پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 671 |
| تعداد مقالات | 9,762 |
| تعداد مشاهده مقاله | 69,478,743 |
| تعداد دریافت فایل اصل مقاله | 48,619,334 |
Quantum inspired genetic algorithm model based thirteen types automatic modulation classification | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 13، شماره 1، خرداد 2022، صفحه 3873-3889 اصل مقاله (615.55 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6186 | ||
| نویسندگان | ||
| Hussain A. Rasool* ؛ Bayan M. Sabbar | ||
| Department of Information and Communication Engineering, Al-Nahrain University, Iraq | ||
| تاریخ دریافت: 14 خرداد 1400، تاریخ بازنگری: 28 مرداد 1400، تاریخ پذیرش: 28 مهر 1400 | ||
| چکیده | ||
| The popularity of automatic modulation categorization (AMC) is high in recent years owing to the many advantages. When it comes to communication, reliability in an AMC is very critical. Increasing the number of signals exponentially increases the cost of using the AMC. Precise classification methods, such as neural networks, in which either the parameters of the neural network or the dimensions of the input or output variables are modified dynamically, are not successful in obtaining high accuracy results. To improve the accuracy of the modulation categorization, this study employs a "QIGA" feature selection model based on a Quantum (inspired) Genetic Algorithm (QIGA). QIGA is used to choose the correct functionality and to limit the number of examples that must be learned so that the overall system time is shortened and the cost of computing is reduced. Selecting excellent characteristics is enhanced via quantum computing and this is done to lower the complexity of the solutions. The internal validation results demonstrated that the QIGA model significantly improved the statistical match quality and significantly outperformed the other models. | ||
| کلیدواژه‌ها | ||
| Quantum computing؛ Genetic Algorithm؛ Automatic Modulation Classification | ||
| مراجع | ||
|
[1] M. Farooq-i-Azam and M.N. Ayyaz, Location and position estimation in wireless sensor networks, Curr. Status Future Trends, CRC Press, 2016. [2] S. Kharbech, I. Dayoub, M. Zwingelstein-Colin and E.P. Simon, On classifiers for blind feature-based automatic modulation classification over multiple-input–multiple-output channels, IET Commun. 10(7) (2016) 790–795. [3] O.A. Dobre, A. Abdi, Y. Bar-Ness and W. Su, Survey of automatic modulation classification techniques: Classical approaches and new trends, IET Commun. 1(2)(2007) 137–156. [4] C. Mair, G. Kadoda, M. Lefley, K. Phalp, C. Schofield, M. Shepperd and S. Webster, An investigation of machine learning based prediction systems, J. Syst. Software 53(1) (2000) 23–29. [5] M. O’Neill, L. Vanneschi, S. Gustafson and W. Banzhaf, Open issues in genetic programming, Genet. Program. Evolvable Mach. 11(3-4)(2010) 339–363. [6] J. Kobashigawa, H.S. Youn, M. Iskander and Z. Yun, Comparative study of genetic programming vs. neural networks for the classification of buried objects, IEEE Antennas Propag. Soci. Int. Symp. 2009, pp. 1–4. [7] M. Brezocnik, M. Kovacic and L. Gusel, Comparison between genetic algorithm and genetic programming approach for modeling the stress distribution, Materials Manuf. Processes, 20(3) (2005) 497–508. [8] H. Guo, L.B. Jack and A.K. Nandi, Feature generation using genetic programming with application to fault classification, IEEE Trans. Syst. Man. Cybern. Part B (Cybern.) 