发表机构
Department of Electrical and Computer Engineering and the Institute of New Media and Communications, Seoul National University; Information Systems Technology and Design (ISTD) pillar, Singapore University of Technology and Design; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology（电气电子工程系和新媒体与通讯研究所，首尔国立大学; 信息与系统技术与设计（ISTD）部门，新加坡科技设计大学; 计算机、电子与数学科学与工程系，卡塔尔科学与技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LLMs在移动性管理应用少的问题，提出基于大型多模态模型的环境感知移动性管理方案，利用LMMs提取环境信息，学习信道容量图预测未来信道容量，据此确定主动切换决策，相比传统DL方法显著提升了信道容量。
AI 中文摘要
近年来，大语言模型（LLMs）凭借其出色的适应性和推理能力在包括无线通信、机器人和自动驾驶车辆等多个领域成功应用。然而，LLMs在移动性管理方面的应用相对较少，因为它不仅需要分析无线测量，还需预测动态用户轨迹并在密集部署的小基站（SBSs）间做出实时切换决策。本文提出一种基于大型多模态模型（LMMs）的环境感知移动性管理方案，LMMs扩展了LLMs处理多模态传感数据的能力。该方案通过利用LMMs从RGB-D图像中提取周围环境的上下文信息，以捕获用户设备（UE）移动模式并识别由静态反射器和动态障碍物引起的信号反射和阻挡。利用提取的环境信息，学习从UE和SBS位置到信道容量的内在映射，即信道容量图（CCM），并据此预测UE轨迹上的未来信道容量。基于预测的信道容量确定主动切换决策，以最大化累积信道容量。仿真结果表明，该方案比传统基于深度学习（DL）的方法在信道容量上有显著提升。
英文摘要
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.
