# Large Multimodal Model-Based Environment-Aware Mobility Management

arXiv: https://arxiv.org/abs/2607.09795 · HTML: https://mlsanity.com/paper/2607.09795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 9 Jul 2026.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [TinyLLaVA: A Framework of Small-scale Large Multimodal Models](https://mlsanity.com/paper/2402.14289/) (2024)
- [MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices](https://mlsanity.com/paper/2312.16886/) (2023)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [Efficient Large Multi-modal Models via Visual Context Compression](https://mlsanity.com/paper/2406.20092/) (2024)
- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences](https://mlsanity.com/paper/2305.00135/) (2023)
- [Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks](https://mlsanity.com/paper/1803.09698/) (2018)
- [Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems](https://mlsanity.com/paper/2111.14805/) (2021)
- [A Tutorial on 5G Positioning](https://mlsanity.com/paper/2311.10551/) (2023)
- [Role of Sensing and Computer Vision in 6G Wireless Communications](https://mlsanity.com/paper/2405.03945/) (2024)
- [A Non-stochastic Learning Approach to Energy Efficient Mobility Management](https://mlsanity.com/paper/1608.07891/) (2016)
- [Transformer-assisted Parametric CSI Feedback for mmWave Massive MIMO Systems](https://mlsanity.com/paper/2410.06504/) (2024)

## Built on by (most cited first, 1 of 1)

- [PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration](https://mlsanity.com/paper/2610.14855/) (2026)

## Abstract (arXiv)

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.