Resource Allocation and Interference Coordination Strategies in Heterogeneous Dual-Layer Satellite Networks

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 4; p. 1005
• Main Authors: Li, Jinhong, Chai, Rong, Zhou, Tianyi, Liang, Chengchao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2025; MDPI
• Subjects: Algorithms; Artificial satellites; Data transmission; Edge computing; Efficiency; heterogeneous satellite networks; interference coordination; LEO satellite networks; Mathematical optimization; Optimization; resource allocation; Satellite communications; Software; Spectrum allocation; Streaming media; Virtual private networks; Switzerland; heterogeneous satellite networks; interference coordination; resource allocation; LEO satellite networks
• DOI: 10.3390/s25041005
• ISSN: 1424-8220

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference.