Effective error floor estimation based on importance sampling with the uniform distribution

A key problem of low-density parity-check (LDPC) codes analysis is estimation of an extremely low error floor that occurs at a high level of the signal-to-noise ratio (SNR). The importance sampling (IS) method is a popular approach to address this problem. Existing works typically use a normal sampling probability density function (PDF) with shifted mean, which yields a large variance of the estimate. In contrast, uniform distribution has equally probable samples on the entire range and thus should reduce the variance, but results in a biased estimation. This paper proposes a modified IS approach (IS-U) that allows considering the uniform distribution as a sampling PDF, and shows that this estimation is better than the traditional one. Also, this paper demonstrates that the existing criteria cannot be applied to evaluate the accuracy of the IS-U on the whole SNR range. To address this issue, a new metric is proposed, which uses only the convergence rate and does not depend on the true data.