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Jie Tang

Researcher at South China University of Technology

Publications -  138
Citations -  4974

Jie Tang is an academic researcher from South China University of Technology. The author has contributed to research in topics: MIMO & Optimization problem. The author has an hindex of 30, co-authored 138 publications receiving 3153 citations. Previous affiliations of Jie Tang include University of Manchester & University of Electronic Science and Technology of China.

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Exact SINR Statistics in the Presence of Heterogeneous Interferers

TL;DR: It is shown that with cellular network densification there exists a tradeoff between the average SINR and rate performance, and the effect of total PPP-based interference power on useful transmission is mathematically equivalent to the severe fluctuations from a one-sided Gaussian fading channel.
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Massive MIMO-Enabled Full-Duplex Cellular Networks

TL;DR: In this paper, the authors provided a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed.
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Massive MIMO-Enabled Full-Duplex Cellular Networks

TL;DR: The results indicate that the UL rate bottleneck in the FD baseline single-input single-output system can be overcome via exploiting massive MIMO, and the anticipated twofold increase in SE is shown to be only achievable when the number of antennas tends to be infinitely large.
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Beamforming and Jamming Optimization for IRS-Aided Secure NOMA Networks

TL;DR: Numerical results show that the secure transmission in the proposed IRS-NOMA scheme can be effectively guaranteed with the assistance of artificial jamming.
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Joint 3D Trajectory and Power Optimization for UAV-Aided mmWave MIMO-NOMA Networks

TL;DR: In this article, a UAV-aided mmWave NOMA system is considered, where a single UAV serves as a flying base station (BS) to provide wireless access services to a set of IoT devices in different clusters.