Reinforcement learning of millimeter wave beamforming tracking over COSMOS platform
Reinforcement learning of millimeter wave beamforming tracking over COSMOS platform
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DOI：
10.1145/3556564.3558242
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Imtiaz Nasim;P. Skrimponis;A. Ibrahim;S. Rangan;I. Seskar
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文献类型：
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作者：
Imtiaz Nasim;P. Skrimponis;A. Ibrahim;S. Rangan;I. Seskar
通过利用高增益波束成形矢量（简称为波束），大带宽毫米波 (mmWave) 频段上的通信可以提供高数据速率。支持移动用户所需的此类光束的实时跟踪可以通过开发机器学习 (ML) 模型来实现。虽然计算机模拟被用来证明此类机器学习模型的成功，但实验结果仍然有限。因此，在本文中，我们在开源 COSMOS 测试平台上验证了毫米波光束跟踪的有效性。我们特别利用多臂老虎机（MAB）方案，该方案遵循强化学习（RL）方法。在我们基于 MAB 的波束跟踪模型中，波束选择被建模为一个动作，而算法的奖励则通过链路吞吐量建模。在基于 60 GHz COSMOS 的移动平台上进行的实验结果表明，在几个学习样本之后，与 Genie 辅助波束相比，基于 MAB 的波束跟踪学习模型可以实现近 92% 的吞吐量。
Communication over large-bandwidth millimeter wave (mmWave) spectrum bands can provide high data rate, through utilizing high-gain beamforming vectors (briefly, beams). Real-time tracking of such beams, which is needed for supporting mobile users, can be accomplished through developing machine learning (ML) models. While computer simulations were used to show the success of such ML models, experimental results are still limited. Consequently in this paper, we verify the effectiveness of mmWave beam tracking over the open-source COSMOS testbed. We particularly utilize a multi-armed bandit (MAB) scheme, which follows reinforcement learning (RL) approach. In our MAB-based beam tracking model, the beam selection is modeled as an action, while the reward of the algorithm is modeled through the link throughput. Experimental results, conducted over the 60-GHz COSMOS-based mobile platform, show that the MAB-based beam tracking learning model can achieve almost 92% throughput compared to the Genie-aided beams after a few learning samples.