Online learning-based beam and blockage prediction for indoor millimeter-wave communications

Xiao, 2017, Millimeter wave communications for future mobile networks, IEEE J. Sel. Areas Commun., 35, 1909, 10.1109/JSAC.2017.2719924 Marcus, 2005, Millimeter wave propagation: spectrum management implications, IEEE Microw. Mag., 6, 54, 10.1109/MMW.2005.1491267 M. Gapeyenko, et al., Analysis of human-body blockage in urban millimeter-wave cellular communications, in: Proc. IEEE ICC, 2016. R. MacCartney, S. Deng, S. Sun, T.S. Rappaport, Millimeter-wave human blockage at 73 GHz with a simple double knife-edge diffraction model and extension for directional antennas, in: Proc. IEEE VTC-Fall, 2017. A. Alkhateeb, I. Beltagy, S. Alex, Machine Learning for reliable mmwave Systems: Blockage Prediction and proactive Handoff, in: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018, pp. 1055–1059. Alrabeiah, 2019 Alrabeiah, 2020, Millimeter wave base stations with cameras: Vision aided beam and block-age prediction Cisco, 2018, 2017 Qualcomm, 2019 T. Shuang, T. Koivisto, H.-L. Määttänen, K. Pietikäinen, T. Roman, M. Enescu, Design and evaluation of LTE-Advanced double codebook, in: Proc. IEEE Veh. Technol. Conf., 2011. 3rd Generation Partnership Project, 2020 Va, 2018, Inverse multipath fingerprinting for millimeter wave V2I beam alignment, IEEE Trans. Veh. Technol., 67, 4042, 10.1109/TVT.2017.2787627 Lu, 2020, Positioning-aided 3D beamforming for enhanced communications in mmwave mobile networks, IEEE Access, 8, 55513, 10.1109/ACCESS.2020.2981815 Schmidhuber, 2015, Deep learning in neural networks: An overview, Neural Netw., 61, 85, 10.1016/j.neunet.2014.09.003 Pedrycz, 2019 Goodfellow, 2016