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Research on Multi-Agent Semantic Communication Framework Based on Comparative Learning Joint Optimization.

Hong Yang1, Hongyan Li1,2, Honggang Chen1

  • 1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Semantic Communication (SeC) framework for multi-agent (MA) systems, optimizing data transmission for enhanced collaborative tasks. The COJO framework improves performance by jointly optimizing reconstruction and classification, and reducing data volume through an enhanced compressor.

Keywords:
comparative learningjoint optimizationmulti-agentsemantic communication

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Communication Engineering
  • Computer Science

Background:

  • The evolution of intelligent services necessitates advanced communication for multi-agent (MA) systems.
  • Semantic Communication (SeC) offers a promising paradigm for efficient information exchange and understanding in MA systems.
  • Existing SeC methods face challenges due to dynamic environments and diverse MA task requirements.

Purpose of the Study:

  • To propose a novel Semantic Communication (SeC) framework, COmparative learning Joint Optimal (COJO), tailored for multi-agent (MA) systems.
  • To address the constraints of dynamic environments and diverse MA tasks in semantic information transmission.
  • To enhance the performance of MA systems in collaborative perception, reasoning, and decision-making.

Main Methods:

  • Jointly optimizing image reconstruction and classification for multi-task semantic objectives under varying channel conditions.
  • Designing an enhanced compressor utilizing input image features, compression ratio, task requirements, and channel conditions to generate a training-based mask for data reduction.
  • Developing a task-driven, end-to-end SeC training scheme to preserve crucial semantic information in multi-task scenarios under channel constraints.

Main Results:

  • Improved overall system task performance through joint optimization of reconstruction and classification.
  • Significant reduction in transmitted data volume via the enhanced compressor and training-based mask.
  • Effective prevention of key semantic information loss in multi-task scenarios despite channel constraints.

Conclusions:

  • The proposed COJO SeC framework effectively enhances MA system performance in complex communication scenarios.
  • The framework demonstrates robust data compression and semantic information preservation under dynamic channel conditions.
  • This work provides a significant advancement in SeC for real-time, collaborative MA systems.