Genetically Optimized Massive MIMO System for Enhanced QoS in 5G Networks

Abstract

The paper shows simulation results and the analysis of a massive MIMO system optimized by Genetic Algorithm (GA) in downlink precoding. The performance of the system is determined in respect to Bit Error Rate (BER), Symbol Error Rate (SER), Spectral Efficiency (SE), Energy Efficiency (EE), and Channel Capacity at different Signal to Noise Ratio (SNR) conditions. A complete Genetic Algorithm is also proposed to minimize the precoding matrix that finally leads to maximization of sum rate of the system. It has been seen through the simulation that the quality of service (QoS) metrics is significantly improved by evolutionary optimization, which makes GA a feasible option in precoding of real-time Massive MIMO in 5G networks.