3 citations · 11 across the 19 of their papers we have counts for
11 papers · 1 filter
Physics-Informed Direction-of-Arrival Estimation Over Distributed Edge Devices
Nathan Tatsuta, Rajeev Sahay
Direction-of-arrival (DoA) estimation is a fundamental array processing task that has benefited substantially from deep learning. Deploying such methods using data distributed acro…
A Speculative GLRT-Backed Approach for Robust Deep Learning-Based Array Processing
Nian-Cin Wang, Rajeev Sahay
Deep learning (DL) has recently emerged as an efficient approach for array processing tasks such as signal detection and direction of arrival. However, DL models lack statistical g…
Robust Eavesdropping in the Presence of Adversarial Communications for RF Fingerprinting
Andrew Yuan, Rajeev Sahay
Deep learning is an effective approach for performing radio frequency (RF) fingerprinting, which aims to identify the transmitter corresponding to received RF signals. However, bey…
An Uncertainty Quantification Framework for Deep Learning-Based Automatic Modulation Classification
Huian Yang, Rajeev Sahay
Deep learning has been shown to be highly effective for automatic modulation classification (AMC), which is a pivotal technology for next-generation cognitive communications. Yet,…
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting
Kasra Borazjani, Kiarash Kianfar, Seyyedali Hosseinalipour +1
Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity of) the corresponding transmit…
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Su Wang, Rajeev Sahay, Adam Piaseczny +1
Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversaria…
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Su Wang, Rajeev Sahay, Christopher G. Brinton
There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating…
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders
Rajeev Sahay, Minjun Zhang, David J. Love +1
Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable…
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
Rajeev Sahay, Swaroop Appadwedula, David J. Love +1
Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment. Signal processing theory yields techniques such as the g…
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Rajeev Sahay, Christopher G. Brinton, David J. Love
Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into tra…
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Rajeev Sahay, Christopher G. Brinton, David J. Love
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.…