Social spider optimizer based large MIMO detector

To meet the ever growing demand of high data rates and high spectral efficiency of future generation networks, multiple-input multiple-output (MIMO) system is a key technique in wireless communication systems. However, design of an efficient low complexity detector for MIMO systems is a challenging research problem. Conventional MIMO detection techniques like zero forcing, minimum mean square error, minimum mean square error successive interference cancellation and minimum mean square error ordered successive interference cancellation detectors provide only sub-optimal performance and are not robust under imperfect channel state information (CSI) at the receiver. Hence, development of robust and efficient detection algorithms are necessary for non-erroneous symbol detection in MIMO systems. In this work, a novel detection algorithm for large MIMO systems is proposed inspired by social foraging behavior of Spiders, which uses a information propagation technique to provide improved performance than several well-studied meta-heuristic techniques like ant colony optimization and particle swarm optimization. Simulation results reveal that the proposed detection technique outperforms conventional detection techniques under both perfect and imperfect CSI at the receiver. The superior performance of the proposed algorithm under imperfect CSI at the receiver validates robustness of the algorithm.