Green Probabilistic Semantic Communication over Wireless Networks
Ruopeng Xu, Zhaohui Yang, Yijie Mao, Chongwen Huang, Qianqian Yang,, Lexi Xu, Wei Xu, Zhaoyang Zhang

TL;DR
This paper introduces a multi-user green semantic communication system using probabilistic knowledge graphs, optimizing energy efficiency through semantic compression and rate-splitting multiple access technology.
Contribution
It proposes a novel probabilistic semantic communication framework with a semantic compression model and an optimization algorithm for energy-efficient multi-user wireless networks.
Findings
System outperforms SDMA and NOMA methods in simulations.
Semantic compression improves transmission efficiency.
Optimization reduces overall energy consumption.
Abstract
In this paper, we propose a multi-user green semantic communication system facilitated by a probabilistic knowledge graph (PKG). By integrating probability into the knowledge graph, we enable probabilistic semantic communication (PSC) and represent semantic information accordingly. On this basis, a semantic compression model designed for multi-user downlink task-oriented communication is introduced, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Based on the rate-splitting multiple access (RSMA) technology, we derive mathematical expressions for system transmission energy consumption and related formulations. Subsequently, the multi-user green semantic communication system is modeled and the optimal problem with the goal of minimizing system energy consumption comprehensively considering…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Cognitive Computing and Networks
