Performance of massive MIMO Systems for Future Generation Wireless Systems

Abstract

Research into 5G enabling technologies has seen much increased activity of late. Among the proposed technologies with much potential for becoming an important underlying aspect of 5G is massive multiple-input multiple-output (MIMO). This paper seeks to evaluate the performance of linear signal processing methods applicable to massive MIMO so as to propose suitable signal processing methods for these applications, and to determine suitable antenna array sizes for a given number of users in a massive MIMO cell. A single cell, and a 16 square cell grid network, were modeled in a MATLAB enviroment with base stations placed at the centre of the 1Km × 1Km square cells and users were randomly deployed in each cell. Monte Carlo simulations were applied with Rayleigh fading channels. Obtainable sum spectral efficiency for various linear signal processing methods were then obtained. Minimum Mean Square Error (MMSE), Zero Forcing (ZF), Regularized Zero Forcing (RZF) and Maximal Ratio Combining (MR) linear detection methods were evaluated. The aim of the Monte Carlo simulations were to determine the achievable spectral efficiency as a function of the ratio between the number of antennas at a base station and the number of users in a cell. This was done initially for a single cell and then extended to a 16 cell network. Pilot reuse factors, which is the ratio between the pilots allocated to a cell and users in a cell, were varied and spectral efficiency evaluated as a function of increasing number of antennas. The results obtained show that the MMSE has the best performance. As antenna array size increase, RZF and ZF monotonically increase so at to coincide when the ratio of antennas to users increases. MR combining method had the least performance among the four receive combining methods looked at.