Elbir, Ahmet M.Mishra, Kumar Vijay2021-12-012021-12-012020978-1-7281-5478-72325-3789https://hdl.handle.net/20.500.12684/1061421st IEEE International Workshop on Signal Processing Advances in Wireless Communications (IEEE SPAWC) -- MAY 26-29, 2020 -- ELECTR NETWORKThe broadband millimeter-wave (mm-Wave) systems use hybrid beamformers with common analog beamformer for the entire band while employing different baseband beamformers in different frequency sub-bands. Furthermore, the performance mostly relies on the perfectness of the channel information. In this paper, we propose a deep learning (DL) framework for hybrid beamformer design in broadband mmWave massive MIMO systems. We design a convolutional neural network (CNN) that accepts the channel matrix of all subcarriers as input and the output of CNN is the hybrid beamformers. The proposed CNN architecture is trained with imperfect channel matrices in order to provide robust performance against the deviations in the channel data. Hence, the proposed precoding scheme can handle the imperfect or limited feedback scenario where the full and exact knowledge of the channel is not available. We show that the proposed DL framework is more robust and computationally less complex than the conventional optimization and phase-extraction-based approaches.eninfo:eu-repo/semantics/closedAccessDeep learninghybrid beamformingmassive MIMOmillimeter-wave communicationswidebandChannel EstimationAntenna SelectionWaveDesignConference Object2-s2.0-85090395670WOS:000620337500116N/AN/A