Kim, GyungminJin, RuiFunk, WilsonKim, Seung-JunLim, Hyuk2024-03-272024-03-27http://hdl.handle.net/11603/32676The radio maps of multiantenna channel state information (CSI) are constructed using deep learning. The desired CSI is predicted for arbitrary locations in a geographical area based on the measurements collected at sampling locations. Such maps can be used to significantly reduce the overhead associated with CSI acquisition. A novel deep architecture is proposed, consisting of an encoder/decoder pair for transforming high-dimensional CSI features to lower-dimensional embeddings, together with a deep embedding interpolator for exploiting the spatial dependency of the CSI. Two important problem classes are tackled in a unified fashion, namely, CSI interpolation and prediction. Practical scenarios involving missing information are also considered. The efficacy of the proposed methods is verified by numerical tests.5 pagesen-USText