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MOEA/D based UAV swarm deployment for wireless coverage
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DOI：10.1109/ICACI52617.2021.9435884.png)
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In recent years, unmanned aerial vehicles (UAVs) have been widely used as flying-based stations to provide wireless coverage services to ground users. Owing to the UAV's limited battery capacity and coverage range, its energy consumption or coverage have been explored by researchers. However, the existing research largely overlooks the tradeoff involved in optimizing UAV swarm deployment for wireless coverage over a ground area. This study considers homogeneous UAV deployment in a 3D space to provide sustainable wireless services as a multi-objective problem. We introduce three objectives: 1) minimize the total energy consumption while deploying a UAV to UAVs on duty, 2) minimize the number of UAVs, and 3) maximize the coverage rate of the target area. With the aim of achieving a better trade-off between these objectives, we adopt the framework of MOEA/D, which allows search progress cooperating with neighboring subproblems each other. Particularly, we introduce a single-tuple encoding scheme and genetic operators (i.e., selection, crossover, and mutation) to generate feasible optimal solutions. The simulations demonstrate that the proposed algorithm is effective and surpasses the improved SPEAII and NSGAII, which indicates that the approach is dependable in solving the proposed multi-objective optimization for UAV deployment.
Keyword:
Multi-objective optimization
Evolutionary algorithms
Unmanned aerial vehicles
Wireless coverage
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