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Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 578710, 14 pages
Research Article

Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems

School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6

Received 1 June 2012; Accepted 31 October 2012

Academic Editor: Jun He

Copyright © 2013 Saeed Ashrafinia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block Code (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection) for finding an optimal detection has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based Evolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to other mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee swarms. An enhanced discrete version of the ABC algorithm is presented and applied to the joint symbol detection problem to find a nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC with other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding, minimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation of distributions algorithm, and the more novel biogeography-based optimization algorithm.