用深度强化学习动态调整天线波束，让移动用户通信更稳更快。
DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users
- 基于深度强化学习自适应调节杜尔夫-切比雪夫天线阵列波束
- 仿真显示数据速率逼近理论最优值，性能接近理想解
- 无需额外硬件或信道估计，适合高复杂度移动场景
随着下一代通信系统中人工智能技术的兴起，机器学习因其在高维、非平稳优化问题中的出色表现而发挥关键作用，尤其适用于动态环境下的高效计算。定向波束成形是典型应用之一，可通过基于学习的盲波束成形技术实现，该技术利用基站获取的用户设备现有射频指纹，无需额外硬件或信道与角度估计。然而，当用户数量和天线维度增加时，问题复杂度急剧上升，学习过程愈发困难，导致学习方法性能难以逼近最优解。为此，本文提出一种基于深度强化学习的盲波束成形方法，采用可调的杜尔夫-切比雪夫天线阵列，能够动态调整波束以适应移动用户。仿真结果表明，所提方法可实现接近理论最优的数据速率。
原文摘要 · Abstract (English)
With the emergence of AI technologies in next-generation communication systems, machine learning plays a pivotal role due to its ability to address high-dimensional, non-stationary optimization problems within dynamic environments while maintaining computational efficiency. One such application is directional beamforming, achieved through learning-based blind beamforming techniques that utilize already existing radio frequency (RF) fingerprints of the user equipment obtained from the base stations and eliminate the need for additional hardware or channel and angle estimations. However, as the number of users and antenna dimensions increase, thereby expanding the problem's complexity, the learning process becomes increasingly challenging, and the performance of the learning-based method cannot match that of the optimal solution. In such a scenario, we propose a deep reinforcement learning-based blind beamforming technique using a learnable Dolph-Tschebyscheff antenna array that can change its beam pattern to accommodate mobile users. Our simulation results show that the proposed method can support data rates very close to the best possible values.
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