activity
20152023
most citedmmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment

19 citations · 35 across the 34 of their papers we have counts for

collaborators
Showing 2020Show all

11 papers · 1 filter

eess.SP2020

Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Enes Krijestorac, Samer Hanna, Danijela Cabric

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired b…

cs.CR2020

Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack

Samurdhi Karunaratne, Enes Krijestorac, Danijela Cabric

Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authentic…

cs.DC2020

Hybrid Vehicular and Cloud Distributed Computing: A Case for Cooperative Perception

Enes Krijestorac, Agon Memedi, Takamasa Higuchi +3

In this work, we propose the use of hybrid offloading of computing tasks simultaneously to edge servers (vertical offloading) via LTE communication and to nearby cars (horizontal o…

eess.SP2020

Band Assignment in Ultra-Narrowband (UNB) Systems for Massive IoT Access

Enes Krijestorac, Ghaith Hattab, Petar Popovski +1

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink tran…

eess.SP202019 cited

mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment

Han Yan, Benjamin W. Domae, Danijela Cabric

Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased…

eess.SP20201 cited

True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems

Veljko Boljanovic, Han Yan, Chung-Ching Lin +4

The best beam steering directions are estimated through beam training, which is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications.…