Modulation Signal Denoising Based on Auto-encoder

This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE International Symposium on Broadband Multimedia Systems and Broadcasting S. 1 - 5
Hauptverfasser: Mo, Zunyin, Li, Hongli, Wang, Jiao, Huang, Hao, Li, Jianqing
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 04.08.2021
Schlagworte:
Advanced signal processing for transmission
Complexity theory
convolution neural network
denoising autoencoder
Feature extraction
Machine learning for communications
Modulation
Neural networks
Noise reduction
Signal processing algorithms
Stability analysis
ISSN:2155-5052
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of the modulated signal with noise as the input of the auto-encoder and the features of the clean signal as the label. At the same time, back-propagation algorithm and gradient descent method are used to optimize and update the parameters in the auto-encoder model to minimize the reconstruction error, so as to realize the denoising function of the modulated signal. For a variety of modulation types, this method can improve the modulation signal about 3-9 dB in different SNR environment. The denoising model can generate high-level features of different modulation signals without any artificial feature extraction and prior knowledge and has strong feature representation ability. It has the advantages of strong versatility, low complexity, good denoising effect and good stability.
ISSN:2155-5052
DOI:10.1109/BMSB53066.2021.9547158