Agentic Link Construction for Environment and Intent Aware 6G Communication
This work addresses the problem of suboptimal communication performance in complex 6G scenarios for network operators and users, representing a novel method rather than an incremental improvement.
The paper tackles the problem of achieving global end-to-end optimality in 6G networks by addressing neglected inter-module dependencies in physical-layer designs, proposing a multimodal communication decision-making model that leverages reinforcement learning on pretrained LLMs to align channel state information and user instructions. The model significantly outperforms conventional planning-based algorithms under challenging channel conditions, achieving robust, efficient, and personalized communication strategies.
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.