Abstract
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.
Highlights
One of the major impairment of the wireless communication channel is fading and the performance of radio channels is mainly governed by fading
We show that 1-Opt and Ant colony optimization (ACO) based detector can give near-optimal bit error rate (BER) at much lower complexity levels
1-Opt and ACO algorithm assisted wide band spatial multiplexing systems symbol detector that views the Multiple-Input Multiple-Output (MIMO) symbol detection issue as a combinatorial optimization problem and try to approximate the near optimal solution iteratively
Summary
One of the major impairment of the wireless communication channel is fading and the performance of radio channels is mainly governed by fading. The relevant information-theoretic analysis reveals that significant performance gains are achievable in wireless communication systems using a MIMO architecture employing multiple antennas [1]. This architecture is suitable for higher data rate multimedia communications [2]. A problem encountered in the design of receivers for MIMO systems is the detection of data from noisy measurements of the transmitted signals. Approximate algorithms are those which quickly find a suboptimal solution which is within a certain range of the optimal one. We report ACO assisted MIMO detection algorithm with a reasonable performance complexity trade off and to the best of authors understanding this is first successful attempt to optimize MIMO detection using ACO meta-heuristic.
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