The construction of ground base stations is often time-consuming and costly, especially in challenging areas such as disaster areas and military battlefields. With the advancement of unmanned aerial vehicle (UAV) technology, UAV-mounted aerial base stations have recently become increasingly attractive for fast and cost-effective communication establishment, and UAV deployment optimization has since are of homogeneous become a vital research problem. However, early studies tend to consider an oversimplified scenario where UAVs are of homogeneous capabilities, leading to limited applicabilities. We consider a more realistic and complex UAV deployment problem, where UAVs have heterogeneous capabilities. This new deployment problem strives to minimize the number of UAVs deployed while meeting the ground user coverage requirements as well as UAV-to-UAV connectivity requirement, by selecting the appropriate UAVs, calculating their three-dimensional (3D) positions, and adjusting their transmission powers. UAV positions are optimized for average air-to-ground channel quality improvement alongside. To tackle this NP-hard problem, we propose MO-BPSO-GA, which carefully adapts and combines multi-objective particle swarm optimization (MO-PSO), binary particle swarm optimization (BPSO), and genetic algorithm (GA). Simulation results demonstrate that MO-BPSO-GA swiftly converges to an outstanding solution in terms of the number of UAVs deployed, the potion of ground users covered, and the mean channel quality.


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    Title :

    Genetic Algorithm enabled Particle Swarm Optimization for Aerial Base Station Deployment


    Contributors:
    Zhang, Bo (author) / Song, Jinpeng (author) / Liu, Zhi (author) / Yang, Kunhao (author)


    Publication date :

    2021-09-01


    Size :

    447412 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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