An Efficient Method for Modulation Recognition of MPSK Signals in Fading Channels
Subject Areas : electrical and computer engineering
S.
Hakimi
1
(Ferdosi University)
Keywords: Bees algorithm modulation recognition neural network,
Abstract :
Automatic modulation recognition of digital signals is an essential for intelligent communication systems. Most automatic classifications of digital signal types deal with recognizing signals formats in presence of additive white Gaussian noise (AWGN) in channels. However, real world communication environments, such as wireless communication channels, suffer from fading effects. There are few methods proposed to perform in fading channels. This paper presents a high efficient method for identification of M-array phase shift keying (MPSK) digital signal type. The proposed method is heuristic hybrid, formed by a multilayer perceptron (MLP) neural network as the classifier and the bees algorithm (BA) as the optimizer. An equalizer is also used to reduce channel effects. A suitable combination of higher order statistics, up to eighth, is considered as prominent characteristics of signals. Simulation results validate the high efficiency of the proposed technique in recognizing the types of digital signals even at low SNRs.
[1] Y. Yang, J. N. Chang, J. C. Liu, and C. H. Liu, "Maximum log-likelihood function-based QAM signal classification over fading channels," Wireless Personal Communications, vol. 28, no. 1, pp. 77-94, Jan. 2004.
[2] J. R. Treichler, "Application of blind equalization techniques to voiceband and RF modems," IFAC Proceedings Volumes, vol. 25, no. 14, 443-451, Jul. 1992.
[3] Q. Yang, O. Dayton, L. Xue, Z. Ruolin, V. Chakravarthy, and W. Zhiqiang, "Software-defined radio based automatic blind hierarchical modulation detector via second-order cyclostationary analysis and fourth-order cumulant," in Proc. Military Communications Conf. (MILCOM), vol. 25, pp. 441-446, San Diego, CA, USA,Nov. 2013.
[4] W. Sue, L. X. Jefferson, and Z. Mengchou, "Real-time modulation classification based on maximum likelihood," IEEE Comm Letters, vol. 12, no. 11, pp. 801-803, Nov. 2008.
[5] Y. Yang and S. S. Soliman, "A suboptimal algorithm for modulation classification," IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 1, pp. 38-45, Jan. 1997.
[6] A. K. Nandi and E. E. Azzouz, "Algorithms for automatic modulation recognition of communication signals," IEEE Trans. on Communication, vol. 46, no. 4, pp. 431-436, Apr. 1998.
[7] L. Hong and K. C. Ho, "BPSK abd QPSK modulation classification with unknown signal level," in IEEE Military Communications Conf., MILCOM'00, vol. 2, pp. 976-980, Oct. 2000.
[8] A. E. El-Mahdy and N. M. Namazi, "Classification of multiple M-ary frequency-shift keying signals over a Rayleigh fading channel," IEEE Trans. on Communication, vol. 50, no. 6, pp. 967-974, Jun. 2002.
[9] S. Z. Hsue and S. S. Soliman, "Automatic modulation classification using zero-crossing," IEE Proceedings F - Radar and Signal Processing, vol. 137, no. 6, pp. 459-464, Dec. 1990.
[10] B. G. Mobasseri, "Digital modulation classification using constellation shape," Signal Processing, vol. 80, no. 2, pp. 251-277, Feb. 2000.
[11] E. E. Azzouz and A. K. Nandi, " Automatic identification of digital modulation types,," Signal Processing, vol. 47, no. 1, pp. 55-69, Nov. 1995.
[12] J. Lopatka and P. Macrej, "Automatic modulation classification using statisticalmoments and a fuzzy classifier," in Proc. 5th Int. Conf. on Signal Processing, ICSP'00, vol. 3, pp. 1500-1506, Beijing, China, Aug. 2000.
[13] N. Chani and R. Lamontagne, "Neural networks applied to the classification of spectral features for automatic modulation recognition," in Proc. IEEE Military Communications Conf., MILCOM'93, Boston, MA, USA, vol. 1, pp. 111-115, Oct. 1993.
[14] G. Hagedorn, B. James, and C. Miller, "Neural network recognition of signal modulation types," Intelligent Engineering Systems Through Artificial Neural Networks , vol. 7, pp. 170-175, St. Louis, Missouri, USA, Nov. 1997.
[15] C. Louis and P. Sehier, "Automatic modulation recognition with neural network," in Proc. IEEE Military Communications Conf., MILCOM'94, vol. 13, pp. 713-717, Fort Monmouth, NJ, USA, Oct. 1994.
