Signal Modulation Classification Based on Deep Belief Network

Modulation classification plays an important role in civil and military fields such as software defined radio, electronic countermeasure and intelligent demodulator. Due to the difficulty of feature extraction in traditional signal modulation classification algorithm, this paper proposes a signal modulation classification algorithm based on deep belief network. The proposed algorithm does not need to extract the signal features, and uses the I/Q data to classify signal directly. The simulation results show that the classification performance of the proposed algorithm is better than traditional machine learning algorithm, when the simulation condition is same.

[1]  Octavia A. Dobre,et al.  A Low Complexity Modulation Classification Algorithm for MIMO Systems , 2013, IEEE Communications Letters.

[2]  Yi Zhang,et al.  Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[3]  C. S. Weaver,et al.  THE AUTOMATIC CLASSIFICATION OF MODULATION TYPES BY PATTERN RECOGNITION. , 1969 .

[4]  Zhenyu Zhang,et al.  Modulation classification in multipath fading channels using sixth-order cumulants and stacked convolutional auto-encoders , 2017, IET Commun..

[5]  Fanggang Wang,et al.  Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test , 2010, IEEE Transactions on Communications.

[6]  Octavia A. Dobre,et al.  On the likelihood-based approach to modulation classification , 2009, IEEE Transactions on Wireless Communications.

[7]  Sofie Pollin,et al.  Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors , 2017, IEEE Transactions on Cognitive Communications and Networking.

[8]  Saleh R. Al-Araji,et al.  A unified practical approach to modulation classification in cognitive radio using likelihood-based techniques , 2015, 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE).

[9]  Ping Zhang,et al.  Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants , 2017, IEEE Transactions on Vehicular Technology.

[10]  Arjuna Madanayake,et al.  Deep Learning Based Radio-Signal Identification With Hardware Design , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Geoffrey E. Hinton,et al.  A New Learning Algorithm for Mean Field Boltzmann Machines , 2002, ICANN.

[13]  Yahia A. Eldemerdash,et al.  Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers , 2017, IEEE Photonics Technology Letters.

[14]  Shuyuan Yang,et al.  Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks , 2018, IEEE Communications Letters.

[15]  Mohd Zuki Yusoff,et al.  Efficient and Low Complexity Modulation Classification Algorithm for MIMO Systems , 2015 .

[16]  Xue Chen,et al.  Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning , 2017, IEEE Photonics Technology Letters.