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An Exhaustive Tree Search for Stopping Sets of LDPC Codes

Martin Tomlinson, Cen Jung Tjhai, Marcel Ambroze, Mohammed Zaki Ahmed, Mubarak Jibril

Published Jan 1, 2017• 0 citations
Publisher

Abstract

The indicative performance of an LDPC code may be determined from exhaustive analysis of the low-weight spectral terms of the code’s stopping sets which by definition includes the low-weight codewords. In a landmark paper, Rosnes and Ytrehus showed that exhaustive, low-weight stopping set analysis of codes whose parity-check matrix is sparse can be carried out using a bounded tree search over the length of the code. For an (n, k) code, the potential total search space is of size $$2^n$$ but a good choice of bound dramatically reduces this search space to a practical size. Indeed, the choice of bound is critical to the success of the algorithm. It is shown in this chapter that an improved algorithm can be obtained if the bounded tree search is applied to a set of k information bits since the potential total search space is reduced to size $$2^k$$ . Since such a restriction will only find codewords and not all stopping sets, a class of bits is defined as unsolved parity bits, and these are also searched as appended bits in order to find all low-weight stopping sets. Weight spectrum and stopping set results are presented for commonly used WiMax LDPC codes in addition to some other well known LDPC codes.

DOI
10.1007/978-3-319-51103-0_13

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The indicative performance of an LDPC code may be determined from exhaustive analysis of the low-weight spectral terms of the code’s stopping sets which by definition includes the low-weight codewords. In a landmark paper, Rosnes and Ytrehus showed that exhaustive, low-weight stopping set analysis of codes whose parity-check matrix is sparse can be carried out using a bounded tree search over the length of the code. For an (n, k) code, the potential total search space is of size 2n2^n2n but a good choice of bound dramatically reduces this search space to a practical size. Indeed, the choice of bound is critical to the success of the algorithm. It is shown in this chapter that an improved algorithm can be obtained if the bounded tree search is applied to a set of k information bits since the potential total search space is reduced to size 2k2^k2k . Since such a restriction will only find codewords and not all stopping sets, a class of bits is defined as unsolved parity bits, and these are also searched as appended bits in order to find all low-weight stopping sets. Weight spectrum and stopping set results are presented for commonly used WiMax LDPC codes in addition to some other well known LDPC codes.
In an earlier transactions paper, Rosnes and Ytrehus presented an efficient algorithm for determining all stopping sets of low-density parity-check (LDPC) codes, up to a specified weight, and also gave results for a number of well-known codes including the family of IEEE 802.16e LDPC codes, commonly referred to as the WiMax codes. It is the purpose of this short paper to review the algorithm for determining the initial part of the stopping set weight spectrum (which includes the codeword weight spectrum), and to provide some improvements to the algorithm. As a consequence, complete stopping set weight spectra up to weight 32 (for selected IEEE 802.16e LDPC codes) can be provided, while in previous work only stopping set weights up to 28 are reported. In the published standard for the IEEE 802.16e codes there are two methods of construction presented, depending upon the code rate and the code length. We compare the stopping sets of the resulting codes and provide complete stopping set weight spectra (up to five terms) for all IEEE 802.16e LDPC codes using both construction methods.
It is proved in this work that exhaustively determining bad patterns in arbitrary, finite low-density parity-check (LDPC) codes, including stopping sets for binary erasure channels (BECs) and trapping sets (also known as near-codewords) for general memoryless symmetric channels, is an NP-complete problem, and efficient algorithms are provided for codes of practical short lengths n~=500. By exploiting the sparse connectivity of LDPC codes, the stopping sets of size <=13 and the trapping sets of size <=11 can be efficiently exhaustively determined for the first time, and the resulting exhaustive list is of great importance for code analysis and finite code optimization. The featured tree-based narrowing search distinguishes this algorithm from existing ones for which inexhaustive methods are employed. One important byproduct is a pair of upper bounds on the bit-error rate (BER) & frame-error rate (FER) iterative decoding performance of arbitrary codes over BECs that can be evaluated for any value of the erasure probability, including both the waterfall and the error floor regions. The tightness of these upper bounds and the exhaustion capability of the proposed algorithm are proved when combining an optimal leaf-finding module with the tree-based search. These upper bounds also provide a worst-case-performance guarantee which is crucial to optimizing LDPC codes for extremely low error rate applications, e.g., optical/satellite communications. Extensive numerical experiments are conducted that include both randomly and algebraically constructed LDPC codes, the results of which demonstrate the superior efficiency of the exhaustion algorithm and its significant value for finite length code optimization.
