A Genetic Algorithm Based Finger Selection Scheme for UWB MMSE Rake Receivers

TL;DR

This paper introduces a genetic algorithm-based method for selecting multipath components in UWB MMSE SRake receivers, aiming to optimize performance in complex multipath environments.

Contribution

It proposes a novel GA-based iterative scheme for finger selection in UWB MMSE SRake receivers, addressing the NP-hard optimization problem.

Findings

The GA-based scheme achieves near-optimal performance.

Simulation results show improved performance over conventional methods.

The method converges efficiently within reasonable iterations.

Abstract

Due to a large number of multipath components in a typical ultra wideband (UWB) system, selective Rake (SRake) receivers, which combine energy from a subset of multipath components, are commonly employed. In order to optimize system performance, an optimal selection of multipath components to be employed at fingers of an SRake receiver needs to be considered. In this paper, this finger selection problem is investigated for a minimum mean square error (MMSE) UWB SRake receiver. Since the optimal solution is NP hard, a genetic algorithm (GA) based iterative scheme is proposed, which can achieve near-optimal performance after a reasonable number of iterations. Simulation results are presented to compare the performance of the proposed finger selection algorithm with those of the conventional and optimal schemes.