Resource Slicing with Cross-Cell Coordination in Satellite-Terrestrial Integrated Networks

TL;DR

This paper introduces a novel resource slicing scheme with cross-cell coordination for satellite-terrestrial integrated networks, optimizing resource use and delay performance amid dynamic service demands and satellite mobility.

Contribution

It proposes a distributed resource slicing scheme with a hybrid data-model approach, including reinforcement learning and optimization algorithms, for scalable management in STIN.

Findings

Outperforms benchmark methods in resource efficiency

Reduces service delay effectively

Adapts to spatiotemporal dynamics

Abstract

Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we propose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimization problem and propose a distributed resource slicing (DRS) scheme for scalable and flexible resource management across different cells. Specifically, a hybrid data-model co-driven approach is developed, including an asynchronous multi-agent reinforcement learning-based algorithm to determine the optimal satellite set serving each cell and a distributed optimization-based algorithm to make the resource reservation decisions for each slice. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and delay performance.