用深度学习预测太赫兹波束相干时间，减少移动场景下的波束更新开销。
Deep Learning Prediction of Beam Coherence Time for Near-FieldTeraHertz Networks
- 用时序输入的前馈神经网络预测波束相干时间。
- 在高速移动场景下数据速率提升且开销降低。
- 适合车联网等高动态太赫兹通信系统使用。
大尺寸天线阵列结合精确波束成形对太赫兹（THz）通信的链路可靠性至关重要。然而，随着天线数量增加，移动网络中的波束对准与跟踪带来巨大开销。同时，近场区域随天线阵列尺寸和载波频率增大而扩展，需考虑球面波前而非传统平面波假设。本文提出一种新型波束相干时间模型，显著降低波束更新频率。进一步设计基于时序输入的简单前馈神经网络，实现波束相干时间的实时预测，并动态调整波束成形，仅引入极低开销。数值结果表明，该方法在高速（如车载）移动场景下能有效提升数据速率并降低开销。
原文摘要 · Abstract (English)
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.
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