State-Aware Adaptive Beam Resource Management for Spectrum-Reuse LEO Satellite-Enabled IoT Networks

Abstract

Low Earth orbit (LEO) satellite networks provide wide-area access for remote Internet of Things (IoT) services, yet massive terminals, dynamic beam coverage, and strong interference coupling caused by spectrum reuse pose severe resource management challenges. Most existing approaches rely on historical traffic data or prediction models and struggle to make real-time decisions for bursty traffic without prior knowledge. This paper investigates access-state-driven joint beam-area association and spectrum block allocation to improve weighted service traffic, reduce blocking probability, and maintain fairness. The core innovation is the design of SSBRM, a scalable framework that completely eliminates the need for historical data and prediction models. SSBRM triggers decisions through real-time access-state scoring, incorporates weighted beam-area matching, under-service-aware fairness regulation, and interference-aware spectrum allocation, thereby enabling efficient resource reuse. Synthetic workloads are constructed to emulate remote IoT access scenarios with spatially non-uniform terminal deployment, periodic sensing reports, and event-driven bursty traffic. Simulation results show that under high load, SSBRM improves weighted service traffic by 23.5% over a load-greedy baseline and reduces blocking probability by 8.6% compared with a reuse baseline without interference awareness.