Agentic Link Construction for Environment and Intent Aware 6G Communication

TL;DR

This paper introduces a multimodal reinforcement learning model leveraging pretrained large language models to optimize link construction in 6G networks, considering both physical conditions and user intents for personalized, adaptive communication strategies.

Contribution

It proposes a novel decision-making framework that semantically aligns channel information and user instructions, enabling dynamic, personalized link construction in complex environments.

Findings

Model outperforms traditional algorithms under challenging conditions.

Achieves robust, efficient, and personalized communication strategies.

Significantly improves end-to-end communication performance.

Abstract

The emergence of sixth-generation networks heralds an intelligent communication ecosystem driven by the rapid proliferation of intelligent services and increasingly complex communication scenarios. However, current physical-layer designs-typically following modular and isolated optimization paradigms-fail to achieve global end-to-end optimality due to neglected inter-module dependencies. Although large language models (LLMs) have recently been applied to communication tasks such as beam prediction and resource allocation, existing studies remain limited to single-task or single-modality scenarios and lack the ability to jointly reason over communication states and user intents for personalized strategy adaptation. To address these limitations, this paper proposes a novel multimodal communication decision-making model for link construction leveraging reinforcement learning on pretrained LLMs. The proposed model semantically aligns channel state information (CSI) and textual user instructions, enabling comprehensive understanding of both physical-layer conditions and communication intents. It then generates physically realizable, user-customized link construction that dynamically adapts to changing environments and preference tendencies. A two-stage reinforcement learning framework is employed: the first stage expands the experience pool via heuristic exploration and behavior cloning to obtain a near-optimal initialization, while the second stage fine-tunes the model through multi-objective reinforcement learning considering BER, throughput, and power consumption. Experimental results demonstrate that the proposed model significantly outperforms conventional planning-based algorithms under challenging channel conditions, achieving robust, efficient, and personalized end-to-end communication strategies.