RUNs: Fast and Robust Network Slicing for UAV-Assisted Wireless Networks Under Imperfect CSI and Node Mobility

Uncrewed aerial vehicle (UAV) assisted wireless network (UAWN) is emerging as a promising architectural innovation for the provisioning of ubiquitous coverage and enhanced connectivity in the forthcoming 6G era. To accommodate the increasingly diversified services of 6G without deploying individual UAWNs for each service type, the integration of network slicing with UAWNs becomes essential. However, unlike terrestrial networks, the dynamic and uncertain network conditions caused by the mobility of the UAVs pose significant challenges to the UAWN slicing problem. In this paper, we investigate the UAWN slicing problem by jointly considering UAV deployment, channel allocation, and power allocation under uncertain network conditions including imperfect channel state information, uncertain user demand, and imprecise user location. As expected, this problem turns out to be a robust nonconvex mixed-integer problem, making it overwhelmingly difficult to solve. In light of the limited computing power of the UAV, we propose a lightweight optimization named RUNs, which jointly exploits problem decomposition, the augmented Lagrange method, and the batch coordinate descent method. We prove that the RUNs framework runs fast in the sense that it converges to the stationary point at a log-linear rate. Meanwhile, the numerical results demonstrate that RUNs has significant performance gains over existing benchmark solutions.