Synergizing Wireless Foundation Models and LLMs: A Multi-Task Framework for MA Systems

Abstract

While movable antennas (MAs) provide enhanced spatial flexibility through dynamic repositioning, they confront critical challenges in acquiring complete channel state information (CSI) and efficiently optimizing antenna positions. Encouragingly, wireless foundation models (WFMs) capture generalizable channel priors for high-quality CSI extrapolation, while large language models (LLMs) enable parameter-efficient, token-level optimization for wireless tasks. In this paper, we synergistically integrate a WFM and an LLM into a multi-task framework for MA systems. First, to address the mismatch between existing WFM pre-training and MA channel characteristics, we develop an MA-oriented ``ExtLWM''based on the large wireless model (LWM) architecture. Second, we design a unified ExtLWM-Connector-LLM architecture that flexibly adapts to different MA configurations without structural redesign. Third, we reformulate discrete MA positioning as a sequential decision-making problem, where candidate positions are represented by numerical tokens and optimized using group relative policy optimization (GRPO) to exploit the serial generation capability of the LLM. Finally, a multi-stage training strategy is developed to maintain multi-task performance under limited training samples while mitigating catastrophic forgetting. Simulation results show that ExtLWM can produce high-quality channel embeddings while outperforming various benchmarks in CSI extrapolation. Meanwhile, the proposed LLM-based approach achieves a favorable trade-off between performance and computational complexity.