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Nourollahi H, Hosseini S A, Shahzadi A, Shaghaghi Kandovan R. Signal detection Using Rational Function Curve Fitting. JSDP 2023; 19 (4) : 1
URL: http://jsdp.rcisp.ac.ir/article-1-1182-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1182-en.html
Department of Communication Engineering, Faculty of Electrical and Computer Engineering, Yadegar -e- Imam Khomeini(RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
Abstract: (1202 Views)
In this manuscript, we proposed a new scheme in communication signal detection which is respect to the curve shape of received signal and based on the extraction of curve fitting (CF) features. This feature extraction technique is proposed for signal data classification in receiver. The proposed scheme is based on curve fitting and approximation of rational fraction coefficients. For each symbol of received signal, a specific rational function approximation is developed to fit with received signal curve and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. Then it will be shown that the coefficients of this polynomials have the potential for using as new features in a statistical classifier and have better performance in competition with other solutions such as linear and even nonlinear feature extraction methods in classification. The criteria used in performance evaluation are probability of error and signal to noise ratio in FSK and ASK modulations. For each symbol of received signal, a specific rational function approximation is developed to fit with received signal curve and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. In the proposed method, there are two phases train and test, which are described in the following two steps. First, in the train phase, the algorithm starts by using binary FSK and ASK modulations, so first, a system generate a number of random symbols then signal is modulated by binary ASK and FSK. The Modulated FSK and ASK signals are corrupted in the channel with noise. The noise-corrupted signal enters the receiver at the corresponding transmitted interval. Then, the samples are extracted from the modulated signals based on predetermined sample rates. Then, we fit a rational fraction curve with degrees of L and M to each set of N samples. Afterward, we apply all the numerator (L+1) and denominator (M) coefficients to 0 and 1 classes in the binary FSK and ASK modulations. We store all the specific coefficients of the deterministic symbols at different M and L values to create the corresponding histogram in each class. In each histogram (i.e., the coefficients of a class), we extract and store specific coefficients that completely discriminate between the two classes. Therefore, we determine all the histograms where there is a good approximation of discrimination and create the related table. Note that the data used in histograms are the training data. Then, in order to analyze and evaluate the performance of the proposed curve fitting method, we send the testing data through the channel corresponding to the related modulator. The results of the proposed classification method show that it provides smaller error rate regarding to the theoretical error rate probability in AWGN channel.
Article number: 1
Type of Study: Research |
Subject:
Paper
Received: 2020/10/5 | Accepted: 2021/12/6 | Published: 2023/03/20 | ePublished: 2023/03/20
Received: 2020/10/5 | Accepted: 2021/12/6 | Published: 2023/03/20 | ePublished: 2023/03/20
References
1. [1] T. S. Rappaport, Wireless communications: Principles and practice,2nd ed. Prentice Hall, 2002.
2. [2] R. M. Gagliardi and S. Karp, Optical communications, 2nd ed. Wiley,1995.
3. [3] H. Meyr, M. Moeneclaey, and S. A. Fechtel, Digital communication receivers: Synchronization, channel estimation, and signal processing. John Wiley & Sons, Inc., 1998.
4. [4] T. Schenk, RF imperfections in high-rate wireless systems: Impact and digital compensation. Springer Science & Business Media, 2008. [DOI:10.1007/978-1-4020-6903-1]
5. [5] J. Proakis and M. Salehi, Digital Communications, 5th ed. McGraw-Hill Education, 2007.
6. [6] A. Goldsmith, Joint source/channel coding for wireless channels, in Proc. IEEE Vehicular Technol. Conf., vol. 2, 1995, pp. 614-618.
7. [7] E. Zehavi, 8-PSK trellis codes for a Rayleigh channel, IEEE Trans.Commun., vol. 40, no. 5, pp. 873-884, 1992. [DOI:10.1109/26.141453]
8. [8] H. Wymeersch, Iterative receiver design. Cambridge University Press,2007, vol. 234. [DOI:10.1017/CBO9780511619199]
9. [9] K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural networks, vol. 2, no. 5, 1989,pp. 359-366. [DOI:10.1016/0893-6080(89)90020-8]
10. [10] S. Reed and N. de Freitas, Neural programmer-interpreters, arXiv preprint, 2015, arXiv: 1511.06279.
11. [11] H. T. Siegelmann and E. D. Sontag, On the computational power of neural nets, in Proc. 5th Annu. Workshop Computational Learning Theory. ACM, 1992, pp. 440-449. [DOI:10.1145/130385.130432]
12. [12] V. Vanhoucke, A. Senior, and M. Z. Mao, Improving the speed of neural networks on CPUs, in Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop, 2011.
13. [13] Y.-H. Chen, T. Krishna, J. S. Emer, and V. Sze, Eyeriss: An energyefficient reconfigurable accelerator for deep convolutional neural networks, IEEE J. Solid-State Circuits, vol. 52, no. 1, 2017 pp. 127-138,. [DOI:10.1109/JSSC.2016.2616357]
14. [14] R. Raina, A. Madhavan, and A. Y. Ng, Large-scale deep unsupervised learning using graphics processors, in Proc. Int. Conf. Mach. Learn.(ICML). ACM, 2009, pp. 873-880. [DOI:10.1145/1553374.1553486]
15. [15] A. Atieg, G.A. Watson, " A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances", Journal of Computational and Applied Mathematics 158 2003 277-296, doi:10.1016/S0377-0427(03)00448-5 [DOI:10.1016/S0377-0427(03)00448-5]
16. [16] Mostafa Ghazizadeh Ahsaee, Hadi Sadoghi Yazdi, Mahmoud Naghibzadeh, " Curve fitting space for classification", Neural Comput & Applic 2011 20:273-285 DOI 10.1007 / s00521-010-0383-7 [DOI:10.1007/s00521-010-0383-7]
17. [17] Maryam Haddadi, Maliheh Ahmadi, Mohammad Reza Keyvanpour, and Noushin Riahi" Using Curve Fitting in Error Correcting Output Codes" Journal of Soft Computing and Information Technology (JSCIT), 2016, Vol. 5, No. 1
18. [18] Seyed Abolfazl Hosseini, Hassan Ghassemian, " Rational function approximation for feature reduction in hyperspectral data " Taylor & Francis, Remote Sensing Letters, 2016 ,Volume 7, Issue 2, Pages 101-110. [DOI:10.1080/2150704X.2015.1101180]
19. [19] Mersedeh Beitollahi, S Abolfazl Hosseini, " Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression ", IEEE, Iranian Conference on Electrical Engineering (ICEE), Pages 1967-1972 , 2017 [DOI:10.1109/IranianCEE.2017.7985378]
20. [20] Maryam Hamidi, Hassan Ghassemian∗, Maryam Imani " Classification of heart sound signal using curve fitting and fractal dimension" Elsevier, Biomedical Signal Processing and Control 39, 2018 ,351-359 [DOI:10.1016/j.bspc.2017.08.002]
21. [21] Yazan A. Alqudah, " Path Loss Modeling Based on Field Measurements Using Deployed 3.5GHzWiMAX Network " Springer Science+Business Media, LC, Wireless Pers Commun , 2012,DOI 10.1007/s11277-012-0612-8 Path. [DOI:10.1007/s11277-012-0612-8]
22. [22] HARRY L. VAN TREES, KRISTINE L. BELL, with ZHI TIAN, " Detection, Estimation, and Modulation Theory Part I: Detection, Estimation, and Filtering Theory Second Edition ", John Wiley & Sons, Inc. 2013
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