141 citations · 438 across the 53 of their papers we have counts for
32 papers · 1 filter
Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robu…
Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels. The adoption of SemCom…
Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
Semantic communications conveys task-relevant meaning rather than focusing solely on message reconstruction, improving bandwidth efficiency and robustness for next-generation wirel…
Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning
Bahman Abolhassani, Tugba Erpek, Kemal Davaslioglu +2
Reactive jammers pose a severe security threat to robotic-swarm networks by selectively disrupting inter-agent communications and undermining formation integrity and mission succes…
Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networks
Utku Demir, Tugba Erpek, Yalin E. Sagduyu +2
In emerging networked systems, mobile edge devices such as ground vehicles and unmanned aerial system (UAS) swarms collectively aggregate vast amounts of data to make machine learn…
MULTI-SCOUT: Multistatic Integrated Sensing and Communications in 5G and Beyond for Moving Target Detection, Positioning, and Tracking
Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek +3
This paper presents a complete signal-processing chain for multistatic integrated sensing and communications (ISAC) using 5G Positioning Reference Signal (PRS). We consider a distr…
Relay Selection and User Equipment Admission in Resource-Efficient NextG Sidelink Communications
Yalin E. Sagduyu, Tugba Erpek, Sastry Kompella +1
5G/6G sidelink communications addresses the challenge of connecting outer UEs, which are unable to directly access a base station (gNodeB), through inner UEs that act as relays to…
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
Utku Demir, Yalin E. Sagduyu, Tugba Erpek +3
In connected and autonomous vehicles, machine learning for safety message classification has become critical for detecting malicious or anomalous behavior. However, conventional ap…
Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning
Yalin E. Sagduyu, Tugba Erpek
LoRa provides long-range, energy-efficient communications in Internet of Things (IoT) applications that rely on Low-Power Wide-Area Network (LPWAN) capabilities. Despite these meri…
Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of m…
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
Yalin E. Sagduyu, Tugba Erpek, Yi Shi
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learn…
Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
This paper introduces a deep learning approach to dynamic spectrum access, leveraging the synergy of multi-modal image and spectrum data for the identification of potential transmi…
Joint Sensing and Semantic Communications with Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
This paper explores the integration of deep learning techniques for joint sensing and communications, with an extension to semantic communications. The integrated system comprises…
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
This paper studies task-oriented, otherwise known as goal-oriented, communications, in a setting where a transmitter communicates with multiple receivers, each with its own task to…
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks
Yi Shi, Yalin E. Sagduyu, Tugba Erpek
Decentralized federated learning (DFL) is an effective approach to train a deep learning model at multiple nodes over a multi-hop network, without the need of a server having direc…
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…
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 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…
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…
Deep Learning for Wireless Communications
Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu +2
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with h…
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Yalin E. Sagduyu, Yi Shi, Tugba Erpek +4
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense s…
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks
Yalin E. Sagduyu, Yi Shi, Tugba Erpek
An adversarial deep learning approach is presented to launch over-the-air spectrum poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to predict…