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Large Models Enabled Ubiquitous Wireless Sensing

arXiv 2411.18277 ↗First posted 27 Nov 2024cs.LGcited by 0

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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.

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References · 10

  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  2. 2024LLM4CP: Adapting Large Language Models for Channel Predictioncited by 42
  3. 2020Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learningcited by 21
  4. 2023The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platformscited by 20
  5. 2022Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?cited by 3
  6. 2024Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twincited by 3
  7. 2022Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systemscited by 2
  8. 2018Sparse Channel Estimation with Gradient-Based Algorithms: A comparative Studycited by 1
  9. 2019Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systemscited by 1
  10. 2022Massive MIMO Channel Prediction Using Machine Learning: Power of Domain Transformationcited by 1

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