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IEEE Transactions on Communications

Decoding Binary Linear Block Codes Using Local Search

Morteza Esmaeili, A. Alampour, Thomas Aaron Gulliver

Published Jun 1, 2013• 6 citations
Publisher

Abstract

This paper presents a novel iterative hard decision decoding algorithm for binary linear block codes over a binary symmetric channel (BSC). The problem is formulated as a 0-1 integer programming problem which is known to be NP-hard. When the crossover probability c of the channel is known, the solution space of the decoding problem can be decreased to a sphere whose radius is related to c. Using the penalty function method, the problem is reformulated on this reduced solution space. Then an iterative multi-flip local search algorithm is designed to find the global solution of this decoding problem. For a code with minimum distance d, when the radius of the sphere is not greater than d-1/2, this algorithm has the maximum likelihood (ML) certificate property, i.e., if the decoder outputs a codeword, it is guaranteed to be the ML codeword. Compared to the probabilistic suboptimal iterative belief propagation (BP) decoder, this approach has lower complexity and better performance. Numerical results show that in terms of speed and performance the proposed decoding method outperforms BP decoding in the error floor region.

DOI
10.1109/tcomm.2013.041113.120057

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This paper presents a novel iterative hard decision decoding algorithm for binary linear block codes over a binary symmetric channel (BSC). The problem is formulated as a 0-1 integer programming problem which is known to be NP-hard. When the crossover probability c of the channel is known, the solution space of the decoding problem can be decreased to a sphere whose radius is related to c. Using the penalty function method, the problem is reformulated on this reduced solution space. Then an iterative multi-flip local search algorithm is designed to find the global solution of this decoding problem. For a code with minimum distance d, when the radius of the sphere is not greater than d-1/2, this algorithm has the maximum likelihood (ML) certificate property, i.e., if the decoder outputs a codeword, it is guaranteed to be the ML codeword. Compared to the probabilistic suboptimal iterative belief propagation (BP) decoder, this approach has lower complexity and better performance. Numerical results show that in terms of speed and performance the proposed decoding method outperforms BP decoding in the error floor region.
In this paper, we develop linear-programming (LP) decoding for multiple-access channels with binary linear codes. For single-user channels, LP decoding has attracted much attention in recent years as a good approximation to maximum-likelihood (ML) decoding. We demonstrate how the ML decoding problem for multiple-access channels with binary linear codes can be formulated as an LP problem. This is not straightforward, because the objective function of the problem is generally a non-linear function of the codeword symbols. We introduce auxiliary variables such that the objective function is a linear function of these variables. The ML decoding problem then reduces to the LP problem. As in the case for single-user channels, we formulate the relaxed LP problem to reduce the complexity for practical implementation, and as a result propose a decoder that has the desirable property known as the ML certificate property (i.e., if the decoder outputs an integer solution, the solution is guaranteed to be the ML codeword). Although the computational complexity of the proposed algorithm is exponential in the number of users, we can reduce this complexity for Gaussian multiple-access channels. Furthermore, we compare the performance of the proposed decoder with a decoder based on the sum-product algorithm.
The problem of low-complexity linear programming (LP) decoding of nonbinary low-density parity-check (LDPC) codes is considered, and an iterative LP decoding algorithm is presented. Results that were previously derived for binary LDPC codes are extended to the nonbinary case. Both simple and generalized nonbinary LDPC codes are considered. It is shown how the algorithm can be implemented efficiently using a finite-field fast Fourier transform. Then, the convergence rate of the algorithm is analyzed. The complexity of the algorithm scales linearly in the block length, and it can approximate, up to an arbitrarily small relative error, the objective function of the exact LP solution. When applied to a typical code from an appropriate nonbinary LDPC code ensemble, the algorithm can correct a constant fraction of errors in linear (in the block length) computational complexity. Computer experiments with the new iterative LP decoding algorithm show that, in the error floor region, it can have better performance compared to belief propagation decoding, with similar computational requirements.
We consider linear programming (LP) decoding of a fixed low-density parity-check (LDPC) code over the binary symmetric channel (BSC). The LP decoder fails when it outputs a pseudo-codeword which is not a codeword. We propose an efficient algorithm termed the instanton search algorithm (ISA) which, given a random input, generates a set of flips called the BSC-instanton and prove that: (a) the LP decoder fails for any set of flips with support vector including an instanton; (b) for any input, the algorithm outputs an instanton in the number of steps upper-bounded by twice the number of flips in the input. We obtain the number of unique instantons of different sizes by running the ISA sufficient number of times. We then use the instanton statistics to predict the performance of the LP decoding over the BSC in the error floor region. We also propose an efficient semi-analytical method to predict the performance of LP decoding over a large range of transition probabilities of the BSC.
A lower bound on the error rate of linear binary block codes(under maximum likelihood decoding) over BSC channels is proposed.According to the principle of the maximum likelihood(ML) decoding algorithm,the decoding error probability is firstly converted into the joint probability of the error events,and the judge rule of the redundant error events is deduced based on the optimization rule of the improved Dawson-Sankoff bound.Moreover,the calculation expression about lower bound of the error probability solely depends on the Hamming weight enumerator function of the code and the crossover probability of the channel.The simulation results applying to various LDPC codes show that the new lower bound outperforms those generic lower bounds and the sphere packing bound.Its computational complexity is also lower.
We consider linear programming (LP) decoding of a fixed low-density parity-check (LDPC) code over the binary symmetric channel (BSC). The LP decoder fails when it outputs a pseudo-codeword which is not a codeword. We propose an efficient algorithm termed the instanton search algorithm (ISA) which, given a random input, generates a set of flips called the BSC-instanton and prove that: (a) the LP decoder fails for any set of flips with support vector including an instanton; (b) for any input, the algorithm outputs an instanton in the number of steps upper-bounded by twice the number of flips in the input. We obtain the number of unique instantons of different sizes by running the ISA sufficient number of times. We then use the instanton statistics to predict the performance of the LP decoding over the BSC in the error floor region. We also propose an efficient semi-analytical method to predict the performance of LP decoding over a large range of transition probabilities of the BSC.
Maximum-likelihood (ML) decoding of linear block codes on a symmetric channel is studied. Exact ML decoding is known to be computationally difficult. We propose an algorithm that finds the exact solution to the ML decoding problem by performing a depth-first search on a tree. The tree is designed from the code generator matrix and pruned based on the statistics of the channel noise. The complexity of the algorithm is a random variable. We characterize the complexity by means of its first moment, which for binary symmetric channels we find in closed-form. The obtained results indicate that the expected complexity of the algorithm is low over a wide range of system parameters.