Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network

• Published in: Aerospace Vol. 11; no. 12; p. 969
• Main Authors: Guo, Shaoxiong, Zhou, Li, Liang, Shijie, Cao, Kuo, Song, Zhiqun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: 3D deployment; Algorithms; Communication; Connectivity; Disaster recovery; Drone aircraft; Genetic algorithms; load balance; Load distribution; Mathematical optimization; Optimization; Optimization algorithms; Probability; Quality of service; Quality of service architectures; Robustness; SAGIN; Satellites; UAV; Unmanned aerial vehicles; Wireless networks
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace11120969

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network.