29 citations
19 papers
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 Flooding Attacks on 5G Radio Access Network Slicing
Yi Shi, Yalin E. Sagduyu
Network slicing manages network resources as virtual resource blocks (RBs) for the 5G Radio Access Network (RAN). Each communication request comes with quality of experience (QoE)…
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…
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
Yi Shi, Yalin E. Sagduyu, Tugba Erpek
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks an…
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
Brian Kim, Yalin E. Sagduyu, Tugba Erpek +2
We consider a wireless communication system, where a transmitter sends signals to a receiver with different modulation types while the receiver classifies the modulation types of t…