12 citations · 46 across the 13 of their papers we have counts for
13 papers
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning
Xiangyu Zhong, Xiaojun Yuan, Huiyuan Yang +1
With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and priv…
Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning
Ziyang Lu, Chen Zhong, M. Cenk Gursoy
Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless netwo…
A Predictive Online Transient Stability Assessment with Hierarchical Generative Adversarial Networks
Rui Ma, Sara Eftekharnejad, Chen Zhong +1
Online transient stability assessment (TSA) is essential for secure and stable power system operations. The growing number of Phasor Measurement Units (PMUs) brings about massive s…
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
Geethu Joseph, Chen Zhong, M. Cenk Gursoy +2
We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at…
Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels
Chenxi Zhong, Huiyuan Yang, Xiaojun Yuan
With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent s…
Anomaly Detection via Controlled Sensing and Deep Active Inference
Geethu Joseph, Chen Zhong, M. Cenk Gursoy +2
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agen…
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
Feng Wang, Chen Zhong, M. Cenk Gursoy +1
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has…
Deep Actor-Critic Reinforcement Learning for Anomaly Detection
Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks. In this p…
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
Chen Zhong, Ziyang Lu, M. Cenk Gursoy +1
To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider…
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks
Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques. Motivated by this, we in this paper consider edge cachin…
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access
Chen Zhong, Ziyang Lu, M. Cenk Gursoy +1
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP). We first propose a model-free actor-critic d…
Learning-Based Delay-Aware Caching in Wireless D2D Caching Networks
Yi Li, Chen Zhong, M. Cenk Gursoy +1
Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobi…
A Deep Reinforcement Learning-Based Framework for Content Caching
Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (…