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.
@article{arxiv.2206.07949,
title = {AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback},
author = {Han Xiao and Zhiqin Wang and Dexin Li and Wenqiang Tian and Xiaofeng Liu and Wendong Liu and Shi Jin and Jia Shen and Zhi Zhang and Ning Yang},
journal= {arXiv preprint arXiv:2206.07949},
year = {2022}
}