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Semantic Communication-Enabled Multi-Access Edge Computing Network Resource Optimization in the 6G Era
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AI
DOI：10.1109/MWC.2025.3600791.png)
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Abstract
En 中文
In semantic communication, artificial intelligence (AI) technologies are used to comprehend and anticipate dynamic fluctuations within multi-access edge computing (MEC) networks, thereby improving the utilization of wireless communication network resources. This communication paradigm shift is expected to address numerous intricate edge resource optimization challenges in the 6th-generation mobile network (6G) era. Within the forthcoming space–air–ground–sea integrated network framework, semantic data optimization and dynamic resource allocation are poised to enable cloud–edge–terminal collaborative task processing to be ultra-low latency, ultra-high reliability, and ultra-efficient. The efficient semantic communication-enabled MEC network architecture can be applied to applications across diverse scenarios, including intelligent transportation, smart healthcare, smart manufacturing, and smart homes. In this paper, we first investigate the architecture and main challenges of the semantic communication-enabled MEC network and then propose an edge computing resource optimization scheme for the Internet of vehicles (IoV) that is based on semantic communication. The simulation results indicate that semantic communication technology can significantly diminish transmission latency and network energy consumption in the intelligent IoV.
Keywords:
Semantic communication
multi-access edge computing
network resource optimization
6G
Internet of Vehicles
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