Meta-Heuristics for Symbol Detection in a Spatial Multiplexing System
An application of Swarm Intelligence (SI) based Meta-heuristics for a NP-hard problem in the area of wireless communications is explored. The specific problem is of detecting symbols in a Multi-Input Multi-Output (MIMO) communications system. This approach is particularly attractive as SI is well suited for physically realizable, real-time applications, where low complexity and fast convergence is of absolute importance. Application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms is discussed. While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, we show that the Swarm Intelligence optimized MIMO detection algorithms gives near-optimal Bit Error Rate (BER) performance in fewer iterations, thereby reducing the ML computational complexity significantly. The simulation results suggest that the proposed detector gives an acceptable performance complexity trade-off in comparison with optimal ML and non-linear Vertical Bell labs Layered Space Time (VBLAST) detectors. The proposed techniques result in as high as 14-dB enhanced BER performance with acceptable increase in computational complexity in comparison with VBLAST. The reported algorithms reduce the computer time requirement significantly over exhaustive search method with a reasonable BER performance.