Joint UAV Access and GEO Satellite Backhaul in IoRT Networks: Performance Analysis and Optimization
Yan Zhu, Weigang Bai, Min Sheng, Jiandong Li, Di Zhou, Zhu Han
Abstract
With the growing demand for communications in remote and dispersed areas, Internet-of-Remote Things (IoRT) networks with joint unmanned aerial vehicle (UAV) access and geostationary orbit (GEO) satellite backhaul hold great promise to provide sufficient access services to Internet-of-Things (IoT) users and devices. As the fundamental of the performance optimization of IoRT networks, the performance analysis sheds light on the relationship between the network performance (i.e., backlog, delay, and throughput) and access scale (i.e., the numbers of UAVs and UAV users). Aiming at the challenges brought by the complex network structure (i.e., two-level queuing network along with the converged traffic), we introduce the stochastic network calculus-based min-plus convolution and the leftover service to mathematically describe the complex structure. For the analytical challenges of the continuous-time arrival process and heterogeneous two-level link capacities, we innovatively prove their supermartingale features and further derive the closed-form expressions of the network backlog and delay bounds based on the martingale theory. To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. For the tractability, we propose a three-directional iterative (TDI) algorithm to search the optimal solution of the optimization problem. Simulation results verify the tightness of our performance bounds in contrast to the standard bound and the effectiveness of the proposed algorithm.