1 citations · 2 across the 7 of their papers we have counts for
7 papers
ReQuestNet: A Foundational Learning model for Channel Estimation
Kumar Pratik, Pouriya Sadeghi, Gabriele Cesa +5
In this paper, we present a novel neural architecture for channel estimation (CE) in 5G and beyond, the Recurrent Equivariant UERS Estimation Network (ReQuestNet). It incorporates…
Vision-Assisted Digital Twin Creation for mmWave Beam Management
Maximilian Arnold, Bence Major, Fabio Valerio Massoli +2
In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, al…
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental +7
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wir…
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…
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods
Hamed Pezeshki, Fabio Valerio Massoli, Arash Behboodi +6
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices. In this work, we aim at…
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…
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…