Elbir, Ahmet M.Papazafeiropoulos, AnastasiosKourtessis, PandelisChatzinotas, Symeon2021-12-012021-12-0120202162-23372162-2345https://doi.org/10.1109/LWC.2020.2993699https://hdl.handle.net/20.500.12684/10703This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.en10.1109/LWC.2020.2993699info:eu-repo/semantics/openAccessChannel estimationMIMO communicationComplexity theoryTrainingMachine learningSurface wavesArray signal processingDeep learningchannel estimationlarge intelligent surfacesmassive MIMOAntenna SelectionDesignArticle99144714512-s2.0-85091179272WOS:000569062000024Q1Q1