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arXiv:1904.05396 · IEEE Information Theory Workshop (ITW)

Covariance Evolution for Spatially “Mt. Fuji” Coupled LDPC Codes

Yuta Nakahara, Toshiyasu Matsushima

Published Aug 1, 2019• 2 citations
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Abstract

A spatially “Mt. Fuji” coupled low-density parity check (LDPC) ensemble is a modified version of the original spatially coupled (SC) LDPC ensemble. Its desirable properties are first observed in experimentally. The decoding error probability in the error floor region over the binary erasure channel (BEC) is theoretically analyzed later. In this paper, as the last piece of the theoretical analysis over the BEC, we analyze the decoding error probability in the waterfall region by modifying the covariance evolution which has been used to analyze the original SC-LDPC ensemble.

DOI
10.1109/itw44776.2019.8989366

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IEEE International Symposium on Information Theory (ISIT)•Jul 1, 2012•2 citations
A spatially “Mt. Fuji” coupled low-density parity check (LDPC) ensemble is a modified version of the original spatially coupled (SC) LDPC ensemble. Its desirable properties are first observed in experimentally. The decoding error probability in the error floor region over the binary erasure channel (BEC) is theoretically analyzed later. In this paper, as the last piece of the theoretical analysis over the BEC, we analyze the decoding error probability in the waterfall region by modifying the covariance evolution which has been used to analyze the original SC-LDPC ensemble.
A spatially “Mt. Fuji” coupled (SFC) low-density parity-check (LDPC) ensemble is a modified version of the spatially coupled (SC) LDPC ensemble. Its decoding error probability in the waterfall region has been studied only in an experimental manner. In this paper, we theoretically analyze it over the binary erasure channel by modifying the expected graph evolution (EGE) and covariance evolution (CE) that have been used to analyze the original SC-LDPC ensemble. In particular, we derive the initial condition modified for the SFC-LDPC ensemble. Then, unlike the SC-LDPC ensemble, the SFC-LDPC ensemble has a local minimum on the solution of the EGE and CE. Considering the property of it, we theoretically expect the waterfall curve of the SFC-LDPC ensemble is steeper than that of the SC-LDPC ensemble. In addition, we also confirm it by numerical experiments.
A new type of spatially coupled low density parity check (SCLDPC) code is proposed. This code has two benefits. (1) This code requires less number of iterations to correct the erasures occurring through the binary erasure channel in the waterfall region than that of the usual SCLDPC code. (2) This code has lower error floor than that of the usual SCLDPC code. Proposed code is constructed as a coupled chain of the underlying LDPC codes whose code lengths exponentially increase as the position where the codes exist is close to the middle of the chain. We call our code spatially “Mt. Fuji” coupled LDPC (SFCLDPC) code because the shape of the graph representing the code lengths of underlying LDPC codes at each position looks like Mt. Fuji. By this structure, when the proposed SFCLDPC code and the original SCLDPC code are constructed with the same code rate and the same code length, L (the number of the underlying LDPC codes) of the proposed SFCLDPC code becomes smaller and M (the code lengths of the underlying LDPC codes) of the proposed SFCLDPC code becomes larger than those of the SCLDPC code. These properties of L and M enables the above reduction of the number of iterations and the bit error rate in the error floor region, which are confirmed by the density evolution and computer simulations.
Spatially “Mt. Fuji” coupled (SFC) low density parity check (LDPC) codes are constructed as a chain of block LDPC codes. A difference between the SFC-LDPC codes and the original spatially coupled (SC) LDPC codes is code lengths of the underlying block LDPC codes. The code length of the block LDPC code at the middle of the chain is larger than that at the end of the chain. It is experimentally confirmed that the bit error probability in the error floor region of the SFC-LDPC code is lower than that of the SC-LDPC code whose code length and design rate are the same as those of the SFC-LDPC code. In this letter, we calculate the weight distribution of the SFC-LDPC code and try to explain causes of the low bit error rates of the SFC-LDPC code.
Low-density parity-check (LDPC) codes are linear codes defined by sparse parity check matrices.Due to the sparseness of the parity check matrices, LDPC codes are efficiently decoded by the belief propagation (BP) decoder.It is known that non-binary LDPC codes can outperform binary ones.However, the decoding complexity grows with the size of non-binary alphabets.Hence, there is a trade-off between performance and complexity.In this dissertation, we focus on both binary and non-binary LDPC codes.We analyze the decoding error probability for finite length LDPC codes under BP decoding.The curve of the decoding error probability for finite length LDPC codes, as a function of channel error probability, is divided into two regions called waterfall region and error floor region.We analyze the decoding error probability in the waterfall region for binary LDPC codes and in the error floor region for non-binary LDPC codes.The main results of this dissertation are summarized as follows:• We analyze the decoding erasure probabilities of waterfall regions for binary LDPC codes over binary erasure channels (BECs) without any assumptions by analytically solving the covariance evolution.• We propose a method to lower the decoding error rates in the error floors of non-binary LDPC codes defined over Galois field and general linear group transmitted over the BEC, the memoryless binary-input output-symmetric (MBIOS) channel and q-ary memoryless symmetric (q-MS) channel.Simulation results show that the decoding error rates of the codes designed by the proposed method outperform those of the codes designed by the conventional method proposed by Poulliat et al.• We give lower bounds of decoding error rates in the error floor regions for non-binary LDPC codes over the BEC, MBIOS channel and q-MS channel.Simulation results show that those lower bounds are tight.Moreover, we show that the decoding error rates for non-binary LDPC codes defined over general linear group have same decoding performance in the error floor regions with that for non-binary LDPC codes defined over Galois field.Furthermore, we show that this tight lower bound monotonically decreases, as the order of Galois field of non-binary LDPC code increases in the BEC and binary additive white Gaussian noise channel.iii • We derive the weight distribution of the decoding error patterns in the BP decoder for non-binary LDPC codes defined over general linear group transmitted over the BEC.
The finite length performance of codes on graphs constructed by connecting spatially coupled low-density parity-check (SC-LDPC) code chains is analyzed. Successive (peeling) decoding is considered for the binary erasure channel (BEC). The evolution of the undecoded portion of the bipartite graph remaining after each iteration is analyzed as a dynamical system. It is shown that, in addition to superior iterative decoding thresholds, connected chain ensembles have better performance than single chain ensembles of the same rate and length.
In this work, we analyze the finite-length performance of low-density parity check (LDPC) ensembles decoded over the binary erasure channel (BEC) using the tree-expectation propagation (TEP) algorithm. In a previous paper, we showed that the TEP improves the BP performance for decoding regular and irregular short LDPC codes, but the perspective was mainly empirical. In this work, given the degree-distribution of an LDPC ensemble, we explain and predict the range of code lengths for which the TEP improves the BP solution. In addition, for LDPC ensembles that present a single critical point, we propose a scaling law to accurately predict the performance in the waterfall region. These results are of critical importance to design practical LDPC codes for the TEP decoder.