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Fang Fang

Researcher at North China Electric Power University

Publications -  237
Citations -  7575

Fang Fang is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 33, co-authored 192 publications receiving 4551 citations. Previous affiliations of Fang Fang include Paris 12 Val de Marne University & Ulsan National Institute of Science and Technology.

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Implementation of machine-learning classification in remote sensing: an applied review

TL;DR: An overview of machine learning from an applied perspective focuses on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN).
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Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network

TL;DR: This paper proposes a low-complexity suboptimal algorithm, which includes energy-efficient subchannel assignment and power proportional factors determination for subchannel multiplexed users and proposes a novel power allocation across subchannels to further maximize energy efficiency.
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A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art.

TL;DR: In this article, the authors provide a comprehensive overview of mobile edge computing (MEC) and its potential use cases and applications, as well as discuss challenges and potential future directions for MEC research.
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Combined cooling, heating and power systems: A survey

TL;DR: The combined cooling, heating and power (CCHP) system has been increasingly attracting attention in academia and industries in recent years, thanks to its distinctive advantages of high system and economic efficiency and less greenhouse gas (GHG) emissions as discussed by the authors.
Posted Content

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

TL;DR: This survey provides a holistic overview of MEC technology and its potential use cases and applications, and outlines up-to-date researches on the integration of M EC with the new technologies that will be deployed in 5G and beyond.