Investigations on energy efficiency in massive mimo systems using bio inspired optimization algorithms

Abstract

In recent years, the requests for remote data services have been definitely expanding because of the more number of cell phones and rising applications. Those universal communication services need higher information transmission rates. In this manner new difficulties in future wireless communications are presented. Subsequently, the massive MIMO emerges with the deployment of enormous antenna arrays at the BSs, which can aid a large number of User Terminals (UTs) around a similar ime/frequency resources. The Massive Multiple Input Multiple Output provides reliable Base Station (BS) for the Mobile Users (MUs) with CSI (Channel State Information) and jointly offers Spectral Efficiency (SE) and Energy Efficiency (EE). Conversely, because of the existence of multiple transceivers at both the transmitter and receiver side, the complexity and expenses are increasing in terms of hardware and energy utilization. Hence, we have proposed effective bio-inspired optimization algorithms to improve EE. From the state of art literature, EE plays a major role in 5G system. Previously, transmit power alone is considered for EE calculation. In the past decade, the researches include circuit power due to hardware components and signal processing part in the transmitter and receiver. Hence the dynamic power consumption model is considered to calculate the EE. The proposed work recognizes the importance of EE enhancement. The initial work presents Energy Efficiency of Low resolution ADCs with perfect CSI . As the name suggests, this work explores the low resolution ADCs to improve EE. The low resolution ADCs utilizes Hybrid Grey Wolf Optimization with Cuckoo Search (GWO-CS) for obtaining the optimal bit resolution to get higher EE with minimum achievable data rate as a design constraint. The performance of the work is compared with the existing bit allocation algorithms. Second work of research proposed the hybrid GWO-CS based Optimal Channel Estimation for developing Energy Efficient massive MIMO. The proposed GWO-CS selects t