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MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning
IEEE Journal on Selected Areas in Communications ( IF 16.4 ) Pub Date : 2021-05-10 , DOI: 10.1109/jsac.2021.3078492
Jingzhi Hu , Hongliang Zhang , Kaigui Bian , Marco Di Renzo , Zhu Han , Lingyang Song

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.

中文翻译:

MetaSensing:通过深度强化学习实现智能超表面辅助 RF 3D 传感

使用射频信号进行无线传感越来越受到关注。然而,由于无法控制的无线电环境中不希望有的多径衰落,RF 感测的准确性是有限的。在本文中,我们不是被动地适应环境,而是考虑部署智能超表面以感知 3D 对象的存在和位置的场景。通过对其波束形成器模式进行编程,超表面可以提供理想的传播特性。然而,实现高感测精度具有挑战性,因为它需要对波束形成器模式进行联合优化,并将接收到的信号映射到感测结果。为了应对这一挑战,我们制定了一个优化问题,以最小化传感结果的交叉熵损失,并提出了一种深度强化学习算法来联合计算最佳波束形成器模式和接收信号的映射。仿真结果验证了所提出算法的有效性,并展示了超表面和目标空间的大小如何影响传感精度。
更新日期:2021-06-18
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