Contextual Learning-Based Wireless Power Transfer Beam Scheduling for IoT Devices
Contextual Learning-Based Wireless Power Transfer Beam Scheduling for IoT Devices
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DOI：
10.1109/jiot.2019.2930061
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发表时间：
2019-07
影响因子：
10.6
通讯作者：
Hyun-Suk Lee;Jang-Won Lee
中科院分区：
文献类型：
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作者：
Hyun-Suk Lee;Jang-Won Lee
在本文中，我们考虑物联网（IoT）系统，其中IoT设备在其可用功率不足时向功率信标（PB）请求功率，并且PB使用切换波束成形向IoT设备提供功率。我们研究了物联网系统在一比特反馈下的无线功率传输（WPT）波束调度，其目的是最大化满足功率请求的物联网设备的时间平均数量。为了实现这一点，我们提出了一种基于上下文学习的WPT波束调度算法与一位反馈（CWBO），学习信道信息，仅使用一位反馈信息，并利用它的波束调度。在CWBO内，波束方向图生成（BPG）问题应当在每个时隙中被解决。为了有效地解决这个问题，我们开发了一个基于单调优化的BPG算法，可以最优地解决BPG问题。此外，我们还开发了一个启发式BPG算法，具有较低的计算复杂度比单调优化为基础的BPG算法，同时提供相当的性能。对于CWBO在单设备WPT，我们证明了一个分析性能界，这表明它的长期平均性能方面的最优性，即使与一位反馈。此外，通过仿真结果，我们表明，我们的算法实现的性能接近的最佳波束调度策略在多设备WPT以及。这表明我们的算法可以用于WPT物联网系统，物联网设备由于其有限的功率而仅具有有限的反馈和信道信息估计能力。
In this paper, we consider Internet of Things (IoT) systems in which IoT devices request power to a power beacon (PB) when their available power is deficient and the PB provides power to the IoT devices using switched beamforming. We study wireless power transfer (WPT) beam scheduling for the IoT systems under one-bit feedback which aims at maximizing the time-average number of the IoT devices whose power requests are satisfied. To achieve this, we propose a contextual learning-based WPT beam scheduling algorithm with one-bit feedback (CWBO) that learns the channel information using only one-bit feedback information and exploits it for the beam scheduling. Within CWBO, a beam pattern generation (BPG) problem should be solved in each time slot. To efficiently solve it, we develop a BPG algorithm based on monotonic optimization that can optimally solve the BPG problem. In addition, we also develop a heuristic BPG algorithm that has a lower computational complexity than the monotonic optimization-based BPG algorithm, while providing comparable performance. For CWBO in single-device WPT, we prove an analytical performance bound, which shows its optimality in terms of the long-term average performance even with one-bit feedback. In addition, through the simulation results, we show that our algorithms achieve performances close to that of the optimal beam scheduling policy in multidevice WPT as well. This demonstrates that our algorithms can be used for WPT IoT systems with IoT devices having only limited capabilities for feedback and estimation of the channel information due to their limited power.