Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Soriaga, Joseph B."'
Autor:
Arnold, Maximilian, Major, Bence, Massoli, Fabio Valerio, Soriaga, Joseph B., Behboodi, Arash
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, allowing for a way to optimize the performance of a deployed system base
Externí odkaz:
http://arxiv.org/abs/2401.17781
Autor:
Hehn, Thomas M., Orekondy, Tribhuvanesh, Shental, Ori, Behboodi, Arash, Bucheli, Juan, Doshi, Akash, Namgoong, June, Yoo, Taesang, Sampath, Ashwin, Soriaga, Joseph B.
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 wireless channel properties based on map data. In this work, we present a
Externí odkaz:
http://arxiv.org/abs/2310.04570
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 conventional phase-shifter based hybrid precoding architecture ineffective.
Externí odkaz:
http://arxiv.org/abs/2211.14866
Autor:
Pezeshki, Hamed, Massoli, Fabio Valerio, Behboodi, Arash, Yoo, Taesang, Kannan, Arumugam, Boroujeni, Mahmoud Taherzadeh, Li, Qiaoyu, Luo, Tao, Soriaga, Joseph B.
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 reducing the spectral efficiency gap between analog and digital beamfo
Externí odkaz:
http://arxiv.org/abs/2211.02102
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 energy consumption. However, channel estimation in the context of HAD i
Externí odkaz:
http://arxiv.org/abs/2205.05202
We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distributions and represent the field data more faithfully. They are tractable and
Externí odkaz:
http://arxiv.org/abs/2203.08588
Autor:
Kadambi, Shreya, Behboodi, Arash, Soriaga, Joseph B., Welling, Max, Amiri, Roohollah, Yerramalli, Srinivas, Yoo, Taesang
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) values with no location information. The model is based on an encoder-de
Externí odkaz:
http://arxiv.org/abs/2203.08264
We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive power of neural networks. Instead of keeping a bank of Kalman filters and
Externí odkaz:
http://arxiv.org/abs/2109.12561
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
IEEE Transactions on Communications; 2023, Vol. 71 Issue: 6 p3679-3693, 15p
