用多模态变换器+强化学习，动态预测最优波束组，提升6G通信效率。
Multi-Modal Transformer and Reinforcement Learning-based Beam Management
- 分两步：先用多模态变换器选波束组，再用强化学习精确定位波束。
- 在6G数据集上，波束预测准确率和系统吞吐量均优于单一方法。
- 适合研究6G波束管理、多模态学习与强化学习融合的科研人员。
波束管理是提升无线通信系统信号强度并降低干扰的重要技术。近年来，利用多种感知模态进行波束管理受到广泛关注，但高效处理多模态数据并提取有效信息仍是重大挑战。多模态变换器（MMT）能捕捉长程依赖关系，有效处理多模态数据，提供鲁棒的波束管理。结合强化学习（RL）可进一步增强其在动态环境中的适应性。本文提出一种两阶段波束管理方法，将MMT与RL结合用于动态波束索引预测。第一阶段将可用波束索引分为若干组，利用MMT处理多种数据模态，预测最优波束组；第二阶段在每组内采用强化学习实现快速波束决策，以最大化系统吞吐量。在6G数据集上的实验表明，该框架在波束预测准确率和系统吞吐量方面均优于仅使用MMT或仅使用RL的方法。
原文摘要 · Abstract (English)
Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the recently emerging multi-modal transformer (MMT) is a promising technique that can process multi-modal data by capturing long-range dependencies. While MMT is highly effective in handling multi-modal data and providing robust beam management, integrating reinforcement learning (RL) further enhances their adaptability in dynamic environments. In this work, we propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In the first step, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. In the second step, we employ RL for fast beam decision-making within each group, which in return maximizes throughput. Our proposed framework is tested on a 6G dataset. In this testing scenario, it achieves higher beam prediction accuracy and system throughput compared to both the MMT-only based method and the RL-only based method.
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