Robust Multi-Objective Hybrid Beamforming Synthesis for Quantized Massive-MIMO Arrays Under Channel Uncertainty

Abstract

Massive multiple-input multiple-output (MIMO) systems are one of the technologies that enable the next-generation wireless communication networks because they can achieve high spectral efficiency and enhanced link reliability. Nevertheless, there are important challenges of massive-MIMO beamforming architectures in practical implementation due to limits in the hardware, quantizing phase shifters, and error in channel estimation. Beamforming algorithms in the conventional form also presume perfect hardware and channel state information, making the algorithms inapplicable to real deployment situations. This paper presents a high-quality multi-purpose hybrid beamforming synthesis model that can be applied successfully to act in the presence of channel uncertainty and hardware quantization. The suggested solution posits beamforming optimization as a multi-objective problem, which will maximise the spectral efficiency, and minimise the interference leakage and computational complexity at the same time. Synthetic channel environments are created with statistical propagation models that can be used to test the performance of the system without the need to measure performance in real-time metrics over the wireless world. The effect of the quantization of resolution and channel estimation noise on beamforming performance is analysed through simulating a hybrid analogue digital beamforming architecture. The simulation findings illustrate that the proposed framework is better in terms of spectral efficiency and less interference than the traditional beamforming methods. The results emphasize the need to include hardware constraints and channel ambiguity in beamforming optimization models of the next generation wireless communication network.