Deep Learning Prediction of Beam Coherence Time for Near-FieldTeraHertz Networks
This work addresses beamforming overhead for mobile terahertz networks, offering a domain-specific incremental improvement.
The paper tackles the high overhead of beam alignment and tracking in mobile terahertz networks by introducing a novel beam coherence time and using a deep learning model to predict it, enabling higher data rates and reduced overhead, especially at high mobility.
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.