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arXiv:1501.02483

Design of LDPC Codes Robust to Noisy Message-Passing Decoding

Alla Tarighati, Hamed Farhadi, Farshad Lahouti

Published Jan 11, 2015• 1 citation
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Abstract

We address noisy message-passing decoding of lowdensity parity-check (LDPC) codes over additive white Gaussian noise channels. Message-passing decoders in which certain processing units iteratively exchange messages are common for decoding LDPC codes. The exchanged messages are in general subject to internal noise in hardware implementation of these decoders. We model the internal decoder noise as additive white Gaussian noise (AWGN) degrading exchanged messages. Using Gaussian approximation of the exchanged messages, we perform a two-dimensional density evolution analysis for the noisy LDPC decoder. This makes it possible to track both the mean, and the variance of the exchanged message densities, and hence, to quantify the threshold of the LDPC code in the presence of internal decoder noise. The numerical and simulation results are presented that quantify the performance loss due to the internal decoder noise. To partially compensate this performance loss, we propose a simple method, based on EXIT chart analysis, to design robust irregular LDPC codes. The simulation results indicate that the designed codes can indeed compensate part of the performance loss due to the internal decoder noise.

DOI
10.48550/arxiv.1501.02483

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We address noisy message-passing decoding of lowdensity parity-check (LDPC) codes over additive white Gaussian noise channels. Message-passing decoders in which certain processing units iteratively exchange messages are common for decoding LDPC codes. The exchanged messages are in general subject to internal noise in hardware implementation of these decoders. We model the internal decoder noise as additive white Gaussian noise (AWGN) degrading exchanged messages. Using Gaussian approximation of the exchanged messages, we perform a two-dimensional density evolution analysis for the noisy LDPC decoder. This makes it possible to track both the mean, and the variance of the exchanged message densities, and hence, to quantify the threshold of the LDPC code in the presence of internal decoder noise. The numerical and simulation results are presented that quantify the performance loss due to the internal decoder noise. To partially compensate this performance loss, we propose a simple method, based on EXIT chart analysis, to design robust irregular LDPC codes. The simulation results indicate that the designed codes can indeed compensate part of the performance loss due to the internal decoder noise.
A noisy message-passing decoding scheme isconsidered for low-density parity-check (LDPC) codes overadditive white Gaussian noise (AWGN) channels. The internaldecoder noise is motivated by the quantization noise in digitalimplementations or the intrinsic noise of analog LDPC decoders.We modelled the decoder noise as AWGN on the exchangedmessages in the iterative LDPC decoder. This is shown to renderthe message densities in the noisy LDPC decoder inconsistent.We then invoke Gaussian approximation and formulate a twodimensionaldensity evolution analysis for the noisy LDPCdecoder. This allows for not only tracking the mean, but also thevariance of the message densities, and hence, quantifying thethreshold of the LDPC code. According to the results, a decodernoise of unit variance, increases the threshold for a regular (3,6) code by 1.672dB.
We introduce a new one-dimensional (1-D) analysis of low-density parity-check (LDPC) codes on additive white Gaussian noise channels which is significantly more accurate than similar 1-D methods. Our method assumes a Gaussian distribution in message-passing decoding only for messages from variable nodes to check nodes. Compared to existing work, which makes a Gaussian assumption both for messages from check nodes and from variable nodes, our method offers a significantly more accurate estimate of convergence behavior and threshold of convergence. Similar to previous work, the problem of designing irregular LDPC codes reduces to a linear programming problem. However, our method allows irregular code design in a wider range of rates without any limit on the maximum variable-node degree. We use our method to design irregular LDPC codes with rates greater than 1/4 that perform within a few hundredths of a decibel from the Shannon limit. The designed codes perform almost as well as codes designed by density evolution.
This dissertation presents new methods for the analysis, design and decoding of low-density parity-check (LDPC) codes. We start by studying the simplest class of decoders: the binary message-passing (BMP) decoders. We show that the optimum BMP decoder must satisfy certain symmetry and isotropy conditions, and prove that Gallager's Algorithm B is the optimum BMP algorithm. We use a generalization of extrinsic information transfer (EXIT) charts to formulate a linear program that leads to the design of highly efficient irregular LDPC codes for the BMP decoder. We extend this approach to the design of irregular LDPC codes for the additive white Gaussian noise channel. We introduce a "semi-Gaussian" approximation that very accurately predicts the behaviour of the decoder and permits code design over a wider range of rates and code parameters than in previous approaches. We then study the EXIT chart properties of the highest rate LDPC code which guarantees a certain convergence behaviour. We also introduce and analyze gear-shift decoding in which the decoder is permitted to select the decoding rule from among a predefined set. We show that this flexibility can give rise to significant reductions in decoding complexity. Finally, we show that binary LDPC codes can be combined with quadrature amplitude modulation to achieve near-capacity performance in a multitone system over frequency selective Gaussian channels.
Density evolution is an algorithm for computing the capacity of low-density parity-check (LDPC) codes under message-passing decoding. For memoryless binary-input continuous-output additive white Gaussian noise (AWGN) channels and sum-product decoders, we use a Gaussian approximation for message densities under density evolution to simplify the analysis of the decoding algorithm. We convert the infinite-dimensional problem of iteratively calculating message densities, which is needed to find the exact threshold, to a one-dimensional problem of updating the means of the Gaussian densities. This simplification not only allows us to calculate the threshold quickly and to understand the behavior of the decoder better, but also makes it easier to design good irregular LDPC codes for AWGN channels. For various regular LDPC codes we have examined, thresholds can be estimated within 0.1 dB of the exact value. For rates between 0.5 and 0.9, codes designed using the Gaussian approximation perform within 0.02 dB of the best performing codes found so far by using density evolution when the maximum variable degree is 10. We show that by using the Gaussian approximation, we can visualize the sum-product decoding algorithm. We also show that the optimization of degree distributions can be understood and done graphically using the visualization.
In traditional communication theory, it is assumed that error correcting codes may be decoded with algorithms that perform perfectly. Noise, however, provides a fundamental limit to computation systems just as it does to communication systems. In this work, we investigate the effect of noise in message-passing decoders for low-density parity-check codes. We observe that the concentration of the performance of the decoder around its average performance continues to hold when noise is introduced into message-passing. Given the concentration result, density evolution equations for a simple noisy message-passing decoder are derived. Analytic computation of thresholds shows that performance degrades smoothly as decoder noise increases. Decoding is robust to noise in the decoder.
In this paper, we are concerned with the finite-length analysis of low-density parity-check (LDPC) codes when used over additive white Gaussian noise (AWGN) channels. We present the iterative decoding algorithm of LDPC codes, and we also discuss the encoding methods and the bipartite graph representation of LDPC codes ensemble. Finite-length LDPC code performances are investigated, and the influence of cycle is pointed out. Simulation results show that irregular LDPC codes with variable and check node degree distribution optimized for infinite-length codes are not optimal for finite-length LDPC codes, and the error patterns of both regular and irregular codes are investigated as an effort to improve the performance and design methodology of finite-length LDPC codes.