A Joint Communication and Computation Design for Probabilistic Semantic Communications
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View abstract on PubMed
Summary
This summary is machine-generated.This study optimizes resource allocation in probabilistic semantic communication (PSC) networks. A novel algorithm maximizes user data rates by efficiently managing transmission and computation resources.
Area Of Science
- Wireless Communication
- Information Theory
- Optimization Theory
Background
- Multi-user communication systems generate large data volumes.
- Semantic communication offers efficient data compression via knowledge graphs.
- Resource allocation is critical for optimizing performance in communication networks.
Purpose Of The Study
- To investigate joint transmission and computation resource allocation in multi-user probabilistic semantic communication (PSC) networks.
- To maximize the sum of equivalent user rates under power and semantic resource constraints.
- To address the non-convex optimization challenge posed by computation load.
Main Methods
- Data compression using semantic information extraction and shared probability graphs.
- Formulation of resource allocation as an optimization problem.
- Development of a three-stage algorithm to solve for BS beamforming, user transmit power, and semantic compression ratios.
Main Results
- The proposed three-stage algorithm effectively solves the complex optimization problem.
- Numerical results demonstrate the scheme's ability to improve resource allocation.
- The method balances transmission and computation loads for enhanced network performance.
Conclusions
- The proposed joint resource allocation scheme is effective for PSC networks.
- The developed algorithm provides a viable solution for optimizing performance in semantic communication systems.
- This work contributes to efficient resource management in future communication networks.
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