Application of Heuristic (1-Opt local Search) and Metaheuristic (Ant Colony Optimization) Algorithms for Symbol Detection in MIMO Systems

作     者：Kiran Khurshid Safwat Irteza Adnan Ahmed Khan S. I. Shah 

作者机构：不详 

出 版 物：《Communications and Network》 (通讯与网络（英文）)

年 卷 期：2011年第3卷第4期

页      面：200-209页

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

主　　题：Spatial Multiplexing System 1-Opt ACO Multi-Input Multi-Output Systems Symbol Detection 

摘      要：Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants foraging behavior, is one of the most recent metaheuristic technique. These techniques are used for solving optimization problems. Multiple-Input Multiple-Output (MIMO) detection problem is an NP-hard combinatorial optimization problem. We present heuristic and metaheuristic approaches for symbol detection in multi-input multi-output (MIMO) system. Since symbol detection is an NP-hard problem so ACO is particularly attractive as ACO algorithms are one of the most successful strands of swarm intelligence and are suitable for applications where low complexity and fast convergence is of absolute importance. Maximum Likelihood (ML) detector gives optimal results but it uses exhaustive search technique. We show that 1-Opt and ACO based detector can give near-optimal bit error rate (BER) at much lower complexity levels. Comparison of ACO with another nature inspired technique, Particle Swarm Optimization (PSO) is also discussed. The simulation results suggest that the proposed detectors give an acceptable performance complexity trade-off in comparison with ML and VBLAST detectors.