Meta-heuristic Algorithm Based Precoding in Massive MIMO

In last few years there is escalating growth of teletraffic accompanied with higher power emission resulting in increased global carbon footprint. But as evolution of a technology should be in harmony with environment, controlling transmission energy at base station and mobile phones becomes mandatory. Therefore novel Beamforming technique or precoding technique is a desideratum at receiver as well as transmitter side. This technique searches for receivers and tracks optimal solution for enhancement of power efficiency, signal to interference noise ratio (SINR) and half power beam width. This paper proposes an integration of evolutionary algorithms and standard beamforming techniques such as least mean square (LMS) and recursive least square (RLS). To solve the global optimization problem, evolutionary algorithms such as particle swarm optimization (PSO), non-dominated sorting genetic algorithm, BAT and Firefly are used. The MATLAB simulation of the proposed fusion of LMS and RLS with evolutionary optimization algorithms has shown improvement in power and SINR which resulted in achieving less bit error rate. The results have shown reduction in mean square error (MSE) and improvement in cost function by 89%.