Spectrum Sensing Based On Deep Learning To Increase Spectrum Utilization

Tae-Yun Jung Et.Al

doi:10.17762/turcomat.v12i6.1971

Abstract

This paper proposes a new spectrum sensing technique for cognitive radio systems. To determine vacancy of the spectrum, the proposed method employs the recurrent neural network (RNN), one of the popular deep learning techniques. The proposed technique determines the spectrum occupancy of the primary user (PU) by observing the received signal’s energy and any information on the PU signal characteristic is not used. To this end, the received signal’s spectrum is obtained by fast Fourier transform (FFT). This process is performed on consecutive received signals and the resulting spectrums are stacked. Finally, a 2-dimensional spectrum (or spectrogram) is made. This 2-D spectrum is cut into sensing channel bandwidths and inputted to the deep learning model to decide the channel’s occupancy. While the recently published spectrum sensing technique based on convolutional neural network (CNN) relies on an empty channel, the proposed technique does not require any empty channel. Only the channel signal of interest to sense is needed. Since spectrum sensing results is two (busy or idle), binary classification deep learning model is developed. According to the computer simulation results, the proposed method has similar performance with the conventional CNN-based method while the spectral efficiency of the proposed method is much higher than that of the existing scheme. In addition, the overall learnable parameters of the proposed deep learning model is only 2/3 of the existing method

Highlights

The cognitive radio (CR) is an effective mean to alleviate the problem of wireless frequency resource scarcity and underused licensed spectrum [1]
This paper considers a new deep learning based spectrum sensing technique in the category of energy detection without empty channel assumption
Spectrum sensing based on deep learning to increase spectrum utilization spectral efficiency is higher than that of the existing scheme

Summary

Introduction

The cognitive radio (CR) is an effective mean to alleviate the problem of wireless frequency resource scarcity and underused licensed spectrum [1]. The objective of spectrum sensing is to determine spectrum occupancy by the PU accurately This technique is grouped into three categories: match filter [2, 3], energy detection [4], and cooperative method [5]. The technique in [11] is relatively simple to implement but shows good performance even at low signal to noise ratios (SNRs) This method requires one empty channel, and this requirement lowers the spectral efficiency of both PU and SU. The received signal is converted into a frequency spectrum vector through fast Fourier transform (FFT) Performing this process on consecutive received signals and stacking the resulting spectrums, 2-dimensional spectrum is made. This 2-D spectrum is cut by a sensing channel bandwidth and inputted to the CNN classifier to decide the channel’s occupancy. The overall learnable parameters of the proposed deep learning model is only 2/3 of the existing model

System model

Environment and signal preparation

Conclusions