Wireless Standard Classification Using Convolutional Neural Networks

The growing prominence of spectrum sharing technologies has spurred interest in spectrum monitoring technologies with the ability to identify unknown wireless signals. This paper presents a convolutional neural network (CNN) deep learning model to classify 4G LTE downlink, 4G LTE uplink, 5G NR downlink, 5G NR uplink, IEEE 802.11ax (WiFi 6), and Bluetooth Low Energy (BLE) 5.0 signals. The classifier operates on In-phase and Quadrature (I/Q) samples and does not require synchronization with the unknown signals. To improve the generalizability of the classifier, comprehensive signal datasets are generated to include a wide range of signal configurations found in the standards. These signals are impaired with additive white Gaussian noise (AWGN), Rayleigh or Ricean multipath fading channels, frequency offsets, and I/Q imbalances to make the signals more realistic. The exploration of time domain, frequency domain, and time-frequency domain features reveals high frequency resolution time-frequency domain features perform best. The proposed CNN model achieves a high classification accuracy in the presence of all of the aforementioned impairments, achieving over 94% accuracy for signal to noise ratios (SNR) greater than 0 dB.

[1]  R. Michael Buehrer,et al.  Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments , 2020, IEEE Transactions on Cognitive Communications and Networking.

[2]  Hatim Alhazmi,et al.  5G Signal Identification Using Deep Learning , 2020, 2020 29th Wireless and Optical Communications Conference (WOCC).

[3]  Dong-Seong Kim,et al.  CNN-Based Automatic Modulation Classification for Beyond 5G Communications , 2020, IEEE Communications Letters.

[4]  Armando Montalvo,et al.  Cellular Signal Identification Using Convolutional Neural Networks: AWGN and Rayleigh Fading Channels , 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[5]  Yu-Dong Yao,et al.  Cellular System Identification Using Deep Learning: GSM, UMTS and LTE , 2019, 2019 28th Wireless and Optical Communications Conference (WOCC).

[6]  Xiaofan Li,et al.  A Survey on Deep Learning Techniques in Wireless Signal Recognition , 2019, Wirel. Commun. Mob. Comput..

[7]  Sarat Kumar Patra,et al.  Blind Identification of Radio Access Techniques Based on Time-Frequency Analysis and Convolutional Neural Network , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[8]  T. Charles Clancy,et al.  Over-the-Air Deep Learning Based Radio Signal Classification , 2017, IEEE Journal of Selected Topics in Signal Processing.

[9]  Ingrid Moerman,et al.  End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications , 2017, IEEE Access.

[10]  Aly El Gamal,et al.  Deep neural network architectures for modulation classification , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[11]  Halina Kwasnicka,et al.  Fuzzy logic based signal classification with cognitive radios for standard wireless technologies , 2010, 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[12]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[13]  Heba M. Abdel-Atty,et al.  Sub-Nyquist Cyclostationary Detection of GFDM for Wideband Spectrum Sensing , 2019, IEEE Access.