Abstract
The 5G and beyond wireless networks will be more dynamic and heterogeneous, which needs to work on multistrand waveforms. One of the most significant challenges in such a dynamic network, especially non cooperated cases, is the identification of particular modulation type, which the transmitter uses at the given time to decode the data successfully. This research proposes a modulation classification algorithm using the combination architectures of modified convolutional neural network. The proposed deep learning architecture is developed by combining the convolutional neural network, dense network, and long short-term memory network (LSTM), which is named as convolutional LSTM dense neural network (CLDNN). Moreover, the mean cumulative sum metric (MCS) is introduced in the pooling layer for improved classification accuracy. Dimensionality reduction through Principal Component Analysis is also applied to minimize the training time, so that the proposed architecture can be adopted for its practical usage. The simulation results prove that the presented CLDNN outperforms an ordinary CNN, while taking less training time.
Similar content being viewed by others
References
Abu-Romoh M, Aboutaleb A, Rezki Z (2018) Automatic modulation classification using moments and likelihood maximization. IEEE Commun Lett 22(5):938–941
Blanquez-Casado F, Torres MDCA, Gomez G (2019) Link adaptation mechanisms based on logistic regression modeling. IEEE Commun Lett 23(5):942–945
Chen W, Xie Z, Ma L, Liu J, Liang X (2019) A faster maximum-likelihood modulation classification in flat fading non-gaussian channels. IEEE Commun Lett 23(3):454–457
Daldal N, Polat K, Guo Y (2019) Classification of multi-carrier digital modulation signals using ncm clustering based feature-weighting method. Comput Ind 109:45–58
Güner A., Alçin ÖF, Şengür A (2019) Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features. Measurement 145:214–225
Hatzichristos G, Fargues MP (2001) A hierarchical approach to the classification of digital modulation types in multipath environments. In: Conference record of thirty-fifth asilomar conference on signals, systems and computers (Cat. No. 01CH37256), vol 2. IEEE, pp 1494–1498
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Hong D, Zhang Z, Xu X (2017) Automatic modulation classification using recurrent neural networks. In: 2017 3rd IEEE international conference on computer and communications (ICCC). IEEE, pp 695–700
Hong L, Ho K (1999) Identification of digital modulation types using the wavelet transform. In: MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No. 99CH36341), vol 1. IEEE, pp 427–431
Huq KMS, Busari SA, Rodriguez J, Frascolla V, Bazzi W, Sicker DC (2019) Terahertz-enabled wireless system for beyond-5g ultra-fast networks: a brief survey. IEEE Netw 33(4):89–95
Huynh-The T, Hua CH, Kim DS (2019) Learning action images using deep convolutional neural networks for 3d action recognition. In: 2019 IEEE sensors applications symposium (SAS). IEEE, pp 1–6
Kamel M, Hamouda W, Youssef A (2016) Ultra-dense networks: a survey. IEEE Commun Surv Tutorials 18(4):2522–2545
Li JM, Hu YH, Tao XH (2005) Recognition method based on principal component analysis and back-propagation neural network. Infrared and Laser Engineering 34(6):719
Li W, Dou Z, Qi L, Shi C (2019) Wavelet transform based modulation classification for 5g and uav communication in multipath fading channel. Phys Commun 34:272–282
Mingquan L, Xianci X, Lemin L (1998) Ar modeling-based features extraction of multiple signals for modulation recognition. In: ICSP’98. 1998 Fourth international conference on signal processing (Cat. No. 98TH8344), vol 2. IEEE, pp 1385–1388
Mobasseri BG (2000) Digital modulation classification using constellation shape. Signal Processing 80(2):251–277
Nie J, Zhang Y, He Z, Chen S, Gong S, Zhang W (2019) Deep hierarchical network for automatic modulation classification. IEEE Access 7:94,604–94,613
Nie J, Zhang Y, He Z, Chen S, Gong S, Zhang W (2019) Deep hierarchical network for automatic modulation classification. IEEE Access 7:94,604–94,613
Nolan KE, Doyle L, O’Mahony D, Mackenzie P (2001) Modulation scheme recognition techniques for software radio on a general purpose processor platform. In: Proceedings of the first joint IEI/IEE symposium on telecommunication systems, Dublin
O’Shea TJ, Corgan J, Clancy TC (2016) Convolutional radio modulation recognition networks. In: International conference on engineering applications of neural networks. Springer, pp 213–226
Parvez I, Rahmati A, Guvenc I, Sarwat AI, Dai H (2018) A survey on low latency towards 5g: ran, core network and caching solutions. IEEE Commun Surv Tutorials 20(4):3098–3130
Polydoros A, Kim K (1990) On the detection and classification of quadrature digital modulations in broad-band noise. IEEE Trans Commun 38(8):1199–1211
Sainath TN, Vinyals O, Senior A, Sak H (2015) Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4580–4584
Shah MH, Dang X (2020) Low-complexity deep learning and rbfn architectures for modulation classification of space-time block-code (stbc)-mimo systems. Digital Signal Processing 99(102):656
Sills J (1999) Maximum-likelihood modulation classification for psk/qam. In: MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No. 99CH36341), vol 1. IEEE, pp 217–220
Sills J (1999) Maximum-likelihood modulation classification for psk/qam. In: MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No. 99CH36341), vol 1. IEEE, pp 217–220
Swami A, Sadler BM (2000) Hierarchical digital modulation classification using cumulants. IEEE Trans Commun 48(3):416–429
Wang Y, Wang J, Zhang W, Yang J, Gui G (2020) Deep learning-based cooperative automatic modulation classification method for mimo systems. IEEE Trans Veh Technol 69(4):4575– 4579
Xue R, Hu D, Zhu T (2017) Application of adaptive coded modulation technology in uav data link. Int J Commun Netw Sys Sci 10(5):181–190
Zhang D, Ding W, Liu C, Wang H, Zhang B (2020) Modulated autocorrelation convolution networks for automatic modulation classification based on small sample set. IEEE Access 8:27,097–27,105
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection: Special Issue on P2P Computing for Beyond 5G Network and Internet-of-Everything
Guest Editors: Prakasam P, Ajayan John, Shohel Sayeed
Rights and permissions
About this article
Cite this article
Clement, J.C., Indira, N., Vijayakumar, P. et al. Deep learning based modulation classification for 5G and beyond wireless systems. Peer-to-Peer Netw. Appl. 14, 319–332 (2021). https://doi.org/10.1007/s12083-020-01003-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-020-01003-3