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A Novel Network Optimization Scheme Based on Anti-Flocking and Improved Nash Equilibrium Algorithm
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DOI：10.1109/ACCESS.2023.3316024.png)
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摘要
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Unmanned Aerial Vehicle (UAV) has very wide application prospect in aiding terrestrial cellular network communication, but it remains a challenge to optimize UAV locations and maximize user service rate during deployment. In this paper, a novel network optimization scheme based on anti-flocking model and improved Nash Equilibrium (NE) algorithm is proposed by studying the problem of dynamic UAV deployment and backhaul transmission. Firstly, the UAV-adaptive algorithm based on gray wolf optimization (U-GWO) is used to predeploy UAVs with limited number of UAVs. Secondly, ground mobile users are tracked by building a UAV-based anti-flocking (U-AF) model. Then, during the tracking of ground users by UAV, an improved NE strategy is used to establish the backhaul transmission links between UAVs, ground BSs and other UAVs to ensure that deployed UAVs can maximize the service rate and effective backhaul transmission rate of ground users. Simulation results show that the average service rate of User Equipment (UE) with U-GWO algorithm is improved from 1 % to 5.77 % compared to other different swarm intelligence optimization algorithms. And the service rate obtained with U-AF algorithm is 43.2 % improved compared to the baseline scenario without U-AF algorithm. For UAV backhaul transmission link construction, the simulation results show that the proposed improved NE strategy improves the average effective backhaul transmission rate by 12 %, the minimum backhaul transmission rate by 84 % and the overall iteration number by 5 % on average compared to a pure NE strategy.
Keyword:
Autonomous aerial vehicles
Backhaul networks
Optimization
Interference
Wireless communication
Simulation
Signal to noise ratio
Nash equilibrium
Anti-flocking
gray wolf optimization
relay
unmanned aerial vehicles
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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