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The 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.