Spectral efficiency optimization with channel state information of a massive MIMO System - Chingore, Paul Chakanetsa (Department of Electrical Engineering, 2023)
The 5G network is expected to provide high data rate transmissions at very low latencies. To meet these high data rates the exploration of the under-utilized millimetre Wave (mm-Wave) frequency spectrum for hereafter broadband cellular communication networks is a focal point. Mm-Wave communication motivates the utilization of massive-MIMO. However, they are some limitations in the use of massive-MIMO since large –scale antenna arrays have high cost as well as the high-power consumption of huge Radio Frequency (RF) chains. This is a major drawback in the adoption of fully digital precoding in large-array systems. This research focuses on reducing the number of RF chains while using fixed large number of arrays for spatial multiplexing gains. A hybrid precoding architecture for mm Wave systems has been proposed for a system that has imperfect channel state information. Many wireless communication operations can be formulated as nonconvex non-smooth optimization problems. Often there is lack of effective algorithms for these problems especially in the event that the optimization variables are non-linear and coupled in some nonconvex constraints. To add on to that it is close to impossible to have perfect channel state information (CSI) in a wireless system. To optimize the spectral efficiency of imperfect CSI, an algorithm called penalty dual decomposition (PDD) is proposed for these problems. The PDD is a double-loop iterative algorithm that has a guaranteed convergence to Karush-Kuhn-Tucker (KKT) solution of the hybrid precoding problem under a mild assumption. The KKT solution supports the multi-stream transmission with few RF Chains. Simulation results reviews that the proposed PDD algorithm is capable of achieving better spectral efficiency than MAP and OMP even though they are few RF chains.