21 citations · 28 across the 7 of their papers we have counts for
7 papers
Dynamic Adaptive Federated Learning for mmWave Sector Selection
Lucas Pacheco, Torsten Braun, Kaushik Chowdhury +3
Beamforming techniques use massive antenna arrays to formulate narrow Line-of-Sight signal sectors to address the increased signal attenuation in millimeter Wave (mmWave). However,…
Learning from the Best: Active Learning for Wireless Communications
Nasim Soltani, Jifan Zhang, Batool Salehi +3
Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert…
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
Batool Salehi, Utku Demir, Debashri Roy +4
Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous com…
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard
Debashri Roy, Batool Salehi, Stella Banou +7
Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguratio…
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
Batool Salehi, Guillem Reus-Muns, Debashri Roy +5
Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed with…
Machine Learning on Camera Images for Fast mmWave Beamforming
Batool Salehi, Mauro Belgiovine, Sara Garcia Sanchez +3
Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards s…
Open-World Class Discovery with Kernel Networks
Zifeng Wang, Batool Salehi, Andrey Gritsenko +3
We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two…