Multi-Modal Transformer and Reinforcement Learning-based Beam Management

• URL: http://arxiv.org/abs/2410.19859v1
• Date: Tue, 22 Oct 2024 21:44:25 GMT
• Title: Multi-Modal Transformer and Reinforcement Learning-based Beam Management
• Authors: Mohammad Ghassemi, Han Zhang, Ali Afana, Akram Bin Sediq, Melike Erol-Kantarci,
• Abstract summary: We propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In this work, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. Our proposed framework is tested on a 6G dataset.
• Score: 10.728362890819392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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