Elbir, Ahmet Musab2020-04-302020-04-3020191089-77981558-2558https://doi.org/10.1109/LCOMM.2019.2915977https://hdl.handle.net/20.500.12684/3089Elbir, Ahmet M./0000-0003-4060-3781WOS: 000475331600032Hybrid beamformer design is a crucial stage in millimeter-wave (mmWave) MIMO systems. In this letter, we propose a convolutional neural network (CNN) framework for the joint design of precoder and combiners. The proposed network accepts the input of channel matrix and gives the output of analog and baseband beamformers. Previous works are usually based on the knowledge of steering vectors of array responses which is not always accurately available in practice. The proposed CNN framework does not require such a knowledge, and it provides higher performance in capacity compared with the conventional greedy-and optimization-based algorithms.en10.1109/LCOMM.2019.2915977info:eu-repo/semantics/closedAccessmmWaveMIMOhybrid beamformingdeep learningconvolutional neural networkArticle23712401243WOS:000475331600032Q1Q2