TY - JOUR
T1 - Energy-Aware 3D Unmanned Aerial Vehicle Deployment for Network Throughput Optimization
AU - Chou, Shih Fan
AU - Pang, Ai Chun
AU - Yu, Ya Ju
N1 - Funding Information:
Manuscript received October 1, 2018; revised March 30, 2019 and July 28, 2019; accepted September 25, 2019. Date of publication October 17, 2019; date of current version January 8, 2020. The work of A.-C. Pang was supported in part by the Ministry of Science and Technology under Grant 108-2221-E-002-069-MY3, Grant 107-2923-E-002-006-MY3, and Grant 106-2221-E-002-MY2, in part by the National Taiwan University under Grant 108L880503, in part by the Ministry of Economic Affairs under Grant 107-EC-17-A-02-S5-007, in part by MOXA, and in part by Microsoft Research Asia. The work of Y.-J. Yu was supported in part by the Ministry of Science and Technology under Grant 107-2218-E-390-003-MY3. The associate editor coordinating the review of this article and approving it for publication was M. Li. (Corresponding author: Ai-Chun Pang.) S.-F. Chou is with the Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei 115, Taiwan (e-mail: [email protected]).
Funding Information:
The work of A.-C. Pang was supported in part by the Ministry of Science and Technology under Grant 108-2221-E-002-069-MY3, Grant 107-2923-E-002-006-MY3, and Grant 106-2221-E-002-MY2, in part by the National Taiwan University under Grant 108L880503, in part by the Ministry of Economic Affairs under Grant 107-EC-17-A-02-S5-007, in part by MOXA, and in part by Microsoft Research Asia. The work of Y.-J. Yu was supported in part by the Ministry of Science and Technology under Grant 107-2218-E-390-003-MY3.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
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U2 - 10.1109/TWC.2019.2946822
DO - 10.1109/TWC.2019.2946822
M3 - Article
AN - SCOPUS:85078363214
SN - 1536-1276
VL - 19
SP - 563
EP - 578
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 1
M1 - 8875002
ER -