Open AccessModulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning
Author(s) -
Isaac A. Oyedeji,
Olumide O. Ajayi,
Sunday T. Aladesae
Publication year - 2022
Publication title -
european journal of engineering and technology research
Language(s) - English
Resource type - Journals
ISSN - 2736-576X
DOI - 10.24018/ejeng.2022.7.2.2739
Subject(s) - demodulation , computer science , additive white gaussian noise , cognitive radio , modulation (music) , quadrature amplitude modulation , signal (programming language) , artificial intelligence , speech recognition , amplitude modulation , deep learning , pattern recognition (psychology) , algorithm , channel (broadcasting) , frequency modulation , telecommunications , bit error rate , radio frequency , physics , programming language , acoustics , wireless
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.