Adaptive modem and interference suppression based on deep learning
P Wei, S Wang, J Luo - Transactions on Emerging …, 2021 - Wiley Online Library
With the increasingly fierce competition of electromagnetic spectrum, developing intelligent
communication systems that can reconfigure its waveform can effectively improve the …
communication systems that can reconfigure its waveform can effectively improve the …
Multi-modem implementation method based on deep autoencoder network
P Wei, R Lu, S Wang, S Xie - Wireless and Satellite Systems: 11th EAI …, 2021 - Springer
With the fierce competition for electromagnetic spectrum, the development of intelligent
satellite communication systems with intelligent waveform generation and reconstruction …
satellite communication systems with intelligent waveform generation and reconstruction …
Combining deep learning and linear processing for modulation classification and symbol decoding
Deep learning has been recently applied to many problems in wireless communications
including modulation classification and symbol decoding. Many of the existing end-to-end …
including modulation classification and symbol decoding. Many of the existing end-to-end …
Deepdemod: Bpsk demodulation using deep learning over software-defined radio
In wireless communication, signal demodulation under non-ideal conditions is one of the
important research topic. In this paper, a novel non-coherent binary phase shift keying …
important research topic. In this paper, a novel non-coherent binary phase shift keying …
A convolutional and transformer based deep neural network for automatic modulation classification
Automatic modulation classification (AMC) aims at identifying the modulation of the received
signals, which is a significant approach to identifying the target in military and civil …
signals, which is a significant approach to identifying the target in military and civil …
Learning to optimize: Training deep neural networks for interference management
Numerical optimization has played a central role in addressing key signal processing (SP)
problems. Highly effective methods have been developed for a large variety of SP …
problems. Highly effective methods have been developed for a large variety of SP …
Unified Deep Neural Demodulation Network Design for QAM Signal Recovery
B Xiao, S Zheng, J Zhu, Z Zhang… - 2023 IEEE 98th …, 2023 - ieeexplore.ieee.org
In this paper, we focus on designing an unified deep neural demodulation network for
recovering multiple QAM signals, which can adapt to the adaptive QAM modulation signal …
recovering multiple QAM signals, which can adapt to the adaptive QAM modulation signal …
Evaluation of Neural Demappers for Trainable Constellation in an End-to-End Communication System
Conventional M-ary Quadrature Amplitude Modulation (M-QAM) constellation designs such
as rectangular constellation, are based on mathematical data and estimated channel …
as rectangular constellation, are based on mathematical data and estimated channel …
A denoising radio classifier with residual learning for modulation recognition
H Zhu, L Zhou, C Chen - 2021 IEEE 21st International …, 2021 - ieeexplore.ieee.org
Deep learning has great potential in modulation recognition. However, when noise causes
signals distortion, the performance of deep learning algorithms degrades dramatically, and …
signals distortion, the performance of deep learning algorithms degrades dramatically, and …
Automatic adaptive wireless demodulator using incremental learning in real time
In wireless communication systems, a received signal is corrupted by various means, such
as noise, multi-path fading, and defects in hardware. To properly demodulate the signal and …
as noise, multi-path fading, and defects in hardware. To properly demodulate the signal and …