ANALYZING THE IMPACT OF MASSIVE MIMO ON NETWORK CAPACITY AND ENERGY EFFICIENCY

Abstract

Massive Multiple-Input Multiple-Output (Massive MIMO) has become a fundamental technology in the next-generation wireless networks because it enables wireless networks to increase network capacity and energy efficiency by orders of magnitude. Massive MIMO makes it possible to use a vast number of antennas at the base station, which permits spatial multiplexing of many users using the same timefrequency resources, and results in significant spectral efficiency improvements. This work examines the effects of Massive MIMO on network capacity and energy efficiency on the theoretical level and on the system-level understanding. The capacity limits are also analyzed in the realistic propagation conditions, where the scaling of the antennas, hardening of the channels, and spatial correlation have been pointed out to have an effect on the data rates that can be achieved. Inter-cell interference is examined as well as the piloting contamination, as these two are significant in determining the capacity gains in dense deployments. A power efficiency model is created that includes a detailed power model of base stations, radio-frequency chain and signal processing unit. The most important energy efficiency indicators are considered to reflect the trade-off between the throughput improvement and the increment in hardware power consumption. Besides, the paper examines higher signal processing algorithm, such as linear and non-linear precoding, efficient channel estimation, and low-complexity detention, which combine to optimize the capacity and the energy consumption. This analysis has shown that Massive MIMO systems can be designed to make substantial improvements to the performance of the system (bits-per-joule) and can meet the demands of high data rates. The results offer useful design insights on the implementation of energy-sensitive Massive MIMO architectures in a future 5G and beyond a wireless network in the context of realistic deployment conditions.