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In this paper, we consider multiple solar-powered wireless nodes whichutilize the harvested solar energy to transmit collected data to multipleunmanned aerial vehicles (UAVs) in the uplink. In this context, we jointlydesign UAV flight trajectories, UAV-node communication associations, and uplinkpower control to effectively utilize the harvested energy and manage co-channelinterference within a finite time horizon. To ensure the fairness of wirelessnodes, the design goal is to maximize the worst user rate. The joint designproblem is highly non-convex and requires causal (future) knowledge of theinstantaneous energy state information (ESI) and channel state information(CSI), which are difficult to predict in reality. To overcome these challenges,we propose an offline method based on convex optimization that only utilizesthe average ESI and CSI. The problem is solved by three convex subproblems withsuccessive convex approximation (SCA) and alternative optimization. We furtherdesign an online convex-assisted reinforcement learning (CARL) method toimprove the system performance based on real-time environmental information. Anidea of multi-UAV regulated flight corridors, based on the optimal offline UAVtrajectories, is proposed to avoid unnecessary flight exploration by UAVs andenables us to improve the learning efficiency and system performance, ascompared with the conventional reinforcement learning (RL) method. Computersimulations are used to verify the effectiveness of the proposed methods. Theproposed CARL method provides 25% and 12% improvement on the worst user rateover the offline and conventional RL methods.

UAV Trajectory, User Association and Power Control for Multi-UAV Enabled  Energy Harvesting Communications: Offline Design and Online Reinforcement  Learning