Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence

2026-02-19

AI Summary

This work addresses the challenge of dynamically adapting to users’ multidimensional intents—such as latency sensitivity and energy consumption preferences—in 6G systems, a capability absent in conventional physical layers due to their lack of autonomous intent comprehension. To bridge this gap, the paper introduces, for the first time, a large language model (LLM)-driven agent into the 6G physical layer, establishing a closed-loop framework that spans natural language–based intent perception, cross-modal understanding, cross-layer autonomous decision-making, and link-level adaptive control. The proposed AgenCom agent enables continuous, intent-driven optimization of communication links, demonstrating the feasibility and efficacy of an intent-aware physical layer under diverse user preferences and channel conditions, thereby overcoming the limitations of traditional rule-based or centralized optimization approaches.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

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 and configuration decisions. Focusing on a closed-loop pipeline of intent perception, autonomous decision making, and network execution, this paper investigates agentic AI for the 6G physical layer and its realization pathways. We review representative physical-layer tasks and their limitations in supporting intent awareness and autonomy, identify application scenarios where agentic AI is advantageous, and discuss key challenges and enabling technologies in multimodal perception, cross-layer decision making, and sustainable optimization. Finally, we present a case study of an intent-driven link decision agent, termed AgenCom, which adaptively constructs communication links under diverse user preferences and channel conditions.

Problem

• 6G
• intent-aware
• physical-layer intelligence
• agentic AI
• autonomous communication

Innovation

• Agentic AI
• Intent-aware communication
• 6G physical layer
• Large Language Models (LLMs)
• Autonomous wireless networks