UAV-Aided Information and Energy Transmissions for Cognitive and Sustainable 5G Networks

• Published in: IEEE transactions on wireless communications Vol. 20; no. 3; pp. 1668 - 1683
• Main Authors: Che, Yueling, Lai, Yabin, Luo, Sheng, Wu, Kaishun, Duan, Lingjie
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Batteries; Cognitive radio; cognitive radio (CR); Communication system security; Constraints; Energy harvesting; Energy transfer; Energy transmission; Idling; Information transfer; Markov decision process (MDP); Markov processes; Optimization; Policies; Power management; Resource management; Unmanned aerial vehicle (UAV) aided communications; Unmanned aerial vehicles; Vehicle dynamics; Wireless communication; wireless energy transfer (WET); wireless information transfer (WIT); Wireless networks
• DOI: 10.1109/TWC.2020.3035425
• ISSN: 1536-1276

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV's mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV's sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV's transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks.