low-density parity-check (LDPC) codes decoded by hard-decision iterative decoding algorithms on binary symmetric channels (BSC) is proposed. Based on the enumeration of the smallest weight error patterns that cannot be all corrected by the decoder, this method estimates both the frame error rate (FER) and the bit error rate (BER) of a given LDPC code with very good precision for all crossover probabilities of practical interest. Through a number of examples, we show that the proposed method can be effectively applied to both regular and irregular LDPC codes and to a variety of hard-decision iterative decoding algorithms. Compared with the conventional Monte Carlo simulation, the proposed method has a much smaller computational complexity, particularly for lower error rates.
A method for estimating the performance of low-density parity-check (LDPC) codes decoded by hard-decision iterative decoding algorithms on binary symmetric channels (BSCs) is proposed. Based on the enumeration of the smallest weight error patterns that cannot be all corrected by the decoder, this method estimates both the frame error rate (FER) and the bit error rate (BER) of a given LDPC code with very good precision for all crossover probabilities of practical interest. Through a number of examples, we show that the proposed method can be effectively applied to both regular and irregular LDPC codes and to a variety of hard-decision iterative decoding algorithms. Compared with the conventional Monte Carlo simulation, the proposed method has a much smaller computational complexity, particularly for lower error rates.
Low-Density Parity-Check code (LDPC) is a kind of linear block error-correcting codes.Sum-product algorithm is an iterative decoding algorithms commonly used in LDPC code decoding.This paper studies into the LDPC code message passing iterative decoding algorithm under the binary symmetric channel (BSC) and simulates the bit error probability,then simulates the convergence of check nodes' and variable nodes' probability density in the iterative process using the method of density evolution.The result show that under a given channel threshold,LDPC code has a good correction performance.
The performance of maximum-likelihood (ML) decoding on the binary erasure channel for finite-length low-density parity-check (LDPC) codes from two random ensembles is studied. A tightened union-type upper bound on the ML decoding error probability based on the precise coefficients of the average weight spectrum is presented. For LDPC codes from the Gallager ensemble and the Richardson-Urbanke ensemble, new upper bounds on the ML decoding performance based on computing the rank of submatrices of the code parity-check matrix are derived. A new lower bound on the ML decoding threshold followed from the latter error probability bound is obtained. An improved lower bound on the error probability for codes with a known estimate on the minimum distance is presented as well. A new low-complexity near-ML decoding algorithm for quasi-cyclic LDPC codes is proposed and simulated. Its performance is compared to the simulated belief propagation and ML decoding performance and simulated performance of the best known improved iterative decoding techniques, as well as, with the derived upper bounds on the ML decoding performance and with decoding thresholds obtained by the density evolution technique.