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[2602.17096] Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence
Summary
The paper explores the evolution of 6G wireless communication, emphasizing the shift towards intent-aware, autonomous systems that adapt to user needs and environmental changes using agentic AI and large language models.
Why It Matters
As wireless technology advances towards 6G, understanding user intent and environmental dynamics becomes crucial for optimizing communication systems. This research highlights the potential of AI-driven solutions to enhance network performance and user experience, paving the way for more responsive and efficient wireless communication.
Key Takeaways
- 6G systems require a shift from rule-based to intent-driven autonomous intelligence.
- User requirements are multi-dimensional and can change over time, necessitating adaptable solutions.
- Large language models can effectively interpret user intent and optimize network decisions.
- The paper presents AgenCom, a case study demonstrating intent-driven link decision-making.
- Key challenges include multimodal perception and sustainable optimization in network management.
Computer Science > Artificial Intelligence arXiv:2602.17096 (cs) [Submitted on 19 Feb 2026] Title:Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence Authors:Zhaoyang Li, Xingzhi Jin, Junyu Pan, Qianqian Yang, Zhiguo Shi View a PDF of the paper titled Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence, by Zhaoyang Li and 4 other authors View PDF HTML (experimental) Abstract:As 6G wireless systems evolve, growing functional complexity and diverse service demands are driving a shift from rule-based control to intent-driven autonomous intelligence. User requirements are no longer captured by a single metric (e.g., throughput or reliability), but by multi-dimensional objectives such as latency sensitivity, energy preference, computational constraints, and service-level requirements. These objectives may also change over time due to environmental dynamics and user-network interactions. Therefore, accurate understanding of both the communication environment and user intent is critical for autonomous and sustainably evolving 6G communications. Large language models (LLMs), with strong contextual understanding and cross-modal reasoning, provide a promising foundation for intent-aware network agents. Compared with rule-driven or centrally optimized designs, LLM-based agents can integrate heterogeneous information and translate natural-language intents into executable control...