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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
Summary
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.
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.
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