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The aim is to develop a framework for optimizing semantic communication in multi-agent systems under varying conditions.

May 10, 2026SensorsOpen Access

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

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The aim is to develop a framework for optimizing semantic communication in multi-agent systems under varying conditions.
Designed a COmparative learning Joint Optimal (COJO) semantic communication framework.
Optimized image reconstruction and classification for multi-task semantic objectives.
Implemented a task-driven end-to-end communication training scheme.
Achieved improved overall task performance in semantic communication under dynamic environments.
Significantly reduced transmitted data volume using a training-based compression mask.
Maintained key semantic information despite channel constraints.

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Randomized trial demonstrates improved task performance in multi-agent systems, suggesting effective communication strategies.

Yang et al. (2026) studied this question.

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