Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

09/13/2019
by   Shilian Zheng, et al.
1

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.

READ FULL TEXT
POST COMMENT

Comments

olafekry

can you send me data set for this paper,,,,please

Authors

page 1

page 2

page 3

page 4

08/01/2019

Robust Deep Sensing Through Transfer Learning in Cognitive Radio

We propose a robust spectrum sensing framework based on deep learning. T...
03/17/2020

Real-World Considerations for Deep Learning in Wireless Signal Identification Based on Spectral Correlation Function

This paper proposes a convolutional neural network (CNN) model which uti...
12/11/2019

SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network

Next generation networks are expected to operate in licensed, shared as ...
02/25/2019

Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method

When applying the foreground removal methods to uncover the faint cosmol...
03/18/2021

Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition

Automatic analysis of bioacoustic signals is a fundamental tool to evalu...
07/21/2019

Validation of Modulation Transfer Functions and Noise Power Spectra from Natural Scenes

The Modulation Transfer Function (MTF) and the Noise Power Spectrum (NPS...
09/06/2019

Deep Learning for Spectrum Sensing

In cognitive radio systems, the ability to accurately detect primary use...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.