用位置信息提升毫米波波束预测精度，降低计算开销。
Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems
- 设计轻量级自编码器，利用位置信息实现波束预测
- 相比传统方法，计算复杂度降低83%，精度相当
- 适合6G智能感知与通信系统中的实时波束优化
集成感知与通信及毫米波（mmWave）已成为6G网络的关键技术。然而，毫米波波束方向狭窄，需精确对准，通常带来较大训练开销。通过结合位置信息进行波束调整，可有效降低该开销。本文提出一种轻量级自编码器（LAE），用于解决位置辅助的波束预测问题，相比传统全连接神经网络基准方法，显著降低了计算复杂度。所提LAE为三层欠完备网络，利用其降维能力减少模型训练开销。仿真结果表明，该模型在保持与基准方法相当的波束预测精度的同时，实现了83%的复杂度降低。
原文摘要 · Abstract (English)
Integrated sensing and communication and millimeter wave (mmWave) have emerged as pivotal technologies for 6G networks. However, the narrow nature of mmWave beams requires precise alignments that typically necessitate large training overhead. This overhead can be reduced by incorporating the position information with beam adjustments. This letter proposes a lightweight autorencoder (LAE) model that addresses the position-assisted beam prediction problem while significantly reducing computational complexity compared to the conventional baseline method, i.e., deep fully connected neural network. The proposed LAE is designed as a three-layer undercomplete network to exploit its dimensionality reduction capabilities and thereby mitigate the computational requirements of the trained model. Simulation results show that the proposed model achieves a similar beam prediction accuracy to the baseline with an 83% complexity reduction.
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