[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85725-en":3,"doc-seo-85725-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85725,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Large Multimodal Model-Based Environment-Aware Mobility Management","The paper addresses limited use of large language models for mobility management, where systems must analyze wireless measurements, forecast dynamic user equipment trajectories, and make real-time handover decisions across ultra-dense small base stations. It proposes an environment-aware mobility management scheme using large multimodal models that extend LLMs to multimodal sensing. Context is extracted from RGB-D images to capture UE mobility patterns and detect reflections and blockages. Using this, the method learns a mapping to predict future channel capacities and selects proactive handover decisions maximizing cumulative capacity, improving results over conventional deep learning approaches.","Large Multimodal Model-Based Environment-Aware Mobility Management  \nSeokhyun Jeong, Student Member, IEEE, Sangmok Shin, Student Member, IEEE, Seungnyun Kim, Member, IEEE, Jiao Wu, Member, IEEE, and Byonghyo Shim, Fellow, IEEE  \narXiv :2607 .09795v 1 [ cs .IT] 9 Jul 2026  \nAbstract—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 reflectionsand 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.  \nIndex Terms—Large multimodal model, mobility management, handover, ultra-dense network, sensing.  \nI. INTRODUCTION  \nLarge language models (LLMs), such as ChatGPT and Gemini, are attracting great attention these days for their powerful capabilities to solve unlimited complex tasks [1] . Indeed,  \nReceived 23 September, 2025; revised 13 March, 2026 and 17 May, 2026; accepted 3 July, 2026 . This work was supported in part by the National Research Foundation (NRF) of Korea under Grant RS-2022-NR070834 and 2022M3C1A3099336, the Institute of Information & Communications Technology Planning & Evaluation(IITP)-ITRC(Information Technology Research Center) grant funded by the Korea government(MSIT)(IITP-2026-2021-0- 02048) and the National Research Foundation, Singapore and Infocomm Media Development Authority under its Communications and Connectivity Bridging Funding Initiative. An earlier version of this paper was presented in part at the IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, December 2025 . (Corresponding author: Byonghyo Shim.)  \nSeokhyun Jeong, Sangmok Shin, and Byonghyo Shim are with the Department of Electrical and Computer Engineering and the Institute of New Media and Communications, Seoul National University, Seoul 08826 Republic of Korea (e-mail: [shjeong@islab.snu.ac.kr](shjeong@islab.snu.ac.kr); [smshin@islab.snu.ac.kr](smshin@islab.snu.ac.kr);  \n[bshim@snu.ac.kr](bshim@snu.ac.kr)) .  \nSeungnyun Kim is with the Information Systems Technology and Design (ISTD) pillar, Singapore University of Technology and Design, Singapore 487372 (e-mail: [snkim94@mit.edu](snkim94@mit.edu)) .  \nJiao Wu is with the Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900 Saudi Arabia (e-mail: [jiao.wu@kaust.edu.sa](jiao.wu@kaust.edu.sa)).  \n\n| Historical UE positions  Bird's eye-view maps  SBS-view sensing images \u003Cbr>Predicted\u003Cbr>Proactive channel\u003Cbr> handover  capacities decision for all SBSs\u003Cbr>\u003Cbr> | Optimal SBS indices for next time slots |\n| --- | --- |\n\nFig. 1: Visualization of mobility management in UDN systems.  \nowing to the vast network size an","cbCaindK6eNnyvMw","https://ap.wps.com/l/cbCaindK6eNnyvMw","pdf",15648751,1,17,"English","en",105,"# Introduction\n## Mobility management in ultradense networks\n## Motivation for environment-aware models\n## Proposed environment-aware LMM framework","[{\"question\":\"Why is applying LLMs to mobility management challenging in ultra-dense networks?\",\"answer\":\"Mobility management requires not only analyzing wireless measurements, but also predicting dynamic UE trajectories and making real-time handover decisions among densely deployed small base stations.\"},{\"question\":\"How does the proposed method use large multimodal models for environment-aware mobility management?\",\"answer\":\"It extracts contextual information from RGB-D images to capture UE mobility patterns and identify signal reflections and blockages caused by static reflectors and dynamic obstacles.\"},{\"question\":\"How are proactive handover decisions determined in the proposed scheme?\",\"answer\":\"The system learns a channel capacity map from UE and SBS positions, predicts future channel capacities along UE trajectories, and selects handover decisions that maximize cumulative channel 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is applying LLMs to mobility management challenging in ultra-dense networks?","Question",{"text":75,"@type":76},"Mobility management requires not only analyzing wireless measurements, but also predicting dynamic UE trajectories and making real-time handover decisions among densely deployed small base stations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use large multimodal models for environment-aware mobility management?",{"text":80,"@type":76},"It extracts contextual information from RGB-D images to capture UE mobility patterns and identify signal reflections and blockages caused by static reflectors and dynamic obstacles.",{"name":82,"@type":73,"acceptedAnswer":83},"How are proactive handover decisions determined in the proposed scheme?",{"text":84,"@type":76},"The system learns a channel capacity map from UE and SBS positions, predicts future channel capacities along UE trajectories, and selects handover decisions that maximize cumulative 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