82 citations · 113 across the 10 of their papers we have counts for
11 papers
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)…
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…
Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing
Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu
The spoofing attack is critical to bypass physical-layer signal authentication. This paper presents a deep learning-based spoofing attack to generate synthetic wireless signals tha…
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…
Coherent Communications in Self-Organizing Networks with Distributed Beamforming
Yi Shi, Yalin E. Sagduyu
Coherent communications aim to support higher data rates and extended connectivity at lower power consumption compared with traditional point-to-point transmissions. The typical se…
Cross-layer Design and SDR Implementation of DSA, Backpressure Routing and Network Coding
Sohraab Soltani, Yalin E. Sagduyu, Sean Scanlon +4
A cross-layer cognitive radio system is designed to support unicast and multicast traffic with integration of dynamic spectrum access (DSA), backpressure algorithm, and network cod…
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…
Deep Learning for Launching and Mitigating Wireless Jamming Attacks
Tugba Erpek, Yalin E. Sagduyu, Yi Shi
An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-…
Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks
Arun Sathanur, Mahantesh Halappanavar, Yi Shi +1
In many complex networked systems, such as online social networks, activity originates at certain nodes and subsequently spreads on the network through influence. In this work, we…
Joint Channel Training and Feedback for FDD Massive MIMO Systems
Wenqian Shen, Linglong Dai, Yi Shi +2
Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance ga…
Compressive sensing based differential channel feedback for massive MIMO
Wenqian Shen, Linglong Dai, Yi Shi +2
Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G wireless communications. Channel feedback for massive MIMO is challenging due to the substa…