activity
20192022
most citedComparison of Neural Network Architectures for Spectrum Sensing

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

eess.SP2022

Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm

Wei Huang, Tianfu Qi, Yundi Guan +2

Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis, which is usually laborious and time-cons…

eess.SP2020

Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach

Lei Li, Qihang Peng, Jun Wang

Modulation recognition using deep neural networks has shown promising advantages over conventional algorithms. However, most existing research focuses on single receive antenna. In…

eess.SP2019

A Neural Network Detector for Spectrum Sensing under Uncertainties

Ziyu Ye, Qihang Peng, Kelly Levick +4

Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretic…

cs.IT2019

Robust Deep Sensing Through Transfer Learning in Cognitive Radio

Qihang Peng, Andrew Gilman, Nuno Vasconcelos +2

We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user's receiver are filtered, sampled and then directly fed into a conv…

eess.SP20191 cited

Comparison of Neural Network Architectures for Spectrum Sensing

Ziyu Ye, Andrew Gilman, Qihang Peng +3

Different neural network (NN) architectures have different advantages. Convolutional neural networks (CNNs) achieved enormous success in computer vision, while recurrent neural net…