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        Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

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        Deep_Channel_Learning_For_Large_Intelligent_Surfaces_Aided_mm_Wave_Massive_MIMO_Systems.pdf (PDF, 565Kb)
        Author
        Elbir, Ahmet M.
        Papazafeiropoulos, Anastasios
        Kourtessis, Pandelis
        Chatzinotas, Symeon
        Senior, John
        Attention
        2299/23259
        Abstract
        This 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.
        Publication date
        2020-09
        Published in
        IEEE Wireless Communications Letters
        Published version
        https://doi.org/10.1109/LWC.2020.2993699
        Other links
        http://hdl.handle.net/2299/23259
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