论文标题
基于多力增强学习基于能量波束形成控制
Multiagent Reinforcement Learning based Energy Beamforming Control
论文作者
论文摘要
超低功率设备使远场无线功率转移成为可行的能量输送的可行选择，尽管指数衰减。电磁梁是由电台构建的，因此无线能量在超低功率设备周围定向集中。由于缺乏对渠道状态的反馈，能量波束形成与信息光束形成相比，面临不同的挑战。已经提出了各种方法，例如一位通道的反馈，以增强能量光束的能力，但是它仍然具有相当大的计算开销，需要集中计算。在来回传输控制信息时浪费了宝贵的资源和时间。在本文中，我们为基于代码书的光束形成控制提出了一种新型的多构钢筋学习（MARL）配方。它利用了无线电动网络中的继承分布结构，并为完全计算的局部梁控制算法奠定了地面工作。源代码可以在https://github.com/bailiping/wirelesspowertransfer上找到。
Ultra low power devices make far-field wireless power transfer a viable option for energy delivery despite the exponential attenuation. Electromagnetic beams are constructed from the stations such that wireless energy is directionally concentrated around the ultra low power devices. Energy beamforming faces different challenges compare to information beamforming due to the lack of feedback on channel state. Various methods have been proposed such as one-bit channel feedback to enhance energy beamforming capacity, yet it still has considerable computation overhead and need to be computed centrally. Valuable resources and time is wasted on transfering control information back and forth. In this paper, we propose a novel multiagent reinforcement learning(MARL) formulation for codebook based beamforming control. It takes advantage of the inherienntly distributed structure in a wirelessly powered network and lay the ground work for fully locally computed beam control algorithms. Source code can be found at https://github.com/BaiLiping/WirelessPowerTransfer.
