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On the computation of the probability of undetected error for linear block codes on the Gilbert channel

B. Wong

Published Jan 1, 2010• 2 citations
Publisher

Abstract

An important measure of the performance of error detecting codes is the probability of undetected error. Extensive study on the subject has yielded results which allow for the computation of the probability of undetected error for many codes on the binary symmetric channel (BSC). However, little is known about code performance in more complicated channel models. The Gilbert channel is a two-state, three-parameter model with memory which simulates the effects of burst noise. In this thesis, we investigate methods to compute the probability of undetected error of binary linear block codes on this channel. We examine an approach to approximate code performance based on the P(m,n) distribution which is the probability of m errors in a block of n bits and the weight distribution of the code. For the Gilbert channel, P(m,n) can in principle be calculated from the channel parameters. In practice however, existing methodologies suffer from rather excessive computational requirements, particularly when n is larger than one thousand or so. We have developed an efficient method to calculate P(m,n) for reasonable channel parameters. This allows the probability of undetected error for many codes to be readily estimated. For certain channel and code parameters, the approximation method described above may not be sufficiently accurate. Exact analytical results are difficult to obtain, however; because unlike the BSC, the probability of a particular error pattern on the Gilbert channel depends not just on the number of 1's in the pattern. Nevertheless, by appropriately exploiting certain symmetries present on the Gilbert channel, we can acquire some useful results. We have derived the probability of undetected error for the single parity check code. We have also obtained a formula for summing over a cyclic class of vectors and shown that reciprocal generator polynomials generate cyclic codes which have the same probability of undetected error on the Gilbert channel. The Monte Carlo simulation technique is often used when exact analysis is difficult. In a simulation study of CRC codes, we are able to observe several interesting qualitative results with just a reasonable amount of computational effort. We find that as on the BSC, on the Gilbert channel the probability of undetected error does not always increase with worsening channel conditions. Also, the CRC-CCITT code appears to maintain its superiority in terms of error detection performance over the CRC-ANSI code on the Gilbert channel, and perhaps most significantly, for some ranges of channel parameters, the probability of undetected error estimated using BSC results with the effective bit error rate can be quite inaccurate.

DOI
10.14288/1.0098608

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An important measure of the performance of error detecting codes is the probability of undetected error. Extensive study on the subject has yielded results which allow for the computation of the probability of undetected error for many codes on the binary symmetric channel (BSC). However, little is known about code performance in more complicated channel models. The Gilbert channel is a two-state, three-parameter model with memory which simulates the effects of burst noise. In this thesis, we investigate methods to compute the probability of undetected error of binary linear block codes on this channel. We examine an approach to approximate code performance based on the P(m,n) distribution which is the probability of m errors in a block of n bits and the weight distribution of the code. For the Gilbert channel, P(m,n) can in principle be calculated from the channel parameters. In practice however, existing methodologies suffer from rather excessive computational requirements, particularly when n is larger than one thousand or so. We have developed an efficient method to calculate P(m,n) for reasonable channel parameters. This allows the probability of undetected error for many codes to be readily estimated. For certain channel and code parameters, the approximation method described above may not be sufficiently accurate. Exact analytical results are difficult to obtain, however; because unlike the BSC, the probability of a particular error pattern on the Gilbert channel depends not just on the number of 1's in the pattern. Nevertheless, by appropriately exploiting certain symmetries present on the Gilbert channel, we can acquire some useful results. We have derived the probability of undetected error for the single parity check code. We have also obtained a formula for summing over a cyclic class of vectors and shown that reciprocal generator polynomials generate cyclic codes which have the same probability of undetected error on the Gilbert channel. The Monte Carlo simulation technique is often used when exact analysis is difficult. In a simulation study of CRC codes, we are able to observe several interesting qualitative results with just a reasonable amount of computational effort. We find that as on the BSC, on the Gilbert channel the probability of undetected error does not always increase with worsening channel conditions. Also, the CRC-CCITT code appears to maintain its superiority in terms of error detection performance over the CRC-ANSI code on the Gilbert channel, and perhaps most significantly, for some ranges of channel parameters, the probability of undetected error estimated using BSC results with the effective bit error rate can be quite inaccurate.
We give an upper band on the undetected error probability of binary (n,k)-codes used on channels with memory described by Markov distributions. This bound is a generalization of the bound presented by Kasami (1983) for the binary symmetric channel (BSC). An extended bound is given for the Gilbert-Elliott channel (1960, 1963) and Markov channels with deterministic errors. We also develop a recursive technique for the exact calculation of the undetected error probability of an arbitrary linear block code used on a Markov type channel. This technique is based on the trellis representation of block codes described by Wolf (1978). Results of the computations are presented.>
We derive an upper bound on the undetected error probability of binary (n, k) block codes used on channels with memory described by Markov distributions. This bound is a generalization of the bound presented by Kasami et al. (1984) for the binary symmetric channel, and is given as an average value of some function of the composition of the state sequence of the channel. It can be extended in particular cases of Markov-type channels. As an example, such an extended bound is given for the Gilbert-Elliott (1960, 1963) channel and Markov channels with deterministic errors determined by the state. We develop a recursive technique for the exact calculation of the undetected error probability of an arbitrary linear block code used on a Markov-type channel. This technique is based on the trellis representation of block codes described by Wolf (1978). Results of some computations are presented.
The problem of computing the probability of undetected error is considered for linear block codes used for error detection. The recent literature is first reviewed and several results are extended. It is pointed out that an exact calculation can be based on either the weight distribution of a code or its dual. Using the dual code formulation, the probability of undetected error for the ensemble of all nonbinary linear block codes is derived as well as a theorem that shows why the probability of undetected error may not be a monotonic function of channel error rate for some poor codes. Several bounds on the undetected error probability are then presented. We conclude with detailed examples of binary and nonbinary codes for which exact results can be obtained. An efficient technique for measuring an unknown weight distribution is suggested and exact results are compared with experimental results.
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.
The performance of optimum linear block codes has been examined by computer simluation, and the error distribution at the decoder output, the block-error probability and the bit-error probability have been determined for the codes when they are used on a binary symmetric channel with random errors. A table showing the distribution of errors in the decoder output is presented, and it is shown how the table can be used to determine exact values for the bit-error probability and the block-error probability. The exact values of bit-error probability computed from the table are compared with values obtained using a known theoretical approximation to the bit-error probability, and some conclusions are drawn as to the general usefulness of the theoretical approximation. Also, block-error probabilities are computed from the information presented in the table and these values are compared with the estimates of block-error probability obtained from minimumdistance considerations.
Estimation of very low word-error probability of hard-decoded linear block codes can be performed through Monte-Carlo simulation. The computational complexity of the standard method however increases as the probability of error to be estimated decreases. In this paper we propose a general algorithm for fast estimation of probability of error of linear block codes on BSC channels based on the importance sampling and the cross-entropy method for rare-events that can be employed for any hard-decision decoder. When optimal decoding is used the algorithm reduces to a single simulation run that can estimate, with a given accuracy, performances for a whole range of sufficiently high signal-to-noise ratios.