In this work, we introduce an efficient algorithm to find all stopping sets, of size less than some threshold, of a fixed low-density parity-check (LDPC) matrix. The solution is inspired by the algorithm proposed by Rosnes and Ytrehus in 2005 to find an exhaustive list of all small-size turbo stopping sets in a turbo code. The efficiency of the proposed algorithm is demonstrated by several numerical examples. For instance, we have applied the algorithm to the well-known (3, 5)-regular (155, 64) Tanner code and found all stopping sets of size at most 18 in about 1 min on a standard desktop computer. Also, we have verified that the minimum stopping set size of the (4896, 2474) Ramanujan-Margulis code is indeed 24, and that the corresponding multiplicity is exactly 204. Furthermore, we have applied the algorithm to the IEEE 802.16e LDPC codes and determined the minimum stopping set size and the corresponding multiplicity exactly for these codes. Finally, as an application, we present a greedy algorithm to find a small number of redundant parity checks to add to the original parity-check matrix in order to remove all stopping sets in the corresponding Tanner graph of size less than the minimum distance. An extensive case study of the (155, 64) Tanner code illustrates the usefulness of the algorithm, and we present a 110 times 155 redundant parity-check matrix for this code with no stopping sets of size less than the minimum distance. Simulation results of iterative decoding on the binary erasure channel show performance improvements for low-to-medium erasure probabilities when this redundant parity-check matrix is used for decoding.
In this work, we introduce an efficient algorithm to find all stopping sets of size less than some threshold of a fixed low-density parity-check (LDPC) matrix. The solution is inspired by the algorithm proposed by Rosnes and Ytrehus in 2005 to find an exhaustive list of all small-size turbo stopping sets in a turbo code. The efficiency of the proposed algorithm is demonstrated by several numerical examples. For instance, we have applied the algorithm to the well-known (3, 5)-regular (155,64) Tanner code and found all stopping sets of size at most 18 in about one minute on a standard desktop computer. Also, we have verified that the minimum stopping set size of the (4896,2474) Ramanujan-Margulis code is indeed 24, and that the corresponding multiplicity is exactly 204.
A maximum-likelihood decoding algorithm inspired by information set decoding is realised for LDPC codes or linear block codes with sparse parity-check matrices of moderate codeword lengths over the AWGN channel. The extension involves an exhaustive branch and bound tree-search based algorithm for finding all small Euclidean distance error vectors. It provides an approach of searching on the bounded n-bit positions, which involves 2kcombinatorial dual codes based on the corresponding parity-check matrix, to achieve the near-optimum decoding output with attainable computational complexity. Soft decision decoding results are presented for some well-known LDPC codes demonstrating near-optimum maximum-likelihood performance.
Assuming iterative decoding for binary erasure channels (BECs), a novel tree-based technique for upper bounding the bit error rates (BERs) of arbitrary, finite low-density parity-check (LDPC) codes is provided and the resulting bound can be evaluated for all operating erasure probabilities, including both the waterfall and the error floor regions. This upper bound can also be viewed as a narrowing search of stopping sets, which is an approach different from the stopping set enumeration used for lower bounding the error floor. When combined with optimal leaf-finding modules, this upper bound is guaranteed to be tight in terms of the asymptotic order. The Boolean framework proposed herein further admits a composite search for even tighter results. For comparison, a refinement of the algorithm is capable of exhausting all stopping sets of size les 13 for irregular LDPC codes of length n ap 500, which requires (13500) ap 1.67 times 1025trials if a brute force approach is taken. These experiments indicate that this upper bound can be used both as an analytical tool and as a deterministic worst-performance (error floor) guarantee, the latter of which is crucial to optimizing LDPC codes for extremely low BER applications, e.g., optical/satellite communications