Deep Learning Prediction of Beam Coherence Time for Near-FieldTeraHertz Networks

TL;DR

This paper introduces a deep learning approach to predict beam coherence time in near-field THz networks, reducing beam update overhead and improving data rates in mobile scenarios.

Contribution

It proposes a novel deep learning model to accurately predict beam coherence time, addressing the challenges of beam alignment overhead and near-field effects in THz communications.

Findings

Deep learning model accurately predicts beam coherence time.

Significant reduction in beam update overhead.

Enhanced data rates in high-mobility scenarios.

Abstract

Large multiple antenna arrays coupled with accurate beamforming are essential in terahertz (THz) communications to ensure link reliability. However, as the number of antennas increases, beam alignment (focusing) and beam tracking in mobile networks incur prohibitive overhead. Additionally, the near-field region expands both with the size of antenna arrays and the carrier frequency, calling for adjustments in the beamforming to account for spherical wavefront instead of the conventional planar wave assumption. In this letter, we introduce a novel beam coherence time for mobile THz networks, to drastically reduce the rate of beam updates. Then, we propose a deep learning model, relying on a simple feedforward neural network with a time-dependent input, to predict the beam coherence time and adjust the beamforming on the fly with minimal overhead. Our numerical results demonstrate the effectiveness of the proposed approach by enabling higher data rates while reducing the overhead, especially at high (i.e., vehicular) mobility.