Y
Yi Gong
Researcher at Southern University of Science and Technology
Publications - 129
Citations - 2541
Yi Gong is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Wireless & Wireless sensor network. The author has an hindex of 19, co-authored 119 publications receiving 1419 citations. Previous affiliations of Yi Gong include South University of Science and Technology of China & University of Science and Technology of China.
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Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks
TL;DR: The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.
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Collaborative Energy and Information Transfer in Green Wireless Sensor Networks for Smart Cities
TL;DR: This work proposes an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrates the challenging issues in this design.
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Multiband Cooperation for 5G HetNets: A Promising Network Paradigm
TL;DR: In this proposed network architecture, control information is transmitted through the control plane at sub-6 GHz, and the user equipment (UE) data are forwarded through the user plane in millimeter-wave (mm-wave) bands.
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An Energy-Ratio-Based Approach for Detecting Pilot Spoofing Attack in Multiple-Antenna Systems
TL;DR: This work proposes an efficient detector, named energy ratio detector (ERD), by exploring the asymmetry of received signal power levels at the transmitter and the legitimate receiver when there exists a pilot spoofing attack, and shows that the detecting threshold is derived without using the knowledge of the CSI of the legitimate channel as well as the illegitimate channel.
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Spectrum Analysis and Convolutional Neural Network for Automatic Modulation Recognition
TL;DR: A CNN-based modulation recognition framework for the detection of radio signals in communication systems and shows that the proposed CNN architecture with spectrogram images as signal representation achieves better recognition accuracy than existing deep learning-based methods.