@inproceedings{54bda25fc8894f548ab674a988127bf6,
title = "When Transformer Meets CSI Feedback in mMIMO Systems: A Lightweight CsiMobileViT Approach",
abstract = "Accurate channel state information (CSI) feedback is essential in frequency division duplex massive multiple-input multiple-output systems, but increasing antennas cause the CSI matrix to grow exponentially, leading to significant feedback overhead. Inspired by the success of Transformers in natural language processing, recent Transformer-based CSI feedback methods have achieved excellent performance, though often with high computational costs that hinder real-time deployment on terminal devices. To address this challenge, in this paper, we present CsiMobileVit, a lightweight network that lowers computational complexity while maintaining reconstruction accuracy. The method achieves a good balance between simplicity and accuracy, making it practical for resource-limited devices. Extensive experiments confirm the effectiveness of this network.",
keywords = "CSI feedback, deep learning, massive MIMO, MobileViT, Transformer",
author = "Xiangyu Cen and Lam, \{Chan Tong\} and Ng, \{Benjamin K.\} and Ke Wang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE 102nd Vehicular Technology Conference, VTC 2025 ; Conference date: 19-10-2025 Through 22-10-2025",
year = "2025",
doi = "10.1109/VTC2025-Fall65116.2025.11310376",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE 102nd Vehicular Technology Conference, VTC 2025-Fall - Proceedings",
address = "United States",
}