UAV Trajectory Optimization in a Post-Disaster Area Using Dual Energy-Aware Bandits.

Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV's flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints.

Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach.

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.