Elbir, Ahmet MusabMishra, Kumar Vijay2020-04-302020-04-3020199781728106922https://dx.doi.org/10.1109/APUSNCURSINRSM.2019.8888753https://hdl.handle.net/20.500.12684/203IEEE Antennas and Propagation Society (APS); The Institute of Electrical and Electronics Engineers (IEEE)2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 -- 7 July 2019 through 12 July 2019 -- 153911In this paper, we propose a deep-learning-based for joint antenna selection and hybrid beamformer design problem in mmWave massive MIMO systems. In this respect, we treat both problems as a classification problem. We design two convolutional neural networks (CNNs) which accept the input as the channel matrix and it yields the output as the optimum antenna subarray. The selected part of channel matrix is fed to the second CNN which gives the output as the analog and baseband beamformers. We evaluate the performance of the proposed approach through numerical simulations and show that our CNN framework provides significantly better performance as compared to the conventional techniques such as orthogonal matching pursuit. © 2019 IEEE.en10.1109/APUSNCURSINRSM.2019.8888753info:eu-repo/semantics/closedAccessConference Object15851586