A low complexity evolutionary algorithm for multi-user MIMO detection
Employing the Multi-Device Multi Input Multi Output (MIMO) communication scheme improves the wireless communication via diversity methods and spacial multiplexing. In this paper, we apply Biogeography-Based Optimization Algorithm (BBO) for Joint Symbol Detection (JSD) at the receiver station in a Multi-Device Space-Time Block Coded (STBC) MIMO system. Exhaustive search for finding an optimal detection - Maximum Likelihood (ML) detection - has a computational complexity that increases exponentially with the number of wireless devices, transmit antennas per wireless device, and the number of bits per symbols (complex modulation schemes e.g. MPSK or M-QAM), which yields to higher complexity and cost. BBO is a new population-based, biogeography inspired global optimization algorithm that mainly uses the biogeography-based migration operator to share the information among solutions. We present a low-complex migration model for partial immigration-based BBO, and apply it to JSD to find a nearly optimal solution in real time with much less computational complexity. The results of multiple independent simulation runs of the algorithms indicate a good performance/complexity trade-off compared with other well-known previously proposed algorithms for JSD, such as the near-optimal Sphere Decoding (SD), Genetic Algorithm (GA), Minimum Mean Square Error (MMSE), Vertical Bell Laboratories Layered Space-Time (V-BLAST) and Semi-Definite Relaxation (SDR). The effectiveness of BBO is verified through these simulation results.