학술논문

Semantic Communication-Aware End-to-End Routing in Large-Scale LEO Satellite Networks
Document Type
Conference
Source
2024 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom) METACOM Metaverse Computing, Networking, and Applications (MetaCom), 2024 IEEE International Conference on. :137-142 Aug, 2024
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Satellites
Spectral efficiency
Knowledge based systems
Bandwidth
Performance gain
Routing
Throughput
Polynomials
Delays
Multimedia communication
Satellite networks
semantic communication
temporal graph
integer programming
graph theory
Language
Abstract
Enhanced by inter-satellite links, large-scale satellite networks (SNs) hold promise to deliver low-latency services globally. However, given the scarcity of available spectrum and the capacity limitations of Shannon’s classic information theory, supporting the ever-growing multimedia communication services poses a challenge for SNs. A promising direction is to employ semantic communication (SC), which utilizes artificial intelligence (AI) to extract and transmit the "meaning" of raw data, thereby reducing bandwidth consumption. We identify that SC requires the raw data to be transmitted to a satellite equipped with the AI encoder supporting the same knowledge base for successful decoding at the destination. This advanced functionality renders traditional routing schemes such as contact graph routing (CGR) not seamlessly suitable. Hence, we develop the SC-aware routing (SCR) strategy aimed at optimizing SC’s delay and bandwidth consumption. Firstly, we define the problem using integer linear programming (ILP) and observe its intractability in practice. Then, we use the temporal graph to represent diverse knowledge bases, semantic encoders and transmission resources of SNs, and design SCR based on it. Simulations conducted on the Starlink constellation show that SCR runs much faster than the ILP solver. Additionally, we verify that SC can bring significant performance gains in terms of higher data throughput and lower average end-to-end delay when compared to CGR.