Optimization on Antenna Selection Using Imperialist Competitive Algorithm

Nowadays, the multiple antenna transmission technique, which may be modeled as Multiple Input Multiple Output (MIMO) systems, is used for increasing the capacity of the wireless communication systems. However, complexities and cost are associated with MIMO systems. Here, we propose a technique based on Imperialist Competitive Algorithm (ICA) to reduce the computational complexity and hardware cost. A new suboptimal configuration on antenna selection both in receiver and transmitter sides is the outcome of applying our method on MIMO systems. Our algorithm achieves almost the same outage capacity as the optimal selection technique while having lower computational complexity than the exiting nearly optimal antenna selection methods such as genetic algorithms. The antenna selection algorithm requires an exhaustive search of all possible combinations and permutations to find the optimum solution at the transmitter or receiver side, thus resulting in extremely high computational complexity. To reduce the computational load while still maximizing channel capacity, the ICA method is adopted to determine the suboptimum solution. The simulation results show that the ICA method has better performance from the point of view of both computational and time complexities, when compared with the Genetic Algorithm (GA) and Exhaustive search method (ES).

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