4.7 Article

Joint Cache Placement, Flight Trajectory, and Transmission Power Optimization for Multi-UAV Assisted Wireless Networks

Journal

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 19, Issue 8, Pages 5389-5403

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.2992926

Keywords

Trajectory; Throughput; Optimization; Wireless networks; Base stations; Relays; UAV communication; cache placement; power control; trajectory design; throughput improvement

Funding

  1. National Natural Science Foundation of China [61701230]
  2. Natural Science Foundation of Jiangsu Province [BK20170805]
  3. Fundamental Research Funds for the Central Universities [NE2018107]
  4. Singapore MOE Tier 1 [2017-T1-002-007 RG122/17]
  5. MOE Tier 2 [MOE2014-T2-2-015 ARC4/15]
  6. Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (WASP/NTU) [M4082187 (4080)]
  7. Singapore EMA Energy Resilience [NRF2017EWT-EP003-041]
  8. Singapore NRF [NRF2015-NRF-ISF001-2277]

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It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

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