Commenced in January 2007
Paper Count: 32586
A Communication Signal Recognition Algorithm Based on Holder Coefficient Characteristics
Abstract:Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298922Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 586
 C. Niu, Y. Li,R. Q. Hu and F. Ye, Fast and efficient radio resource allocation in dynamic ultra-dense heterogeneous networks. IEEE Access, vol. 5, no. 99, pp.19111924, 2017.
 O. A Dobre, A Abdi, Y. Bar-Ness and W. Su, Survey of automatic modulation classification techniques: classical approaches and new trends. CommunicationsIet, vol. 1, no. 2, pp. 137156, 2007.
 J. L. Xu, W. Su and M. Zhou, Likelihood-ratio approaches to automatic modulation classification. IEEE Transactions on Systems Man and Cybernetics Part C, vol. 41, no. 4, pp. 455469, 2011.
 E. E. Azzouz and A. K. Nandi, Procedure for automatic recognition of analogue and digital modulations. IEE Proc.-Commun., vol. 143, no. 5, pp. 259266, 1996.
 L. I.Wen-Sheng and L. I. Yi-Bing, A new algorithm for spectrum detection in cognitive radio system. Applied Science & Technology, 2011.
 M. Zaerin and B. Seyfe, Multiuser modulation classification based on cumulants in additive white gaussian noise channel. Iet Signal Processing, vol. 6, no. 9, pp.815823, 2012.
 C. C. Ho, T. T. Tsai and T. H. Kuo, Ieee 1451-based intelligent computer numerical control tool holder. inInternational Symposium on Computer, Consumer and Control, 2012, pp. 767770.
 K. E. Hil, D. Erdogmus, K. Torkkola and J. C. Principe, Feature extraction using information-theoretic learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 138592, 2006.
 T. ZhiLing, Y. XiaoNiu and L. JianDong, Study on fractal features of modulated radio signals. Chinese Journal of Physics, vol. 60, no. 5, pp. 550556, 2011.
 Y. Zhang, J. Zhu and L. Wang, Temperature prediction model of rotary kiln firing zone based on improved bp neural network,. in Second International Conference on Intelligent System Design and Engineering Application, 2012, pp. 549552.
 L. DeYi, L. ChangYu, D. Yi and H. Xu,Artificial intelligence with uncertainty. Journal of Software, vol. 15, pp. 15831594, 2004.
 G. B. Huang, Q. Y. Zhu and C. K. Siew, Extreme learning machine: Theory and applications. Neurocom?puting, vol. 70, no. 1, pp. 489501, 2006.
 C. W. Deng, G. B. Huang, X. U. Jia and J. X. Tang, Extreme learning machines: new trends and applications. Science China(Information Sciences), vol. 58, no. 2, pp. 20 301020 301, 2015.
 J. Li, A new robust signal recognition approach based on holder cloud features under varying snr environment. Ksii Transactions on Internet and Information Systems, vol. 9, no. 12, pp. 49344949, 2015.
 X. Shi, Facial expression recognition based on data field and cloud model. Computer Sciences and appli?cation, vol. 04, no. 12, pp. 385392, 2014.