4.7 Article

Fair and Energy-Efficient Coverage Optimization for UAV Placement Problem in the Cellular Network

期刊

IEEE TRANSACTIONS ON COMMUNICATIONS
卷 70, 期 6, 页码 4222-4235

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2022.3170615

关键词

Quality of service; Optimization; Wireless communication; Resource management; Autonomous aerial vehicles; Receiving antennas; Cellular networks; UAV-aided wireless cellular network; UAV placement problem; fair coverage; backhaul constraint; proximal stochastic gradient descent algorithm

资金

  1. National Natural Science Foundation of China [U21A20456, 61822104]
  2. National Key R&D Program of China [2019YFB1803304]
  3. Fundamental Research Funds for the Central Universities [FRF-BD-20-11A]
  4. Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB [BK19AF005]
  5. 111 Project [B170003]

向作者/读者索取更多资源

This paper investigates the optimization problem of UAV base station placement and proposes an accurate and efficient algorithm to solve the problem. The algorithm ensures fair coverage and energy efficiency while satisfying backhaul constraints. Experimental results demonstrate that the algorithm performs well in different scenarios.
Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

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