[16] L. Mingquan, X. Xianci, and L. Leming, "Cyclic spectral features based modulation recognition," in International Conf. on Communications Technology, ICCT'96, vol. 2, pp. 792-795, Beijing, China, May 1996.
[17] Y. Zhao, G. Ren, X. Wang, Z. Wu, and X. Gu, "Automatic digital modulation recognition using artificial neural networks," in Proc. IEEE Int. Conf. Neural Networks & Signal Processing, ICNNSP'03, vol. 1, pp. 257-260, Nanjing, China, Dec. 2003.
[18] H. Mustafa and M. Dorolovacki, "Digital modulation recognition using SVM classifier," in Proc. 38th Asilomar Conf. on Signals, Systems and Computers, vol. 2, pp. 2238-2242, Pacific Grove, CA, USA, Nov. 2004.
[19] E. Avci, D. Hanbay, and A. Varol, "An expert discretewavelet adaptive network based fuzzy inference systemfor digital modulation recognition," Expert Systems with Applications, vol. 33, no. 3, pp. 582-589, Oct. 2007.
[20] A. E. Zadeh and M. Aredebilipour, "Numericalmethods for signal identification," J. of Numerical Methods in Engineering, vol. 5, no. 2, pp. 34-42, Jul. 2008.
[21] M. W. Aslam, Z. Zhu, and A. K. Nandi, "Automatic modulation classification using combination of genetic programming and KNN," IEEE Trans. on Wireless Communications, vol. 11, no. 8, pp. 2742-2750, Aug. 2012.
[22] P. Hoeher, "A statistical discrete-time model for the WSSUS multipath channel," IEEE Trans. on Vehicular Technology, vol. 41, no. 4, pp. 461-468, Nov. 1992.
[23] D. Godard, "Self-recovering equalization and carrier tracking in two-dimensional data communication systems," IEEE Trans. on Communications, vol. 28, no. 11, pp. 1867-1875, Nov. 1980.
[24] J. R. Treichler and B. G. Agee, "A new approach to multipath correction of constant modulus signals," IEEE Trans. on Acoustics, Speech Signal Processing, vol. 31, no. 2, pp. 459-472, Apr. 1983.
[25] N. K. Jablon, "Joint blind equalization, carrier recovery and timin recovery for high-orde QAM signal constellations," IEEE Trans. Signal Process., vol. 40, no. 6, pp. 1383-1398, Jun. 1992.
[26] R. Johnson, P. Schniter, T. J. Endres, J. D. Behm, D. R. Brown, and R. A. Casas, "Blind equalization using the constant modulus criterion: a review," Proceedings of the IEEE , vol. 86, no. 10, pp. 1927-1950, Oct. 1998.
[27] J. G. Proakis, Digital Communications, New York: McGraw-Hill, 2001.
[28] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., New Jersey, USA: Prentice-Hall, 1999.
[29] C. L. P. Sehier, "Automatic modulation recognition with a hierarchical neural network," in Proc. Military Communications Conf., MILCOM'93, vol. 12, pp. 111-115, New York, USA, Oct. 1993.
[30] M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: the RPROP algorithm," in Proc. IEEE Int. Conf. on Neural Networks, vol. 1, pp. 586-591, Apr. 1993.
[31] D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, and S. Zaidi, "The bees algorithm, a novel tool for complex optimization problems," in Proc. 2nd Int Virtual Conf. on Intelligent Production Machines and Systems, IPROMS'06, vol. 2, pp. 454-459, Jul. 2006.
[32] Y. Wang, L. Li, J. Ni, and S. Huang, "Feature selection using tabu search with long-term memories and probabilistic neural networks," J. of Pattern Recognition Letter, vol. 30, no. 7, pp. 661-670, May 2009.
[33] A. A. Minai and R. D. Williams, "Back-propagation heuristics: a study of the extended delta-bar-delta algorithm," in Proc. Int. Joint Conf. on Neural Networks, vol. 2, pp. 595-600, San Diego, California, US, Jun. 1990.
[34] D. Karabog and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, vol. 8, no. 1, pp. 687-697, Jan. 2008.
[35] K. S. Tang, K. F. Man, S. Kwong, and Q. He, "Genetic algorithms and their applications," IEEE Signal Processing Magazine, vol. 13, no. 6, pp. 22-37, Nov. 1996.
[36] R. Eberhart and J. Kennedy," A new optimizer using particle swarm theory," in Proc. of Int. Sym. on Micro Machine and Human Science, vol. 6, pp. 39-43, Oct. 1995.
[37] D. T. Gillespie, "Moment expansion representation of probability density functions," American J. of Physics, vol. 49, no. 6, pp. 552-555, Jun. 1981.