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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

A Joint Communication and Computation Design for Probabilistic Semantic Communications.

Zhouxiang Zhao¹, Zhaohui Yang^1,2, Mingzhe Chen³

¹College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

Entropy (Basel, Switzerland)

|May 24, 2024

Summary

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.

Keywords:

knowledge graph probability graph resource allocation semantic communication

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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.