141 citations · 438 across the 53 of their papers we have counts for
7 papers · 1 filter
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…