A novel Bayesian detection approach for ackack signalling in third generation high speed packet data systems

摘要

In this paper we develop a novel framework for Bayesian detection that is generic in its formulation, and is referred to as Bayesian constrained false alarm detection. It is shown that this formulation serves as a bridge between classical and Bayesian detection approaches, and can be thought of as a generalization of both the classical Neyman Pearson detection framework as well as of the minimum risk Bayesian detection strategy. This problem formulation is motivated by an application of signaling (ackack) detection in third generation wireless packet data systems, such as HSDPA. In these systems, signaling information in the form of ackack are critical to be detected with high fidelity in order to ensure that the performance of a packet data system is not affected adversely. We also propose a provably convergent adaptive algorithm to estimate the a priori probabilities. These concepts and algorithms have widespread utility in any application of statistical signal processing. Simulation results are presented for their application to ackack detection in a realistic UMTS simulator.