activity
20182022
most citedDesigning Wireless Powered Networks assisted by Intelligent Reflecting Surfaces with Mechanical Tilt

12 citations · 21 across the 7 of their papers we have counts for

collaborators

16 papers

eess.SP20223 cited

Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric Learning

Cheng Wang, Xue Fu, Yu Wang +4

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio fr…

eess.SP2022

Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning

Zeyang Yang, Xue Fu, Guan Gui +4

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under the frien…

eess.SP20221 cited

Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training

Xue Fu, Yang Peng, Yuchao Liu +4

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual em…

eess.SP2022

A Novel Channel Identification Architecture for mmWave Systems Based on Eigen Features

Yibin Zhang, Jinlong Sun, Guan Gui +2

Millimeter wave (mmWave) communication technique has been developed rapidly because of many advantages of high speed, large bandwidth, and ultra-low delay. However, mmWave communic…

cs.IT202112 cited

Designing Wireless Powered Networks assisted by Intelligent Reflecting Surfaces with Mechanical Tilt

Zoran Hadzi-Velkov, Slavche Pejoski, Nikola Zlatanov +1

In this paper, we propose a fairness-aware rate maximization scheme for a wireless powered communications network (WPCN) assisted by an intelligent reflecting surface (IRS). The pr…

eess.SP2020

On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment

Suren Sritharan, Harshana Weligampola, Haris Gacanin

This paper studies practical limitations of learning methods for resource management in non-stationary radio environment. We propose two learning models carefully designed to suppo…