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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 proposes a multi-user green semantic communication system facilitated by a probabilistic knowledge graph (PKG), and proposes an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design, semantic compression ratio, and computation capacity allocation.

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 the computation and communication process under given constrains is formulated. In order to address the optimal problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design, semantic compression ratio, and computation capacity allocation. Simulation results validate the effectiveness of our approach, demonstrating the superiority of our system over methods using Space Division Multiple Access (SDMA) and non-orthogonal multiple access (NOMA) instead of RSMA, and highlighting the benefits of our PSC compression model.

Green Probabilistic Semantic Communication over Wireless Networks

TL;DR

This paper proposes a multi-user green semantic communication system facilitated by a probabilistic knowledge graph (PKG), and proposes an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design, semantic compression ratio, and computation capacity allocation.

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 the computation and communication process under given constrains is formulated. In order to address the optimal problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design, semantic compression ratio, and computation capacity allocation. Simulation results validate the effectiveness of our approach, demonstrating the superiority of our system over methods using Space Division Multiple Access (SDMA) and non-orthogonal multiple access (NOMA) instead of RSMA, and highlighting the benefits of our PSC compression model.
Paper Structure (16 sections, 57 equations, 9 figures, 1 table, 1 algorithm)

This paper contains 16 sections, 57 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model Design
  3. Probabilistic Knowledge Graph
  4. Semantic Information Extraction
  5. Semantic Information Compression
  6. Downlink RSMA Semantic Communication Model
  7. Problem Formulation and Algorithm Design
  8. Problem Formulation
  9. Joint Optimization of Power Allocation and Beamforming Design
  10. Semantic Compression Ratio Optimization
  11. Computation Capacity Allocation Optimization
  12. Algorithm Design and Analysis
  13. Simulation Results and Analysis
  14. Sub-problems Solving Verification
  15. Optimization Problem Simulation Results and Analysis
  16. ...and 1 more sections

Figures (9)

  • Figure 1: System model design of PSC aided multi-user green semantic communication
  • Figure 2: A sample of an entity pair containing multiple relations
  • Figure 3: Schematic diagram of probability-related parameter $\mathcal{N}$
  • Figure 4: Mapping relation between $\Omega$ and $L_1(\Omega)$
  • Figure 5: Simulation results of power allocation and beamforming design sub-problem solving verification
  • ...and 4 more figures