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IEEE International Symposium on Information Theory (ISIT)

Performance evaluation of faulty iterative decoders using absorbing Markov chains

Predrag Ivaniš, Bane V. Vasic, David Declercq

Published Jul 1, 2016• 4 citations
Publisher

Abstract

We propose an iterative decoder made of a combination of faulty and perfect logic gates that is capable of correcting more channel errors than its counterpart made completely of perfect logic gates. We present an error probability analysis based on absorbing Markov chains, and explain how the randomness in the check node update function helps a decoder to escape to local minima associated with trapping sets. For the (155, 64) Tanner low-density parity check code, we provide a range of gate failure probabilities for which imperfect decoders perform better.

DOI
10.1109/isit.2016.7541562

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We propose an iterative decoder made of a combination of faulty and perfect logic gates that is capable of correcting more channel errors than its counterpart made completely of perfect logic gates. We present an error probability analysis based on absorbing Markov chains, and explain how the randomness in the check node update function helps a decoder to escape to local minima associated with trapping sets. For the (155, 64) Tanner low-density parity check code, we provide a range of gate failure probabilities for which imperfect decoders perform better.
A majority logic decoder made of unreliable logic gates, whose failures are transient and data-dependent, is analyzed. Based on a combinatorial representation of fault configurations a closed-form expression for the average bit error rate for a one-step majority logic decoder is derived, for a regular low-density parity-check (LDPC) code ensemble and the proposed failure model. The presented analysis framework is then used to establish bounds on the one-step majority logic decoder performance under the simplified probabilistic gate-output switching model. Based on the expander property of Tanner graphs of LDPC codes, it is proven that a version of the faulty parallel bit-flipping decoder can correct a fixed fraction of channel errors in the presence of data-dependent gate failures. The results are illustrated with numerical examples of finite geometry codes.
We propose a gradient descent type bit flipping algorithm for decoding low density parity check codes on the binary symmetric channel. Randomness introduced in the bit flipping rule makes this class of decoders not only superior to other decoding algorithms of this type, but also robust to logic-gate failures. We report a surprising discovery that for a broad range of gate failure probability our decoders actually benefit from faults in logic gates which serve as an inherent source of randomness and help the decoding algorithm to escape from local minima associated with trapping sets.
In this paper we propose an analytical method to evaluate the performance of one step majority logic decoders constructed from faulty gates. We analyze the decoder under the assumption that the gates fail independently. We calculate the average bit error probability of such a decoder and apply the method to the special case of projective geometry codes. The method, however, applies to any regular low-density parity-check code of girth at least six but the calculations are much simpler for the projective geometry codes. We present results for the bit error rate performance of four codes from projective planes
Absorbing sets are graphical structures that cause iterative decoders to fail. The sets depend on the particular iterative decoding algorithm. In this paper, we consider a variant of the wellstudied Gallager B decoding algorithm for binary low-density parity-check codes. Here, a bit node updates its value according to either the number of or fraction of unsatisfied neighboring check nodes. The traditional Gallager B algorithm is the special case in which half (meaning a fraction of ½) of the check nodes being unsatisfied is required for a bit to change its value. We study absorbing sets in these settings along with the received words which give decoder failure. We determine the how changing the update rules pick up new absorbing sets. This allows for fine-tuning the standard algorithm according to the particular channel or error probability. We also connect these notions to Boolean functions.
This paper contains a survey on iterative decoders of low-density parity-check (LDPC) codes made of unreliable logic gates that are capable to provide lower probability of error, when compared to their perfectly reliable counterparts. We have recently shown that the error-floor performance of message-passing decoders can be improved, if randomness that exists in unreliable logic gates is incorporated into decoding deliberately, without any complexity cost. Furthermore, we have shown that controlling the level of unreliability enable us to exploit the stochastic resonance phenomenon, previously observed in theoretical physics, electronic and magnetic systems. In contrary to common belief, we have shown that for a narrow range of gate failure probability the overall decoding performance is dramatically increased. In this paper, we show that the effect of stochastic resonance is even more noticeable for the case of the gradient descent bit-flipping (GDBF) algorithm. This decoder combines the simplest iterative decoding algorithm with gradient descent optimization, making it an attractive solution for a variety of low complexity storage systems, or code-based cryptosystems. In addition, we show that getting the most of the stochastic resonance is essentially a deep learning problem, since setting the levels of unreliability for individual parts of the decoder by a training process is a step toward incorporating the machine learning techniques into design and analysis of iterative decoders of LDPC codes.
Ever since the publication of Shannon's seminal work in 1948, the search for capacity achieving codes has led to many interesting discoveries in channel coding theory. Low-density parity-check (LDPC) codes originally proposed in 1963 were largely forgotten and rediscovered recently. The significance of LDPC codes lies in their capacity approaching performance even when decoded using low complexity sub-optimal decoding algorithms. Iterative decoders are one such class of decoders that work on a graphical representation of a code known as the Tanner graph. Their properties have been well understood in the asymptotic limit of the code length going to infinity. However, the behavior of various decoders for a given finite length code remains largely unknown.An understanding of the failures of the decoders is vital for the error floor analysis of a given code. Broadly speaking, error floor is the abrupt degradation in the frame error rate (FER) performance of a code in the high signal-to-noise ratio domain. Since the error floor phenomenon manifests in the regions not reachable by Monte-Carlo simulations, analytical methods are necessary for characterizing the decoding failures. In this work, we consider hard decision decoders for transmission over the binary symmetric channel (BSC).For column-weight-three codes, we provide tight upper and lower bounds on the guaranteed error correction capability of a code under the Gallager A algorithm by studying combinatorial objects known as trapping sets. For higher column weight codes, we establish bounds on the minimum number of variable nodes that achieve certain expansion as a function of the girth of the underlying Tanner graph, thereby obtaining lower bounds on the guaranteed error correction capability. We explore the relationship between a class of graphs known as cage graphs and trapping sets to establish upper bounds on the error correction capability.We also propose an algorithm to identify the most probable noise configurations, also known as instantons, that lead to error floor for linear programming (LP) decoding over the BSC. With the insight gained from the above analysis techniques, we propose novel code construction techniques that result in codes with superior error floor performance.