Deep learning-based (DL-based) channel state information (CSI) feedback for a Massive multiple-input multiple-output (MIMO) system has proved to be a creative and efficient application. However, the existing systems ignored the wireless channel environment variation sensing, e.g., indoor and outdoor scenarios. Moreover, systems training requires excess pre-labeled CSI data, which is often unavailable. In this letter, to address these issues, we first exploit the rationality of introducing semi-supervised learning on CSI feedback, then one semi-supervised CSI sensing and feedback Network (S2CsiNet) with three classifiers comparisons is proposed. Experiment shows that S2CsiNet primarily improves the feasibility of the DL-based CSI feedback system by \textbf{\textit{indoor}} and \textbf{\textit{outdoor}} environment sensing and at most 96.2\% labeled dataset decreasing and secondarily boost the system performance by data distillation and latent information mining.
@article{arxiv.2110.06142,
title = {CSI Sensing and Feedback: A Semi-Supervised Learning Approach},
author = {Haozhen Li and Boyuan Zhang and Xin Liang and Haoran Chang and Xinyu Gu and Lin Zhang},
journal= {arXiv preprint arXiv:2110.06142},
year = {2021}
}