2020 IVADO Digital October: Hamed Hojatian

October 22, 2020: Tomorrow’s algorithms

Hamed Hojatian: Unsupervised deep learning for massive MIMO hybrid beamforming.

Summary: Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. We proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.

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Compte tenu des bruits de fond, des difficultés à comprendre l'interlocuteur et de la complexité du sujet. Un sous-titrage en français aurait été grandement apprécié.

Dommage compte tenu de l'importance que la 5G a actuellement et ds le futur.