139 citations · 509 across the 82 of their papers we have counts for
12 papers · 1 filter
End-to-End Autoencoder Communications with Optimized Interference Suppression
Kemal Davaslioglu, Tugba Erpek, Yalin E. Sagduyu
An end-to-end communications system based on Orthogonal Frequency Division Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding and modulation) a…
Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces
Brian Kim, Tugba Erpek, Yalin E. Sagduyu +1
By moving from massive antennas to antenna surfaces for software-defined wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control th…
Autoencoder-based Communications with Reconfigurable Intelligent Surfaces
Tugba Erpek, Yalin E. Sagduyu, Ahmed Alkhateeb +1
This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep n…
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…
Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning
Yi Shi, Yalin E. Sagduyu
An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier. Machine learning (ML) provides powerful means to class…
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.…
Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction
Nof Abuzainab, Muhammad Alrabeiah, Ahmed Alkhateeb +1
We consider the problem of proactive handoff and beam selection in Terahertz (THz) drone communication networks assisted with reconfigurable intelligent surfaces (RIS). Drones have…
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…
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…
How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning
Yi Shi, Yalin E. Sagduyu, Tugba Erpek +1
In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the…