SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

Open Access Research

A complexity-performance-balanced multiuser detector based on artificial fish swarm algorithm for DS-UWB systems in the AWGN and multipath environments

Zhendong Yin1*, Zhiyuan Zong1, Hongjian Sun2, Zhilu Wu1 and Zhutian Yang1

Author Affiliations

1 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, People’s Republic of China

2 Department of Electronic Engineering, King’s College London, London, WC2R 2LS, UK

For all author emails, please log on.

EURASIP Journal on Advances in Signal Processing 2012, 2012:229  doi:10.1186/1687-6180-2012-229

Published: 30 October 2012

Abstract

In this article, an efficient multiuser detector based on the artificial fish swarm algorithm (AFSA-MUD) is proposed and investigated for direct-sequence ultrawideband systems under different channels: the additive white Gaussian noise channel and the IEEE 802.15.3a multipath channel. From the literature review, the issues that the computational complexity of classical optimum multiuser detection (OMD) rises exponentially with the number of users and the bit error rate (BER) performance of other sub-optimal multiuser detectors is not satisfactory, still need to be solved. This proposed method can make a good tradeoff between complexity and performance through the various behaviors of artificial fishes in the simplified Euclidean solution space, which is constructed by the solutions of some sub-optimal multiuser detectors. Here, these sub-optimal detectors are minimum mean square error detector, decorrelating detector, and successive interference cancellation detector. As a result of this novel scheme, the convergence speed of AFSA-MUD is greatly accelerated and the number of iterations is also significantly reduced. The experimental results demonstrate that the BER performance and the near–far effect resistance of this proposed algorithm are quite close to those of OMD, while its computational complexity is much lower than the traditional OMD. Moreover, as the number of active users increases, the BER performance of AFSA-MUD is almost the same as that of OMD.

Keywords:
DS-UWB; Multiuser detection (MUD); Artificial fish swarm algorithm (AFSA); Euclidean solution space