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Tony Q. S. Quek
Researcher at Singapore University of Technology and Design
Publications - 784
Citations - 23103
Tony Q. S. Quek is an academic researcher from Singapore University of Technology and Design. The author has contributed to research in topics: Computer science & Wireless network. The author has an hindex of 65, co-authored 663 publications receiving 16996 citations. Previous affiliations of Tony Q. S. Quek include National University of Singapore & University of North Carolina at Charlotte.
Papers
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Enhanced intercell interference coordination challenges in heterogeneous networks
TL;DR: In this article, the authors present the concept of heterogeneous networks and also describe the major technical challenges associated with such network architecture, focusing in particular on the standardization activities within the 3GPP related to enhanced intercell interference coordination.
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Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling
TL;DR: This paper proposes an optimization framework of offloading from a single mobile device (MD) to multiple edge devices and proposes a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search- based approach and an SDR-based approaches for the elasticCPU frequency case.
Journal ArticleDOI
Enhanced Inter-Cell Interference Coordination Challenges in Heterogeneous Networks
TL;DR: In this paper, the authors present the concept of heterogeneous networks and also describe the major technical challenges associated with such network architecture, focusing in particular on the standardization activities within the 3GPP related to enhanced inter-cell interference coordination.
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Energy Efficient Heterogeneous Cellular Networks
TL;DR: A stochastic geometry based model is used to derive the success probability and energy efficiency in homogeneous macrocell and heterogeneous K-tier wireless networks under different sleeping policies and provides an essential understanding on the deployment of future green heterogeneous networks.
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Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Kang Wei,Jun Li,Ming Ding,Chuan Ma,Howard H. Yang,Farhad Farokhi,Shi Jin,Tony Q. S. Quek,H. Vincent Poor +8 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel framework based on the concept of differential privacy, in which artificial noise is added to parameters at the clients' side before aggregating, namely, noising before model aggregation FL (NbAFL).