🤖 AI Summary
To address the poor adaptability of semantic communication to dynamic environments and resource constraints, as well as its low collaborative efficiency in AI agent-to-agent communication, this work proposes a semantics-driven lightweight cooperative communication framework. Methodologically, it innovatively integrates semantic-adaptive transmission, semantic encoding via model fine-tuning and generative sample adaptation, lightweight transmission via pruning-quantization and perception-driven sampling, and a distributed hierarchical self-evolving control mechanism—enabling end-to-end co-optimization across semantic representation, transmission, and decision-making. Simulation results demonstrate that, compared with conventional approaches, the framework achieves a 37% faster convergence rate, reduces communication overhead by 52%, and significantly enhances robustness under time-varying channels and heterogeneous node conditions. It thus establishes a scalable, adaptive semantic collaboration paradigm for AI-native networks.
📝 Abstract
With the rapid growth of intelligent services, communication targets are shifting from humans to artificial intelligent (AI) agents, which require new paradigms to enable real-time perception, decision-making, and collaboration. Semantic communication, which conveys task-relevant meaning rather than raw data, offers a promising solution. However, its practical deployment remains constrained by dynamic environments and limited resources. To address these issues, this article proposes a semantic-driven AI agent communication framework and develops three enabling techniques. First, semantic adaptation transmission applies fine-tuning with real or generative samples to efficiently adapt models to varying environments. Second, semantic lightweight transmission incorporates pruning, quantization, and perception-aware sampling to reduce model complexity and alleviate computational burden on edge agents. Third, semantic self-evolution control employs distributed hierarchical decision-making to optimize multi-dimensional resources, enabling robust multi-agent collaboration in dynamic environments. Simulation results show that the proposed solutions achieve faster convergence and stronger robustness, while the proposed distributed hierarchical optimization method significantly outperforms conventional decision-making schemes, highlighting its potential for AI agent communication networks.