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Bounds on Error-Correction Coding Performance

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

Published Jan 1, 2017• 2 citations
Publisher

Abstract

The theoretical performance of binary codes for the additive white Gaussian noise (AWGN) channel is discussed. In particular, Gallager’s coding theorem for binary codes is treated extensively. By assuming a binomial weight distribution for linear codes, it is shown that the decoder error probability performance of some of the best, known linear, binary codes is the same as the average performance of the ensemble of all randomly chosen, binary nonlinear codes having the same length and dimension. Assuming a binomial weight distribution, an upper bound is determined for the erasure performance of any code, and it is shown that this can be translated into an upper bound for code performance in the AWGN channel. Finally, theoretical bounds on the construction of error-correction codes are discussed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

DOI
10.1007/978-3-319-51103-0_1

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•Jan 1, 2022•0 citations
The theoretical performance of binary codes for the additive white Gaussian noise (AWGN) channel is discussed. In particular, Gallager’s coding theorem for binary codes is treated extensively. By assuming a binomial weight distribution for linear codes, it is shown that the decoder error probability performance of some of the best, known linear, binary codes is the same as the average performance of the ensemble of all randomly chosen, binary nonlinear codes having the same length and dimension. Assuming a binomial weight distribution, an upper bound is determined for the erasure performance of any code, and it is shown that this can be translated into an upper bound for code performance in the AWGN channel. Finally, theoretical bounds on the construction of error-correction codes are discussed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
We study the limits of performance of Gallager codes (low-density parity-check (LDPC) codes) over binary linear intersymbol interference (ISI) channels with additive white Gaussian noise (AWGN). Using the graph representations of the channel, the code, and the sum-product message-passing detector/decoder, we prove two error concentration theorems. Our proofs expand on previous work by handling complications introduced by the channel memory. We circumvent these problems by considering not just linear Gallager codes but also their cosets and by distinguishing between different types of message flow neighborhoods depending on the actual transmitted symbols. We compute the noise tolerance threshold using a suitably developed density evolution algorithm and verify, by simulation, that the thresholds represent accurate predictions of the performance of the iterative sum-product algorithm for finite (but large) block lengths. We also demonstrate that for high rates, the thresholds are very close to the theoretical limit of performance for Gallager codes over ISI channels. If C denotes the capacity of a binary ISI channel and if C/sub i.i.d./ denotes the maximal achievable mutual information rate when the channel inputs are independent and identically distributed (i.i.d.) binary random variables (C/sub i.i.d.//spl les/C), we prove that the maximum information rate achievable by the sum-product decoder of a Gallager (coset) code is upper-bounded by C/sub i.i.d./. The last topic investigated is the performance limit of the decoder if the trellis portion of the sum-product algorithm is executed only once; this demonstrates the potential for trading off the computational requirements and the performance of the decoder.
In this paper, upper bound on the probability of maximum a posteriori (MAP) decoding error for systematic binary linear codes over additive white Gaussian noise (AWGN) channels is proposed. The proposed bound on the bit error probability is derived with the framework of Gallager’s first bounding technique (GFBT), where the Gallager region is defined to be an irregular high-dimensional geometry by using a list decoding algorithm. The proposed bound on the bit error probability requires only the knowledge of weight spectra, which is helpful when the input-output weight enumerating function (IOWEF) is not available. Numerical results show that the proposed bound on the bit error probability matches well with the maximum-likelihood (ML) decoding simulation approach especially in the high signal-to-noise ratio (SNR) region, which is better than the recently proposed Ma bound.
Bounds on the error probability of maximum likelihood decoding of a binary linear code are considered. The bounds derived use the weight spectrum of the code and they are tighter than the conventional union bound in the case of large noise in the channel. The bounds derived are applied to a code with an average spectrum, and the result is compared to the random coding exponent. The author shows that the bound considered for the binary symmetrical channel case coincides asymptotically with the random coding bound. For the case of AWGN channel the author shows that Berlekamp's (1980) tangential bound can be improved, but even this improved bound does not coincide with the random coding bound, although it can be very close to it.>
