11 citations · 23 across the 10 of their papers we have counts for
18 papers
PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems
Pranav Madadi, Jeongho Jeon, Joonyoung Cho +3
In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in th…
Robust Non-Coherent Beamforming for FDD Downlink Massive MIMO
François Rottenberg, Ming-Chun Lee, Thomas Choi +2
Designing beamforming techniques for the downlink (DL) of frequency division duplex (FDD) massive MIMO is known to be a challenging problem due to the difficulty of obtaining chann…
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
Zhou Zhou, Lingjia Liu, Shashank Jere +3
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC). We first introd…
Grip-Aware Analog mmWave Beam Codebook Adaptation for 5G Mobile Handsets
Ahmad AlAmmouri, Jianhua Mo, Boon Loong Ng +2
This paper studies the effect of the user hand grip on the design of beamforming codebooks for 5G millimeter-wave (mmWave) mobile handsets. The high-frequency structure simulator (…
Channel Extrapolation for FDD Massive MIMO: Procedure and Experimental Results
Thomas Choi, François Rottenberg, Jorge Gomez-Ponce +4
Application of massive multiple-input multiple-output (MIMO) systems to frequency division duplex (FDD) is challenging mainly due to the considerable overhead required for downlink…
Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G
Rubayet Shafin, Lingjia Liu, Vikram Chandrasekhar +4
Mobile Network Operators (MNOs) are in process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase…