141 citations · 438 across the 53 of their papers we have counts for
55 papers
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…
When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
Yalin E. Sagduyu, Tugba Erpek
Semantic communication (SemCom) with learned encoder-decoder architectures enables end-to-end learning of compact task-oriented representations optimized for the wireless channel,…
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…
How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu +1
This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmi…
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…
Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then trans…
Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
Kemal Davaslioglu, Sastry Kompella, Tugba Erpek +1
Deep Reinforcement Learning (DRL) has been highly effective in learning from and adapting to RF environments and thus detecting and mitigating jamming effects to facilitate reliabl…
I-SCOUT: Integrated Sensing and Communications to Uncover Moving Targets in NextG Networks
Utku Demir, Kemal Davaslioglu, Yalin E. Sagduyu +3
Integrated Sensing and Communication (ISAC) represents a transformative approach within 5G and beyond, aiming to merge wireless communication and sensing functionalities into a uni…
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…
Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning
Yalin E. Sagduyu, Tugba Erpek
Low-Power Wide-Area Network (LPWAN) technologies, such as LoRa, have gained significant attention for their ability to enable long-range, low-power communication for Internet of Th…
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…
Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance
Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek +1
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while col…
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…
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks
Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus +1
This paper highlights vulnerabilities of deep learning-driven semantic communications to backdoor (Trojan) attacks. Semantic communications aims to convey a desired meaning while t…
Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks
Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus +1
Semantic communications seeks to transfer information from a source while conveying a desired meaning to its destination. We model the transmitter-receiver functionalities as an au…