11 papers
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 across distributed edge dev…
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…
REVERB-FL: Server-Side Adversarial and Reserve-Enhanced Federated Learning for Robust Audio Classification
Sathwika Peechara, Rajeev Sahay
Federated learning (FL) enables a privacy-preserving training paradigm for audio classification but is highly sensitive to client heterogeneity and poisoning attacks, where adversa…
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…
Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks
Ali Mohammadiasl, Bita Akram, Seyyedali Hosseinalipour +1
Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students an…
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,…