This paper presents a new method for the performance evaluation of bit decoding algorithms. The method is based on estimating the Probability Density Function (pdf) of the bit Log Likelihood Ratio (LLR) by using an exponential model. It is widely known that the pdf of the bit LLR is close to the normal density. The proposed approach takes advantage of this property to present an efficient algorithm for the pdf estimation. The moment matching method is combined with the maximum entropy principle to estimate the underlying parameters. We present a simple method for computing the probabilities of the point estimates for the estimated parameters, as well as for the bit error rate. The corresponding results are used to compute the number of samples that are required for a given precision of the estimated values. It is demonstrated that this method requires significantly fewer samples as compared to the conventional Monte-Carlo simulation.
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