论文标题
AI启发了无线通信：用于CSI反馈的变压器骨架
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback
论文作者
论文摘要
本文基于第二届无线通信人工智能（AI）竞赛（WAIC）的背景，该竞赛由IMT-2020（5G）促销小组5G+AIWORK组主持，首先提供了基于EIGENVECTOR的渠道状态信息（CSI）反馈问题的框架。然后提出了提到EVCSINET-T的CSI反馈的基本变压器主链。此外，引入了一系列基于深度学习（基于DL的）CSI反馈的潜在增强功能，包括i）数据增强，ii）损失功能设计，iii）培训策略和iv）模型集合。进一步提供了涉及EVCSINET-T与传统代码簿方法之间比较的实验结果，进一步提供了对基于DL的CSI反馈问题的高级性能和有前途的变压器前景。
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.
