Hybrid Dragonfly with Electric Fish Optimization-Based Multi User Massive MIMO System: Optimization Model for Computation and Communication Power

The massive multiple-input multiple-output (massive MIMO) system is the major section of the fifth generation (5G) future wireless cellular systems. It consists of hundreds of antennas in the base station that serves more number of users, concurrently. Thus, this system will get optimized energy usage, high data rate, and more precision because of their larger degrees of freedom. The computation power to the total power consumption ratio is considered for rapid increment owing to the more data traffic at the baseband unit that seeks more attention in the exploitation of massive MIMO systems for 5G wireless systems. The main intent of this paper is to develop the multi-user massive MIMO systems by deriving the joint optimization problem of computation and communication power. In the existing energy efficiency analysis, there is a negative effect on energy efficiency when increasing the count of RF chains and antennas by considering only computation power or communication power in massive MIMO. In order to overwhelm this problem, this paper focuses on two optimization problems. The first problem is focusing on the improvement of upper bound on energy efficiency with the optimal baseband and RF precoding matrices based on a new hybrid meta-heuristic algorithm. The combination of two well-performing meta-heuristic algorithms like electric fish optimization and dragonfly algorithm is used as the new algorithm, which is named as hybrid dragonfly with electric fish optimization (HD-EFO) for enhancing the efficiency of massive MIMO system. In the second phase, the joint optimization of both computation and communication power is performed by the same HD-EFO for developing the optimized hybrid precoding matrix. The extensive results have shown that the implemented multi-user massive MIMO systems with partially-connected structures using HD-EFO increase the cost and energy efficiencies, and save the maximum power.