Energy-Aware 3D Unmanned Aerial Vehicle Deployment for Network Throughput Optimization

• Published in: IEEE transactions on wireless communications Vol. 19; no. 1; pp. 563 - 578
• Main Authors: Chou, Shih-Fan, Pang, Ai-Chun, Yu, Ya-Ju
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D deployment; Algorithms; Batteries; Broadband; Cellular communication; cellular network; Cellular networks; Computer simulation; Energy management; Flight altitude; Flight plans; Lagrangian dual relaxation; maneuvering power; Mobile computing; non-convex non-linear optimization; Optimization; Three-dimensional displays; Throughput; Tradeoffs; Travel time; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Wireless communication; Wireless networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2019.2946822

Introducing mobile small cells to next generation cellular networks is nowadays a pervasive and cost-effective way to fulfill the ever-increasing mobile broadband traffic. Being agile and resilient, unmanned aerial vehicles (UAVs) mounting small cells are deemed emerging platforms for the provision of wireless services. As the residual battery capacity available to UAVs determines the lifetime of an airborne network, it is essential to account for the energy expenditure on various flying actions in a flight plan. The focus of this paper is therefore on studying the 3D deployment problem for a swarm of UAVs, with the goal of maximizing the total amount of data transmitted by UAVs. In particular, we address an interesting trade-off among flight altitude, energy expense and travel time. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. Afterwards, we prove the optimality of a special case derived from the convexification transformation. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can benefit from the proper treatment of the trade-off.