Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications

Area of Science:

• Wireless Communications Engineering
• Network Optimization
• Graph Theory Applications

Background:

• Resource allocation (RA) is critical for wireless network throughput maximization.
• Graph-based solutions offer higher efficiency but pose scalability challenges.
• Existing research covers cellular, device-to-device, and cognitive radio networks.

Purpose of the Study:

• To comprehensively review and analyze graph-based RA methods in key wireless network types.
• To propose a novel framework for integrated space and terrestrial communications.
• To introduce a design process for resource management algorithms.

Main Methods:

• Literature review and analysis of graph-based RA techniques.
• Development of the Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) concept using hypergraphs.
• Introduction of a graph-based design process for resource management.

Main Results:

• Identified design characteristics and future research directions for graph-based RA in cellular, D2D, and CRN.
• Proposed GRIST to model inter-connectivity in integrated space and terrestrial networks.
• Detailed implementation challenges for GRIST and a complementary design scheme.

Conclusions:

• Graph-based RA is a vital area with evolving challenges and opportunities.
• GRIST offers a promising hypergraph-based approach for integrated space and terrestrial networks.
• A structured design process is essential for balancing resource management considerations.