Abstract
In this paper, pilot contamination, in massive multiple-input multiple-output (MIMO) system is reduced to a significant extent by using base station (BS) rotation, user (UE) scheduling and enhanced Zero Forcing (e-ZF) precoding. Cellular network is divided, based on the angle, and partitioned into rings for grouping mobile UEs. Grouping is done by creating specifically designed policies using three significant network parameters. Neural network is used for scheduling grouped UEs and then UEs are assigned with optimal pilots. Hybrid particle swarm optimization with fuzzy (PSOF) logic detects optimal pilot in accordance to channel quality and additive white gaussian noise (AWGN). Simulations are performed in MATLAB-R2017b and performance is experimentally evaluated in terms of achievable rate, noise ratio and spectral efficiency in Rician Fading environment. Comparative results are evaluated with respect to conventional precoding techniques as dirty paper coding (DPC), block diagonalization (BD), matched filter (MF) and Tomlinson Harashima (TH) precoding.
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