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This paper addresses the problem of unsupervised soft BER estimation for digital communications systems, where no prior knowledge about transmitted information bits is available. The authors show that the problem of BER estimation is equivalent to estimating the conditional probability density functions (pdf)s of soft channel/receiver outputs. They also propose a non parametric Gaussian Kernel-based pdf estimation technique. Then, they introduce an iterative stochastic Expectation Maximization (EM) algorithm for the estimation of both a priori and a posteriori probabilities of transmitted information bits, and the classification of soft observations according to transmitted bit values.
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