Efficient performance evaluation of Forward Error Correcting codes

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.