SNR-Invariant Importance Sampling for Hard-Decision Decoding Performance of Linear Block Codes

We present an importance sampling (IS) technique for evaluating the word-error rate (WER) and bit-error rate (BER) performance of binary linear block codes under hard-decision decoding. This IS technique takes advantage of the invariance of the decoding outcome to the transition probability of the binary symmetric channel given a received error pattern, and is equivalent to the method of stratification for variance reduction. A thorough analysis of the accuracy of the proposed signal-to-noise-ratio-invariant IS (IIS) estimator based on computing its relative bias and standard deviation is provided. Under certain conditions, which may be achieved fairly easily for certain code and decoder combinations, we demonstrate that it is possible to use the proposed IIS technique to accurately evaluate the WER and BER to arbitrarily low values. Further, in all cases, the probability estimates obtained via IIS always serve as a lower bound on the true probability values