Hierarchical Network Slicing for UAV-Assisted Wireless Networks With Deployment Optimization

• Published in: IEEE journal on selected areas in communications 2024-12, Vol.42 (12), p.3705-3718
• Main Authors: Wei, Fengsheng, Feng, Gang, Qin, Shuang, Peng, Youkun, Liu, Yijing
• Format: Article
• Language: English
• Subjects: Autonomous aerial vehicles; decomposition technique; Distance learning; distributed learning; Dynamic scheduling; Games; Heuristic algorithms; Network slicing; UAV assisted wireless networks; Vehicle dynamics
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2024.3459055

Unmanned aerial vehicle (UAV) has been recognized as a key supplement for terrestrial networks to meet the stringent requirements of the forthcoming 6G networks. However, a significant challenge lies in providing differentiated services through a common UAV network, without the need to deploy individual networks for each service type. In this paper, we consider the problem of joint network slicing and UAV deployment under dynamic wireless environments as well as the uncertain traffic demands. To overcome the challenges posed by the network dynamics, we propose an intelligent hierarchical UAV slicing framework that operates at two different time-scales. At the large time-scale, the problem of inter-slice resource slicing and UAV deployment is formulated as a mixed integer nonlinear program, and a decomposition technique is applied to resolve it. At the small time-scale, the problem of intra-slice resource adjustment is modeled as a stochastic game and a distributed learning algorithm is proposed to find its Nash Equilibrium. Simulation results demonstrate that the proposed framework is lightweight and outperforms a number of known benchmark algorithms in terms of system utility, throughput and transmission delay.