K
Kunal Sankhe
Researcher at Northeastern University
Publications - 27
Citations - 955
Kunal Sankhe is an academic researcher from Northeastern University. The author has contributed to research in topics: Communication channel & Transmitter. The author has an hindex of 9, co-authored 27 publications receiving 426 citations. Previous affiliations of Kunal Sankhe include International Institute of Information Technology, Hyderabad & Sardar Patel Institute of Technology.
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Journal ArticleDOI
Deep Learning Convolutional Neural Networks for Radio Identification
TL;DR: A method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques, demonstrating up to 90-99 percent experimental accuracy at transmitter- receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel.
Proceedings ArticleDOI
ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
TL;DR: This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices using only IQ samples at the physical layer with near-perfect device classification accuracy.
Journal ArticleDOI
Deep Learning for RF Fingerprinting: A Massive Experimental Study
Tong Jian,Bruno Costa Rendon,Emmanuel Ojuba,Nasim Soltani,Zifeng Wang,Kunal Sankhe,Andrey Gritsenko,Jennifer G. Dy,Kaushik R. Chowdhury,Stratis Ioannidis +9 more
TL;DR: This is the first work, to the best of the knowledge, reporting on RF fingerprinting and scalability issues on very large device populations, in the range of 50-10,000 devices.
Journal ArticleDOI
No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments
Kunal Sankhe,Mauro Belgiovine,Fan Zhou,Luca Angioloni,Frank Restuccia,Salvatore D'Oro,Tommaso Melodia,Stratis Ioannidis,Kaushik R. Chowdhury +8 more
TL;DR: This paper presents ORACLE, a novel system based on convolutional neural networks to identify a unique radio from a large pool of devices by deep-learning the fine-grained hardware impairments imposed by radio circuitry on physical-layer I/Q samples, and proposes an impairment hopping spread spectrum (IHOP) technique that is resilient to spoofing attacks.
Journal ArticleDOI
Beyond 5G: THz-Based Medium Access Protocol for Mobile Heterogeneous Networks
TL;DR: In this article, a MAC protocol design that switches among the aforementioned bands for data transmissions, falling back on the slower link each time for the reverse channel ACKs is presented.