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arXiv · 2206.07949

AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback

Han Xiao·Zhiqin Wang·Dexin Li·Wenqiang Tian·Xiaofeng Liu·Wendong Liu·Shi Jin·Jia Shen·Zhi Zhang·Ning Yang
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Abstract

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided. Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed. Moreover, a series of potential enhancements for deep learning based (DL-based) CSI feedback including i) data augmentation, ii) loss function design, iii) training strategy, and iv) model ensemble are introduced. The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided, which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.

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Han Xiao, Zhiqin Wang, Dexin Li, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang. 2022-06-16. AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback. https://arxiv.org/abs/2206.07949

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