141 citations · 226 across the 21 of their papers we have counts for
24 papers
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks
Yi Shi, Yalin E. Sagduyu, Tugba Erpek
NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault d…
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
Brian Kim, Yi Shi, Yalin E. Sagduyu +2
We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a de…
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning
Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek +1
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs, w…
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond
Brian Kim, Yalin E. Sagduyu, Tugba Erpek +1
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications.…
Adversarial Machine Learning for 5G Communications Security
Yalin E. Sagduyu, Tugba Erpek, Yi Shi
Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. A…
Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks
Tarun S. Cousik, Vijay K. Shah, Tugba Erpek +2
We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces t…