Towards deep learning-aided wireless channel estimation and channel state information feedback for 6G

Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success DL stimulated increasing interest applying this paradigm to wireless channel estimation recent years. Since principles are inductive nature distinct from the conventional rule-based algorithms, when one tries use technique estimation, might easily get stuck confused by so many knobs control small details be aware of. The primary purpose paper is discuss key issues possible solutions DL-based state information (CSI) feedback including model selection, training data acquisition, neural network design for 6G. Specifically, we present several case studies together with numerical experiments demonstrate effectiveness framework.