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

Technical Deep Dive: Agentic Wireless Communication for 6G

Overview

This paper presents an overview of how agentic AI can enable intent-driven, autonomous, and sustainable 6G wireless communications. The key contributions are:

• Reviewing representative 6G physical-layer tasks and the limitations of existing approaches in supporting intent awareness and autonomous operation
• Proposing agentic AI as a foundational paradigm for 6G wireless systems, and highlighting its key capabilities including intent-aware decision making, autonomous exploration, closed-loop interaction, and cross-module coordination
• Discussing the challenges and enabling technologies for deploying agentic AI in wireless communications, including domain-adaptive pretraining, structured action modeling, and hybrid learning with domain knowledge
• Introducing the AgenCom architecture, an intent-aware communication agent that can dynamically adapt physical-layer link configurations to diverse user preferences and channel conditions

Methodology

Intent-Aware Perception and Representation

• AgenCom jointly observes channel state information (CSI) and user intent expressed in natural language
• This multimodal perception allows associating physical-layer states with semantic intent, enabling user-centric link decisions

LLM-Based Domain-Adaptive Policy Network

• AgenCom's policy network is centered around large language models (LLMs) for reasoning over intent and context
• A lightweight domain adapter injects physical-layer priors to improve controllability and executability

Structured Action Generation

• AgenCom generates a complete link construction strategy by selecting a coherent set of physical-layer configurations
• Link decisions are structured as a sequence of interrelated sub-actions to maintain scalability

Learning-Based Adaptation and Interaction

• AgenCom invokes a physical-layer tool executor to evaluate candidate link configurations under the current environment
• The returned performance metrics are used as reward signals for learning and policy refinement
• A memory module stores historical high-quality samples to improve adaptation speed and decision consistency

Results and Interpretation

Comparison of Output Strategies for Different User Intents

• Under a reliability-oriented intent, AgenCom selects more conservative and robust physical-layer configurations, prioritizing low bit error rate (BER) over throughput
• With a throughput-oriented intent, AgenCom chooses more aggressive modulation and coding to maximize data rate, at the cost of higher BER
• The energy-aware intent leads AgenCom to reduce power consumption by adopting more conservative link configurations, while maintaining reasonable BER and throughput

User Interaction Example

• AgenCom receives the user's intent (e.g., high-throughput) and current channel state information (CSI)
• It then outputs the corresponding communication link decision, demonstrating its ability to translate user preferences into executable physical-layer strategies

Limitations and Uncertainties

The paper acknowledges several key challenges in applying agentic AI to wireless communications:

• Knowledge-domain mismatch between general-purpose AI models and specialized wireless concepts
• Handling heterogeneous and non-stationary wireless environments
• Scaling with large and structured action spaces
• Addressing multi-objective and intent-driven optimization requirements
• Ensuring safety, reliability, and interpretability for operational wireless networks

What Comes Next

To realize agentic AI at scale for 6G, the authors highlight the need for further research in areas such as:

• Domain-adaptive pretraining and representation learning
• Parameter-efficient fine-tuning and alignment techniques
• Reinforcement learning with structured action modeling
• Hybrid learning that integrates domain knowledge and external tools
• Curriculum learning and staged training approaches

Addressing these challenges will be crucial to translate the potential of intent-aware agents into practical, reliable, and sustainable 6G network deployments.