Deep Learning-Based FM Demodulation in Complex Electromagnetic Environment
S Zheng, Z Pei, T Chen, J Chen, W Lu… - IEEE Open Journal of …, 2024 - ieeexplore.ieee.org
In recent years, deep learning has been applied widely in the field of communication. In this
paper, a deep learning-based frequency modulation (FM) demodulation method is proposed …
paper, a deep learning-based frequency modulation (FM) demodulation method is proposed …
End to end deep neural network frequency demodulation of speech signals
D Elbaz, M Zibulevsky - … and Communication Networks: Proceedings of the …, 2019 - Springer
Frequency modulation (FM) is a form of radio broadcasting which is widely used nowadays
and has been for almost a century. We suggest a software-defined-radio (SDR) receiver for …
and has been for almost a century. We suggest a software-defined-radio (SDR) receiver for …
End to end deep neural network frequency demodulation of speech signals
D Elbaz, M Zibulevsky - arXiv preprint arXiv:1704.02046, 2017 - arxiv.org
Frequency modulation (FM) is a form of radio broadcasting which is widely used nowadays
and has been for almost a century. We suggest a software-defined-radio (SDR) receiver for …
and has been for almost a century. We suggest a software-defined-radio (SDR) receiver for …
Deep learning for signal demodulation in physical layer wireless communications: Prototype platform, open dataset, and analytics
In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and
establish the first open dataset of real modulated signals for wireless communication …
establish the first open dataset of real modulated signals for wireless communication …
Deep learning-based demodulation of radio signal
K Chia, VM Baskaran - 2022 International Symposium on …, 2022 - ieeexplore.ieee.org
M-ary quadrature amplitude modulation (M-QAM) modulated signal is commonly used in
digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by …
digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by …
Lightweight machine learning for efficient frequency-offset-aware demodulation
Carrier frequency offset (CFO) arises from the intrinsic mismatch between the oscillators of a
wireless transmitter and the corresponding receiver, as well as their relative motion (ie …
wireless transmitter and the corresponding receiver, as well as their relative motion (ie …
Demodulation of faded wireless signals using deep convolutional neural networks
AS Mohammad, N Reddy, F James… - 2018 IEEE 8th Annual …, 2018 - ieeexplore.ieee.org
This paper demonstrates exceptional performance of approximately 10.0 dB learning-based
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …
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 …
Detection of Impaired OFDM Waveforms Using Deep Learning Receiver
J Pihlajasalo, D Korpi, T Riihonen… - 2022 IEEE 23rd …, 2022 - ieeexplore.ieee.org
With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware
impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear …
impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear …
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 …