Výsledky vyhledávání - "Salehi, Batool"

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Learning from the Best: Active Learning for Wireless Communications

Autor: Soltani, Nasim, Zhang, Jifan, Salehi, Batool, Roy, Debashri, Nowak, Robert, Chowdhury, Kaushik

Collecting an over-the-air wireless communications training dataset for deep learning-based communication tasks is relatively simple. However, labeling the dataset requires expert involvement and domain knowledge, may involve private intellectual pro

Externí odkaz: http://arxiv.org/abs/2402.04896

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Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming

Autor: Salehi, Batool, Demir, Utku, Roy, Debashri, Pradhan, Suyash, Dy, Jennifer, Ioannidis, Stratis, Chowdhury, Kaushik

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 computing power. Such a software-based emulation of an entity that exists

Externí odkaz: http://arxiv.org/abs/2305.10350

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Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Autor: Roy, Debashri, Salehi, Batool, Banou, Stella, Mohanti, Subhramoy, Reus-Muns, Guillem, Belgiovine, Mauro, Ganesh, Prashant, Bocanegra, Carlos, Dick, Chris, Chowdhury, Kaushik

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radi

Externí odkaz: http://arxiv.org/abs/2203.16706

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Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

Autor: Salehi, Batool, Reus-Muns, Guillem, Roy, Debashri, Wang, Zifeng, Jian, Tong, Dy, Jennifer, Ioannidis, Stratis, Chowdhury, Kaushik

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 within short contact times. We solve this problem via a novel expediting b

Externí odkaz: http://arxiv.org/abs/2201.04712

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Machine Learning on Camera Images for Fast mmWave Beamforming

Autor: Salehi, Batool, Belgiovine, Mauro, Sanchez, Sara Garcia, Dy, Jennifer, Ioannidis, Stratis, Chowdhury, Kaushik

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 spend up to several milliseconds exploring different sector combination

Externí odkaz: http://arxiv.org/abs/2102.07337

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Open-World Class Discovery with Kernel Networks

Autor: Wang, Zifeng, Salehi, Batool, Gritsenko, Andrey, Chowdhury, Kaushik, Ioannidis, Stratis, Dy, Jennifer

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 critical challenges to addressing this paradigm: (a) transferring kno

Externí odkaz: http://arxiv.org/abs/2012.06957

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