Efficient performance evaluation of Forward Error Correcting codes

Abstract

Standard Monte Carlo (SMC) simulation is employed to evaluate the performance of Forward Error Correcting (FEC) codes.This performance is in terms of the probability of error during the transmission of information through digital communication systems. The time taken by SMC simulation to estimate the FER increases exponentially with the increase in Signal-to-Noise Ratio (SNR). We hereby present an improved version of Fast Flat Histogram (FFH) method, an Adaptive Importance Sampling (AIS) technique inspired by algorithms existing in statistical physics. We show that the improved FFH method employing Wang Landau algorithm based on a Markov Chain Monte Carlo (MCMC) sampler reduces the simulation time of the performance evaluation of complex FEC codes having different code rates.

An efficient pseudo-codeword search algorithm for Belief Propagation decoding of LDPC codes

“…where t l and z l are the transmitted bit and the noise value in the lth position respectively. This definition of V (z) is different from the one that we used in [17], [18].…”

“…The basic skeleton of our technique is the same as that in [17], [18]. Let Γ be the n-dimensional probability space of the noise in the n bits of a codeword.…”

“…In this paper, we investigate the use of a physics inspired algorithm known as Fast Flat Histogram (FFH) method [16] which has already been implemented for the efficient performance evaluation of forward error correcting codes [17], [18]. The method consists of a random walk scheme employing Wang-Landau Algorithm in adaptive noise sampler framework.…”

Fast Simulation of Error Control Coded Systems Using Flat Histogram Monte Carlo Methods

“…DAIS potentially works in all scenarios where the error probability is significantly correlated with the noise power. It has been applied to turbo-coded systems operating over AWGN channels where moderate simulation gains have been obtained [8].…”