Parameter Selection for an Energy-Efficient Massive MIMO System

Abstract

In the most recent past years, tremendous development in the quantity of associated wireless gadgets are found. Billions of gadgets are associated and oversaw by wireless systems. In the meantime, every gadget needs high throughput to bolster applications like, voice, real-time video, and various media files. Request for wireless throughput and the quantity of wireless gadgets will dependably increase day by day. Likewise, there is a developing worry about energy efficiency of wireless communication frameworks. In this manner, future remote frameworks need to fulfill three principle necessities: Higher throughput, number of users served simultaneously and lesser energy consumption. Massive MIMO innovation, where a base station (BS) furnished with expansive number of antennas serves numerous users in a similar time-frequency block, can meet the above prerequisites. With huge number of antenna exhibits at the BS, the random channel vectors become pair-wisely orthogonal between the base station (BS) and the clients and henceforth, direct handling is about ideal and basic. High throughput and energy efficiency can be accomplished in light of the multiplexing gain, array gain and beamforming technique. In this thesis, the energy-efficient massive multiple input multiple output (MIMO) plan issues are being discussed for single cell scenario. Proper parameters like, number of antennas (M), number of users (K), length of pilot sequences and the transmission power are being selected to boost the general framework of energy use in accordance with the alternative optimization plus bisection searching (AO-BS) algorithm, which is found to be the most efficient in tackling the issues rapidly. At last, we varied some of the basic parameters like coherence time (Tc) and observed that higher the coherence time, better is the energy utilization. In addition to it, we have applied the AO-BS algorithm for different types of environment like indoor area, free space, urban area and suburban area with their specified path loss exponent and simulated it. From the simulations, it is being observed that the system is more energy efficient in the indoor environment than free space followed by urban area and then suburban area.