Toward Deterministic Satellite-Terrestrial Integrated Networks via Resource Adaptation and Differentiated Scheduling
Weiting Zhang, Peixi Liao, Dong Yang, Qiang Ye, Shiwen Mao, Hongke Zhang
Abstract
Satellite-terrestrial integrated network (STIN) is a full-scale communication paradigm, which can support joint information processing and seamless service provision by leveraging satellites' wide coverage and terrestrial networks' high capacity. The existing STIN operates with insufficient synergy in transmission scheduling, impacting resource allocation efficiency and transmission delay optimization, particularly in complex transmission scenarios. In this paper, we design <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Det</u>erministic STIN (DetSTIN), a novel architecture for STIN, along with two algorithms tailored for transmission scheduling to collaboratively optimize resource adaptation and service flow scheduling. Specifically, the DetSTIN enables the smooth interconnection and integration of heterogeneous networks by providing layered deterministic services. Besides, a genetic-based resource adaptation algorithm is designed for fixed-mobile-satellite heterogeneous networks to reduce resource allocation overhead while maintaining the network performance. Furthermore, we propose a deep reinforcement learning-based differentiated scheduling algorithm to solve the routing-queue two-dimensional decision problem to differentially optimize transmission delay of service flows, thus obtaining higher transmission scheduling benefit. By addressing resource adaptation and differentiated scheduling synergistically, the proposed solution achieves reduced resource allocation overhead and increased transmission scheduling benefit, ultimately leading to increased network operation revenue of the DetSTIN. Simulation results demonstrate that the proposed solution delivers effective performance across various flow proportions, and as the number of flows increases, the network operation revenue exhibits a noticeable improvement, compared with benchmark algorithms.