Large Multimodal Model-Based Environment-Aware Mobility Management

2026-07-09

AI Summary

This work addresses the challenges of mobility management in dense small-cell deployments, where conventional approaches struggle to jointly handle wireless measurements, user trajectory prediction, and real-time handover decisions. To overcome these limitations, the study introduces large multimodal models (LMMs) into mobility management for the first time, leveraging environmental perception data such as RGB-D images to extract information about static reflectors and dynamic obstacles. This enables the construction of a Channel Capacity Map (CCM) that establishes a mapping from user location to channel capacity. Building upon predicted trajectories and their associated channel capacities, the authors devise a reinforcement learning–driven proactive handover strategy. Simulation results demonstrate that the proposed method significantly outperforms existing deep learning–based solutions, achieving substantial gains in cumulative channel capacity.

Abstract

Recently, large language models (LLMs) have been successfully adopted in various fields, including wireless communications, robotics, and autonomous vehicles, owing to their outstanding adaptability and reasoning abilities. Despite their huge potential, the application of LLMs for mobility management is relatively scarce since 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 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 maximizing the cumulative channel capacities. Simulation results demonstrate that the proposed scheme achieves substantial channel capacity improvements over conventional deep learning (DL)-based approaches.

Problem

• mobility management
• handover decision
• channel capacity
• small base stations
• signal blockage

Innovation

• Large Multimodal Models
• Environment-Aware Mobility Management
• Channel Capacity Map
• Proactive Handover
• RGB-D Sensing