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20102022
most citedMulti-Modal Beam Prediction Challenge 2022: Towards Generalization

11 citations · 30 across the 13 of their papers we have counts for

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7 papers · 1 filter

cs.IT2022

Spatially Sparse Precoding in Wideband Hybrid Terahertz Massive MIMO Systems

Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +2

In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conve…

cs.IT2022

Learning Perturbations for Soft-Output Linear MIMO Demappers

Daniel E. Worrall, Markus Peschl, Arash Behboodi +1

Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to th…

cs.IT20221 cited

Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO

Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +2

Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and e…

cs.IT20221 cited

MIMO-GAN: Generative MIMO Channel Modeling

Tribhuvanesh Orekondy, Arash Behboodi, Joseph B. Soriaga

We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distribution…

cs.IT2022

Neural RF SLAM for unsupervised positioning and mapping with channel state information

Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga +4

We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) va…

cs.IT2019

Sensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group

Arya Bangun, Arash Behboodi, Rudolf Mathar

In this paper, {the goal is to design deterministic sampling patterns on the sphere and the rotation group} and, thereby, construct sensing matrices for sparse recovery of band-lim…