139 citations · 509 across the 82 of their papers we have counts for
45 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…
How to Discover Knowledge for FutureG: Contextual RAG and LLM Prompting for O-RAN
Nathan Conger, Nathan Scollar, Kemal Davaslioglu +2
We present a retrieval-augmented question answering framework for 5G/6G networks, where the Open Radio Access Network (O-RAN) has become central to disaggregated, virtualized, and…
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…
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
Yi Shi, Yalin E. Sagduyu
Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is…
Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning
Yalin E. Sagduyu
This paper presents a game-theoretic framework to study the interactions of attack and defense for deep learning-based NextG signal classification. NextG systems such as the one en…
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects
Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener
Communications systems to date are primarily designed with the goal of reliable transfer of digital sequences (bits). Next generation (NextG) communication systems are beginning to…
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems
Ziad El Jamous, Kemal Davaslioglu, Yalin E. Sagduyu
This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a Wi…
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning
Kemal Davaslioglu, Serdar Boztas, Mehmet Can Ertem +2
Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distri…
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 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)…