Blind estimation of bit and block error probabilities using soft information

We consider the problem of estimating the bit error probability (BEP) and the block error probability (BLEP) in digital communication systems in a blind manner, i.e., without knowledge of the transmitted data. We propose estimators based on soft information and show that they outperform conventional non-blind estimators, which know the transmitted data. We study the performance of these estimators and provide conditions that guarantee that our BEP estimator is the minimum variance unbiased estimator. For the Gaussian case we derive the Cramér-Rao lower bound for BEP estimation and show that an efficient estimator does not exist. Furthermore, we derive simple lower and upper bounds for the BLEP estimator. Our results are numerically corroborated using Monte Carlo simulations.