Mutation-Based Bee Colony Optimization Algorithm for Near-ML Detection in GSM-MIMO

Generalized spatial modulation multiple-input multiple-output (GSM-MIMO) is a promising technique to fulfil the ever-growing need for high data rates and high spectral efficiency for 5G and beyond systems. Maximum likelihood (ML) detection achieves optimal performance for GSM-MIMO systems. However, ML detection performs an exhaustive search and hence, ML have intractable exponential computational complexity. Hence, low complexity detection algorithms are needed to be explored for reliable detection in GSM-MIMO systems. In this paper, a novel and robust GSM-MIMO detection algorithm are proposed based on artificial bee colony optimization with mutation operator. Simulation results validate that the proposed algorithm outperforms minimum mean square error detection and achieves near-ML bit error rate performance for GSM-MIMO systems, under both perfect and imperfect channel state information at the receiver.