Performance Analysis of Linear and Hybrid Precoding on Massive MIMO System Using Truncated Polynomial Expansion

This research examines the performing of linear precoding techniques in downlink massive MIMO systems. The downlink massive MIMO systems, linear precoding techniques as combined to zero forcing (ZF) and matched filter (MF), truncated polynomial expansion (TPE) and regularized zero forcing (RZF). The output of massive MIMO downlinks is analyzed with linear precoding. The performing of sum rate precoding of signal-to-noise (SNR) ratio, as well as numerous transmitter-receiver antennas, is discussed, including defective CSI and inter-user interference. On the channel, the transmitter as entire state information. The data describes how a signal travels from the transmitter to the receiver, taking into account factors like as space scattering and power decay. They appearance that given a perfect chain, the performing analyzed of two linear precoding approaches, namely Zero Forcing (ZF) and Matched filter (MF) for downlink mMIMO. The results demonstrate ZF, higher BER and sum rate as comparing to MF schemes, as well as a comparison of RZF and TPE average achievable rates. In multi-user scenarios, Kalman-based hybrid precoding (analog/digital) as used in a downlink throughput of 5.14 Gbps and a uplink rate of 2.26 Gbps. The impact of beam steering capability on both base stations (BS) and 5G user equipment (UEs) is also investigated. Based on the simulation results, the suggested approach provides a significant increase in spectral efficiency of 10.53 bps/Hz with 10 channel routes.