35(1) (2005) 89–99. [9] N.M. Razali and J. Geraghty, Genetic algorithm performance with different selection strategies in solving TSP, Proc. World Congress Eng. Hong Kong, Int. Assoc. Eng. 2(1) (2011) 1–6. [10] D. Rist`e, M.P. Da Silva, C.A. Ryan, A.W. Cross, A.D. C´orcoles, J.A. Smolin, J.M. Gambetta, J.M. Chow and B.R. Johnson, Demonstration of quantum advantage in machine learning, npj Quantum Inf. 3(1) (2017) 1–5. [11] Z. Laboudi and S. Chikhi, Comparison of genetic algorithm and quantum genetic algorithm, Int. Arab J. Inf. Technol. 9(3) (2012) 243–249. [12] L. Wang, F. Tang and H. Wu, Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation, Appl. Math. Comput. 171(2) (2005) 1141–1156. [13] S.Y. Kuo, Y.H. Chou and C.Y. Chen, Quantum-inspired algorithm for cyber-physical visual surveillance deployment systems, Comput. Networks 117 (2017) 5–18. [14] A.O. Pittenger, An Introduction to Quantum Computing Algorithms, Springer Sci. Bus. Media, 2012. [15] J. L. Xu, W. Su and M. Zhou, Likelihood-ratio approaches to automatic modulation classification, IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(4)-(2010) 455–469. [16] W. Su, Feature space analysis of modulation classification using very high-order statistics, IEEE Commun. Lett. 17(9) (2013) 1688–1691. [17] F. Hameed, O.A. Dobre and D.C. Popescu, On the likelihood-based approach to modulation classification, IEEE Trans. Wirel. Commun. 8(12) (2009) 5884–5892. [18] A.K. Nandi and E.E. Azzouz, Algorithms for automatic modulation recognition of communication signals, IEEE Trans. Commun. 46(4) (1998), 431–436. [19] F. Wang and X. Wang, Fast and robust modulation classification via Kolmogorov-Smirnov test, IEEE Trans. Commun. 58(8) (2010) 2324–2332. [20] K.C. Ho, W. Prokopiw and Y. Chan, Modulation identification of digital signals by the wavelet transform, IEE Proc.-Radar Sonar Navig. 147(4(2000) 169–176. [21] L. Hong and K.C. Ho, Identification of digital modulation types using the wavelet transform, IEEE Mil. Commun. Conf. Proc. 1999, pp. 427-431. [22] Z. Fucai, H. Yihua and H. Shiqi, Classification using wavelet packet decomposition and support vector machine for digital modulations, J. Syst. Eng. Electron. 19(5) (2008) 914–918. [23] P. Li, F. Wang and Z. Wang, Algorithm for modulation recognition based on high-order cumulants and subspace decomposition, Int. Conf. Signal Process. 2006. [24] M.R. Mirarab and M.A.Sobhani, Robust modulation classification for PSK/QAM/ASK using higher-order cumulants, Int. Conf. Inf. Commun. Signal Process. 2007, pp. 1–4. [25] L. Shen, S. Li, C. Song and F. Chen, Automatic modulation classification of MPSK signals using high order cumulants, Int. Conf. Signal Process. 1 (2006). [26] N. An, B. Li and M. Huang, Modulation classification of higher order MQAM signals using mixed-order moments and Fisher criterion, Int. Conf. Comput. Autom. Eng.(ICCAE), 3 (2010), pp. 150-153. [27] M.W. Aslam, Z. Zhu and A. K. Nandi, Automatic digital modulation classification using genetic programming with K-nearest neighbor, Mil. Commun. Conf., 2010, pp. 1731–1736. [28] Z. Shan, Z. Xin and W. Ying, Improved modulation classification of MPSK signals based on high order cumulants, Int. Conf. Future Compu. Commun. 2 V2-444. [29] M. Zhang, V.B. Ciesielski and P.Andreae, A domain-independent window approach to multiclass object detection using genetic programming, EURASIP J. Adv. Signal Process. 8 (2003) 1–19. [30] L. Zhang, L. B. Jack and A. K. Nandi, Extending genetic programming for multi-class classification by combining k-nearest neighbor, Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 5(2005) v-349. | ||
|
آمار تعداد مشاهده مقاله: 16,038 تعداد دریافت فایل اصل مقاله: 7,516 |
||