Abstract
The increasing maturity of the concepts which would allow for the operation of a practical Cognitive Radio (CR) Network require functionalities derived through different methodologies from other fields. One such approach is Deep Learning (DL) which can be applied to diverse problems in CR to enhance its effectiveness by increasing the utilization of the unused radio spectrum. Using DL, the CR device can identify whether the signal comes from the Primary User (PU) transmitter or from an interferer. The method proposed in this paper is a hybrid DL architecture which aims at achieving high recognition rate at low signal-to-noise ratio (SNR) and various channel impairments including fading because such are the relevant conditions of operation of the CR. It consists of an autoencoder and a neural network structure due to the good denoising qualities of the former and the recognition accuracy of the latter. The autoencoder aims to restore the original signal from the corrupted samples which would increase the accuracy of the classifier. Afterwards its output is fed into the NN which learns the characteristics of each modulation type and classifies the restored signal correctly with certain probability. To determine the optimal classification DL model, several types of NN structures are examined and compared for input comprised of the IQ samples of the reconstructed signal. The performance of the proposed DL architecture in comparison to similar models for the relevant parameters in different channel impairments scenarios is also analyzed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The term “layer” in the context of CNNs throughout this paper, is understood in the sense described in [5], i.e. each “complex” convolutional layer is composed of sub-layers which can, because of the coherence of their functions, be denoted as a single unit.
- 2.
Consequently, the number of data vectors in both the training and test sets will be 8 times higher than it was originally.
References
Ali, A., Yangyu, F., Liu, S.: Automatic modulation classification of digital modulation signals with stacked autoencoders. Digit. Signal Process. 71, 108–116 (2017)
Arumugam, K.S.K., Kadampot, I.A., Tahmasbi, M., Shah, S., Bloch, M., Pokutta, S.: Modulation recognition using side information and hybrid learning. In: 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), pp. 1–2. IEEE (2017)
Bhatti, S.A., et al.: Impulsive noise modelling and prediction of its impact on the performance of WLAN receiver. In: 2009 17th European Signal Processing Conference, pp. 1680–1684. IEEE (2009)
Dobre, O.A., Abdi, A., Bar-Ness, Y., Su, W.: Blind modulation classification: a concept whose time has come. In: 2005 IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication, pp. 223–228. IEEE (2005)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Gouldieff, V., Palicot, J., Daumont, S.: Blind automatic modulation classification in multipath fading channels. In: 2017 22nd International Conference on Digital Signal Processing (DSP), pp. 1–5. IEEE (2017)
Gurugopinath, S.: Energy-based bayesian spectrum sensing over \(\alpha \)-\(\mu \)/stacy/generalized gamma fading channels. In: 2016 8th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–6. IEEE (2016)
Gurugopinath, S., Muralishankar, R., Shankar, H.: Spectrum sensing in the presence of cauchy noise through differential entropy. In: Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 201–204. IEEE (2016)
Hong, D., Zhang, Z., Xu, X.: Automatic modulation classification using recurrent neural networks. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 695–700. IEEE (2017)
Ivanov, A., Mihovska, A., Tonchev, K., Poulkov, V.: Real-time adaptive spectrum sensing for cyclostationary and energy detectors. IEEE Aerosp. Electron. Syst. Mag. 33(5–6), 20–33 (2018). https://doi.org/10.1109/MAES.2018.170098
Jang, W.M.: Blind cyclostationary spectrum sensing in cognitive radios. IEEE Commun. Lett. 18(3), 393–396 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lee, J.H., Kim, J., Kim, B., Yoon, D., Choi, J.W.: Robust automatic modulation classification technique for fading channels via deep neural network. Entropy 19(9) (2017). https://doi.org/10.3390/e19090454, http://www.mdpi.com/1099-4300/19/9/454
Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.: Independently recurrent neural network (IndRNN): building a longer and deeper RNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5457–5466 (2018)
Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam. arXiv preprint arXiv:1711.05101 (2017)
Mendis, G.J., Wei, J., Madanayake, A.: Deep learning-based automated modulation classification for cognitive radio. In: 2016 IEEE International Conference on Communication Systems (ICCS), pp. 1–6. IEEE (2016)
Orlic, V.D., Dukic, M.L.: Automatic modulation classification: sixth-order cumulant features as a solution for real-world challenges. In: 2012 20th Telecommunications Forum (TELFOR), pp. 392–399. IEEE (2012)
O’Shea, T.J., Corgan, J., Clancy, T.C.: Convolutional radio modulation recognition networks. In: Jayne, C., Iliadis, L. (eds.) EANN 2016. CCIS, vol. 629, pp. 213–226. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44188-7_16
O’Shea, T.J., Roy, T., Clancy, T.C.: Over-the-air deep learning based radio signal classification. IEEE J. Sel. Top. Signal Process. 12(1), 168–179 (2018)
Peng, S., Jiang, H., Wang, H., Alwageed, H., Yao, Y.D.: Modulation classification using convolutional neural network based deep learning model. In: 2017 26th Wireless and Optical Communication Conference (WOCC), pp. 1–5. IEEE (2017)
Qing Yang, G.: Modulation classification based on extensible neural networks. Math. Probl. Eng. 2017 (2017)
Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., Pollin, S.: Distributed deep learning models for wireless signal classification with low-cost spectrum sensors. arXiv preprint arXiv:1707.08908 (2017)
Smith, S.L., Kindermans, P.J., Le, Q.V.: Don’t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017)
Spaulding, A., Middleton, D.: Optimum reception in an impulsive interference environment - Part I: coherent detection. IEEE Trans. Commun. 25(9), 910–923 (1977). https://doi.org/10.1109/TCOM.1977.1093943
Stevenson, C.R., Chouinard, G., Lei, Z., Hu, W., Shellhammer, S.J., Caldwell, W.: IEEE 802.22: the first cognitive radio wireless regional area network standard. IEEE Commun. Mag. 47(1), 130–138 (2009)
The GNU Radio Foundation: GNU Radio, the free and open software radio ecosystem, October 2018. https://www.gnuradio.org/
Tsakalides, P., Nikias, C.L.: Maximum likelihood localization of sources in noise modeled as a stable process. IEEE Trans. Signal Process. 43(11), 2700–2713 (1995)
Xiong, X., Feng, J., Jiang, L.: Automatic digital modulation classification for ors satellite relay communication. In: 2015 International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5. IEEE (2015)
Xu, J.L., Su, W., Zhou, M.: Likelihood-ratio approaches to automatic modulation classification. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(4), 455–469 (2011)
Xu, Y., Li, D., Wang, Z., Guo, Q., Xiang, W.: A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals. Wireless Networks. Springer, New York (2017). https://doi.org/10.1007/s11276-018-1667-6
Zhang, D., et al.: Automatic modulation classification based on deep learning for unmanned aerial vehicles. Sensors 18(3), 924 (2018)
Zhang, Z., Hua, Z., Liu, Y.: Modulation classification in multipath fading channels using sixth-order cumulants and stacked convolutional auto-encoders. Wirel. Netw. 11(6), 910–915 (2017)
Zhu, X., Fujii, T.: A novel modulation classification method in cognitive radios using higher-order cumulants and denoising stacked sparse autoencoder. In: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–5, December 2016. https://doi.org/10.1109/APSIPA.2016.7820860
Acknowledgment
The paper is published with the support of the project No BG05M2OP001-2.009-0033 “Promotion of Contemporary Research Through Creation of Scientific and Innovative Environment to Encourage Young Researchers in Technical University - Sofia and The National Railway Infrastructure Company in The Field of Engineering Science and Technology Development” within the Intelligent Growth Science and Education Operational Programme co-funded by the European Structural and Investment Funds of the European Union.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ivanov, A., Tonchev, K., Poulkov, V., Al-Shatri, H., Klein, A. (2019). Hybrid Noise-Resilient Deep Learning Architecture for Modulation Classification in Cognitive Radio Networks. In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-23976-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-23976-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23975-6
Online ISBN: 978-3-030-23976-3
eBook Packages: Computer ScienceComputer Science (R0)