Abstract
Cognitive Radio (CR) system has been adopted for efficient utilization of radio frequency spectrum. The classification of signal modulation schemes is one of the main characteristics of the CR for appropriate demodulation of sensed signals. However, conventional Modulation Classification (MC) techniques require extensive extraction of signal features, which is not often guaranteed. Thus, Deep Learning (DL) has been seen as a promising solution to this drawback in MC. This paper proposes a DL-based MC technique using the Long Short-Term Memory (LSTM) network architecture. The proposed LSTM model was trained on M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude Modulation (MQAM) signal types. The LSTM directly learns the features of a given modulation scheme of a signal sample during training. The signal samples were generated via computer simulation for Signal-to-Noise Ratios (SNRs) from -10 dB to 20 dB with an interval of 5 dB over flat fading channel and Additive white Gaussian noise (AWGN). Simulation results show that the proposed LSTM model achieves an average classification accuracy of 95% at SNRs above 0 dB.
Highlights
The continued global increase in the number of connecting devices for Device-to-Device (D2D) and Machine-toMachine (M2M) wireless communications such as the Internet of Things (IoT) [1], [2] has imposed a heavy demand for radio frequency spectrum
The results clearly indicate that the Long Short-Term Memory (LSTM) model was able to recognize 8PSK better than both the Binary PSK (BPSK) and Quadrature PSK (QPSK)
The proposed LSTM model achieves an average of 94.71% classification accuracy with Signal-to-Noise Ratios (SNRs) of 5 dB and above, whereas an average of 65.49% classification accuracy is obtained by the model in the low SNR region (i.e., 0 dB and below)
Summary
The continued global increase in the number of connecting devices for Device-to-Device (D2D) and Machine-toMachine (M2M) wireless communications such as the Internet of Things (IoT) [1], [2] has imposed a heavy demand for radio frequency spectrum. The traditional spectrum access policy in which fixed frequency bands are assigned exclusively to licensed Primary Users (PUs) is no longer sustainable. This is because most of the frequency spectrum allocated to PUs are often underutilized; thereby creating the phenomenon called white spaces (or spectrum holes) [3]. In the ML techniques, signal features’ extraction stage must precede the training of the classifier such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) [11] - [14]. The LSTM classifier which is a DLbased technique is employed to address the problem of signal feature extraction during the MC process
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: European Journal of Engineering and Technology Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
