# Large Models Enabled Ubiquitous Wireless Sensing

arXiv: https://arxiv.org/abs/2411.18277 · HTML: https://mlsanity.com/paper/2411.18277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms](https://mlsanity.com/paper/2307.07319/) (2023)
- [Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?](https://mlsanity.com/paper/2210.08770/) (2022)
- [Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin](https://mlsanity.com/paper/2401.08023/) (2024)
- [Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systems](https://mlsanity.com/paper/2202.02492/) (2022)
- [Sparse Channel Estimation with Gradient-Based Algorithms: A comparative Study](https://mlsanity.com/paper/1812.04196/) (2018)
- [Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems](https://mlsanity.com/paper/1912.10573/) (2019)
- [Massive MIMO Channel Prediction Using Machine Learning: Power of Domain Transformation](https://mlsanity.com/paper/2208.04545/) (2022)

## Abstract (arXiv)

In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functionalities such as adaptive modulation. We review existing methodologies for CSI estimation, emphasizing the shift from traditional to data-driven approaches. Then a novel framework for spatial CSI prediction using realistic environment information is proposed, and experimental results demonstrate the effectiveness. This research paves way for innovative strategies in managing wireless networks.