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Hauptverfasser: Zhang, Ping, Meng, Rui, Xu, Xiaodong, Wang, Yaheng, Huang, Zixuan, Liu, Yiming, Zhang, Ruichen, Liu, Yinqiu, Tong, Haonan, Song, Huishi, Wu, Gang, Lu, Zhaoming, Kang, Jiawen, Sun, Geng, Du, Qinghe, Yang, Zhaohui, Zhang, Jingxuan, Meng, Han, Xu, Lexi, Zhao, Haitao, Fei, Zesong, Zhou, Yiqing, Xiao, Pei, Tao, Meixia, Zhang, Qinyu, Cui, Shuguang, Tafazolli, Rahim
Format: Preprint
Veröffentlicht: 2026
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2603.24328
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Inhaltsangabe:
  • The International Telecommunication Union (ITU) identifies "Artificial Intelligence (AI) and Communication" as one of six key usage scenarios for 6G. Agentic AI, characterized by its ca-pabilities in multi-modal environmental sensing, complex task coordination, and continuous self-optimization, is anticipated to drive the evolution toward agent-based communication net-works. Semantic communication (SemCom), in turn, has emerged as a transformative paradigm that offers task-oriented efficiency, enhanced reliability in complex environments, and dynamic adaptation in resource allocation. However, comprehensive reviews that trace their technologi-cal evolution in the contexts of agent communications remain scarce. Addressing this gap, this paper systematically explores the role of semantics in agent communication networks. We first propose a novel architecture for semantic-based agent communication networks, structured into three layers, four entities, and four stages. Three wireless agent network layers define the logical structure and organization of entity interactions: the intention extraction and understanding layer, the semantic encoding and processing layer, and the distributed autonomy and collabora-tion layer. Across these layers, four AI agent entities, namely embodied agents, communication agents, network agents, and application agents, coexist and perform distinct tasks. Furthermore, four operational stages of semantic-enhanced agentic AI systems, namely perception, memory, reasoning, and action, form a cognitive cycle guiding agent behavior. Based on the proposed architecture, we provide a comprehensive review of the state-of-the-art on how semantics en-hance agent communication networks. Finally, we identify key challenges and present potential solutions to offer directional guidance for future research in this emerging field.