Abstract
Recently, large language models (LLMs) have been successfully adopted in various fields, including wireless communications, robotics, and autonomous vehicles, due to their outstanding adaptability and reasoning abilities. However, the application of LLMs for mobility management is relatively scarce, as it requires not only analyzing wireless measurements but also predicting dynamic user trajectories and making real-time handover decisions across densely deployed small base stations (SBSs).
In this paper, we propose an environment-aware mobility management scheme based on large multimodal models (LMMs), which extend the capabilities of LLMs to process multimodal sensing data. By leveraging LMMs, the proposed scheme extracts contextual information on the surrounding environments from RGB-D images to capture user equipment (UE) mobility patterns and identify signal reflections and blockages caused by static reflectors and dynamic obstacles.
Using the extracted environmental information, the proposed scheme learns the intrinsic mapping from UE and SBS positions to channel capacity, referred to as channel capacity map (CCM), from which future channel capacities along UE trajectories are predicted. Based on the predicted channel capacities, we determine proactive handover decisions that maximize the cumulative channel capacities. Simulation results demonstrate that the proposed scheme achieves substantial channel capacity improvements over conventional deep learning (DL)-based approaches.
Blogger's Review: This paper explores an innovative approach to mobility management using multimodal models, integrating environmental awareness with channel prediction. It highlights the potential of LLMs in complex dynamic environments, signaling advancements in future wireless communication technologies.