In this paper, amended truncated union bounds on the bit error probability of systematic linear codes over additive white Gaussian noise (AWGN) channels are proposed. The proposed bounds are based on Gallager's first bounding technique (GFBT), where the “good” region is defined by a suboptimal list decoding algorithm. The proposed bounds require only the knowledge of partial input output weight enumerating function (IOWEF) with truncated information weight, which can also be adapted to the codes when the IOWEF is not available and difficult to calculate but the weight spectrum is known. The proposed amended truncated union bounds improve the conventional union bounds and the recently proposed Ma bounds but have a similar complexity since they involve only the Q-function.
In this paper, the sphere bound (SB) is revisited within a general bounding framework based on nested Gallager regions. We show that the SB proposed by Herzberg et al. is equivalent to the SB proposed by Kasami et al.. Interestingly, this general framework provides a new way to evaluate the performance of binary linear codes over additive white Gaussian noise (AWGN) channels. This simulation approach relies on only the geometry structure of the code itself. Therefore, simulation results can be used to evaluate not only the performance over AWGN channels but also that over other symmetrical real channels. Numerical results obtained with the new performance evaluation approach are presented.
With the development of communication technology, modern communication networks need to achieve ultra-high reliability to meet practical demands. Forward error correction coding has been widely used in real-world communication systems for providing reliable data transmission over noisy channels. In this thesis, we investigate the efficient performance evaluation methods for block codes over memoryless channels. Performance bounds are powerful tools to provide insights on the error performance of the coded systems. In many practical scenarios where performance bounds are not applicable (e.g., due to the unavailability of the relevant coding parameters under a given decoder), the Monte Carlo simulation is still popular despite its inefficiency, especially in the low error probability regime. As a variance-reduction technique, importance sampling (IS) can significantly reduce the sample size needed for reliable estimation. We firstly examine the problem of efficient performance evaluation of linear block codes over binary symmetric channels (BSCs). Contrary to the conventional wisdom, we prove that for BSCs, all bounds based on Gallager’s first bounding technique, including the famous union bound, are not asymptotically tight for all possible choices of the Gallager region. By proposing the so-called input demodulated-output weight enumerating function (IDWEF) of a code, asymptotically tight maximum-likelihood decoding upper and lower bounds for BSCs are derived. For the cases that coding parameters are absent or suboptimal decoders are applied, we propose an efficient IS estimator by deriving the optimal IS distribution of the Hamming weight of the error vector. In addition, the asymptotic percentage saving of the proposed IS estimator over the state-of-the-art IS estimator is characterized in terms of the sample size. Its accuracy in predicting the efficiency of the proposed IS estimator is verified by extensive computer simulation. We further examine the IS estimator design and efficiency analysis problems for the additive white generalized Gaussian noise (AWGGN) channel. The generalized Gaussian distribution is an effective model to describe real-world systems with impulsive noise. It can be specialized to the Laplace and Gaussian distributions depending on the choices of the decay rates of the heavy tail. By deriving the optimal IS distribution on the one-dimensional space mapped from the observation space, we present a general framework for designing IS estimators for linear block codes over a memoryless continuous channel. We apply the framework to the AWGGN channel and propose a noise-magnitude-domain IS (NM-IS) estimator. As an efficiency measure, the asymptotic IS gain of the proposed estimator is derived in a multiple integral as the signal-to-noise ratio (SNR) tends to infinity. Specifically, we reduce the gains of both Laplace and Gaussian noise cases to a one-dimensional integral. In addition, by limiting the use of the union bound to an L1-norm sphere with an optimized radius, we derive the sphere bound for the additive white Laplace noise channel. Simulation results show the efficiency of the proposed IS estimator in terms of the sample size and verify the accuracy of the prediction of the derived IS gain. Besides, we specialize the proposed framework to further improve the efficiency of the IS simulation based on the side information of the modulation. For the AWGGN channel with binary phase-shift keying (BPSK) modulation, we propose an enhanced NM-IS estimator, which outperforms the NM-IS estimator in efficiency. For the additive white Gaussian noise channel with M-PSK modulation, we propose an angular domain IS estimator and provide its efficiency analysis in terms of the asymptotic IS gain. Furthermore, in the performance evaluation of communication systems, it is often seen that researchers are more interested in the required SNR at a target error probability Pe than the other way around. The conventional solution to this Pe-targeted SNR estimation problem involves interpolation of simulated error probabilities at multiple handpicked SNRs for estimating the required SNR at the target error probability. We propose a Pe-targeted iterative IS estimator that solves the problem algorithmically. The proposed method can achieve the same efficiency (in terms of sample size) as estimating the error probability at the required SNR and allow the confidence interval of the SNR estimate to be obtained.