Multiuser detection in a dynamic environment Part I: User identification and data detection

TL;DR

This paper introduces a Bayesian approach using Random-Set Theory for identifying active users and detecting data in dynamic multiuser systems where users frequently enter and leave, improving detection accuracy over traditional fixed-user assumptions.

Contribution

It presents a novel Bayesian framework with Random-Set Theory for joint user identification and data detection in environments with fluctuating user activity.

Findings

Derived optimal receivers for unknown user sets

Developed Bayesian-filter equations for user activity evolution

Enhanced detection performance in dynamic user scenarios

Abstract

In random-access communication systems, the number of active users varies with time, and has considerable bearing on receiver's performance. Thus, techniques aimed at identifying not only the information transmitted, but also that number, play a central role in those systems. An example of application of these techniques can be found in multiuser detection (MUD). In typical MUD analyses, receivers are based on the assumption that the number of active users is constant and known at the receiver, and coincides with the maximum number of users entitled to access the system. This assumption is often overly pessimistic, since many users might be inactive at any given time, and detection under the assumption of a number of users larger than the real one may impair performance. The main goal of this paper is to introduce a general approach to the problem of identifying active users and estimating their parameters and data in a random-access system where users are continuously entering and leaving the system. The tool whose use we advocate is Random-Set Theory: applying this, we derive optimum receivers in an environment where the set of transmitters comprises an unknown number of elements. In addition, we can derive Bayesian-filter equations which describe the evolution with time of the a posteriori probability density of the unknown user parameters, and use this density to derive optimum detectors. In this paper we restrict ourselves to interferer identification and data detection, while in a companion paper we shall examine the more complex problem of estimating users' parameters.