Massive MIMO Linear Precoding Techniques Performance Assessment - Kebede, Tewelgn; Wondie, Yihenew; Steinbrunn, Johannes (2021 International Symposium on Networks, Computers and Communications (ISNCC), 2021)
Massive MIMO is one of the 5G nominee technologies that provides high energy and bandwidth efficiencies. Precoding at the basestation is compulsory to ensure such efficiencies in the performance and design of massive MIMO. This work investigates the performance of all recent massive MIMO linear precoding techniques called: Zero Forcing, Maximum Ratio Transmission, Regularized Zero Forcing, Truncated Polynomial Expansion and Phased Zero Forcing. The performance metrics which are used for their performance assessment are: bit error rate, signal to noise ratio, spectral efficiency and energy efficiency for a single cell downlink massive MIMO network; where the base station has ideal channel state information. Analytical expressions are formulated to each performance metric for the evaluation of these linear precoding techniques. Besides, the relationship between the number of base station antennas, users and signal to noise ratio with the achievable rates are revealed. The simulation result shows that Zero Forcing is the optimum linear precoding scheme both in bandwidth and power efficiency. However, its computational complexity is very high relative to other linear precoders. As far as the overall performance is concerned, Phased Zero Forcing approaches to Zero Forcing with low computational complexity.