Deep Learning of Near Field Beam Focusing in Terahertz Wideband Massive MIMO Systems

TL;DR

This paper introduces a low-complexity, frequency-aware beamforming method for near-field, wideband massive MIMO systems that effectively manages wideband effects and near-field conditions using hybrid RF architectures.

Contribution

It proposes a novel joint design approach for time delays and phase shifts, combining online learning and geometry-assisted methods for robust wideband beamforming.

Findings

Achieves robust near-field beam focusing across wide frequency ranges.

Reduces complexity of beamforming in large antenna array systems.

Demonstrates improved performance over traditional methods in simulations.

Abstract

Employing large antenna arrays and utilizing large bandwidth have the potential of bringing very high data rates to future wireless communication systems. However, this brings the system into the near-field regime and also makes the conventional transceiver architectures suffer from the wideband effects. To address these problems, in this paper, we propose a low-complexity frequency-aware beamforming solution that is designed for hybrid time-delay and phase-shifter based RF architectures. To reduce the complexity, the joint design problem of the time delays and phase shifts is decomposed into two subproblems, where a signal model inspired online learning framework is proposed to learn the shifts of the quantized analog phase shifters, and a low-complexity geometry-assisted method is leveraged to configure the delay settings of the time-delay units. Simulation results highlight the efficacy of the proposed solution in achieving robust performance across a wide frequency range for large antenna array systems.