Cross-layer energy efficiency optimization for semantic communication networks
Semantic communication focuses on the meaning of the transmitted information,which can significantly reduce the amount of data to be transmitted and improve the communication efficiency through semantic extraction,showing great potential in the future communication scenarios of smart devices.However,deep learning-enabled semantic codec further exacerbate the energy consumption of traditional communications.To address this problem,we propose a joint cross-layer optimization framework,and design a semantic energy efficiency metrics to evaluate the user's quality of experience and energy consumption of the global system.The optimization process is modeled as a partially observable Markov process.Jointly optimize power control in the physical layer and semantic compression allocation in the semantic layer:the power allocation is used to eliminate inter-cell interference,and the semantic compression level configuration is used to optimize the semantic transmission efficiency.Simulation results show that the proposed framework and algorithm can effectively solve the joint optimization problem of semantic and physical layers.
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