  This paper addresses the problem of unsupervised soft bit error rate (BER)
estimation for any communications system, where no prior knowledge either about
transmitted information bits, or the transceiver scheme is available. We show
that the problem of BER estimation is equivalent to estimating the conditional
probability density functions (pdf)s of soft channel/receiver outputs. Assuming
that the receiver has no analytical model of soft observations, we propose a
non parametric Kernel-based pdf estimation technique, and show that the
resulting BER estimator is asymptotically unbiased and point-wise consistent.
We then 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. These inputs serve in the iterative
Kernel-based estimation procedure of conditional pdfs. We analyze the
performance of the proposed unsupervised and non parametric BER estimator in
the framework of a multiuser code division multiple access (CDMA) system with
single user detection, and show that attractive performance are achieved
compared with conventional Monte Carlo (MC)-aided techniques.
