Unsupervised and Non Parametric Iterative Soft Bit Error Rate Estimation for Any Communications System

TL;DR

This paper introduces an unsupervised, non-parametric iterative method for soft bit error rate estimation applicable to any communication system, using Kernel density estimation and EM algorithms to improve accuracy without prior system knowledge.

Contribution

It proposes a novel combination of Kernel-based pdf estimation and iterative EM algorithms for BER estimation without prior system information, applicable to diverse communication systems.

Findings

Achieves asymptotically unbiased BER estimates.

Demonstrates superior performance over Monte Carlo methods.

Effective in multiuser CDMA systems with single user detection.

Abstract

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.