International audienceThe quality of transmission in digital communication systems is usually measured by Frame Error Rate (FER). The time taken by standard Monte Carlo (MC) simulation to estimate the FER increases exponentially with the increase in Signal-to-Noise Ratio (SNR). In this correspondence, we present an Adaptive Importance Sampling (AIS) technique inspired by statistical physics called Fast Flat Histogram (FFH) method to evaluate the performance of LDPC codes with a reduced simulation time. The FFH method employs Wang Landau algorithm based on a Markov Chain Monte Carlo (MCMC) sampler and we managed to decrease the simulation time by a factor of 13 to 173 for LDPC codes with block lengths up to 2640 bits

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.