- Research Article
- Open Access
Digital-Signal-Type Identification Using an Efficient Identifier
EURASIP Journal on Advances in Signal Processingvolume 2007, Article number: 037690 (2007)
Automatic digital-signal-type identification plays an important role for various applications. This paper presents a highly efficient identifier (technique) that identifies a variety of digital signal types. In this technique, a selected number of the higher-order moments and the higher-order cumulants up to eighth are utilized as the effective features. A hierarchical support-vector-machine- (SVMs) based structure is proposed for multiclass classification. A genetic algorithm is proposed in order to improve the performance of the identifier. Genetic algorithm selects the suitable parameters of SVMs that are used in the structure of the classifier. Simulation results show that the proposed identifier has high performance for identification of the considered digital signal types even at very low SNRs.
Iversen A: The use of artificial neural networks for automatic modulation recognition. In Tech. Rep. HW-MACS-TR-0009. Heriot-Watt University, Edinburgh, Scotland; 2003.
Wei W, Mendel JM: Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Transactions on Communications 2000,48(2):189-193. 10.1109/26.823550
Panagiotou P, Anastasopoulos A, Polydoros A: Likelihood ratio tests for modulation classification. Proceedings of the 21st Century Military Communications Conference (MILCOM '00), October 2000, Los Angeles, Calif, USA 2: 670–674.
Swami A, Sadler BM: Hierarchical digital modulation classification using cumulants. IEEE Transactions on Communications 2000,48(3):416-429. 10.1109/26.837045
Wong MLD, Nandi AK: Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Processing 2004,84(2):351-365. 10.1016/j.sigpro.2003.10.019
Mobasseri BG: Digital modulation classification using constellation shape. Signal Processing 2000,80(2):251-277. 10.1016/S0165-1684(99)00127-9
Azzouz EE, Nandi AK: Automatic identification of digital modulation types. Signal Processing 1995,47(1):55-69. 10.1016/0165-1684(95)00099-2
Lopatka J, Pedzisz M: Automatic modulation classification using statistical moments and a fuzzy classifier. Proceedings of the 5th International Conference on Signal Processing (ICSP '00), August 2000, Beijing, China 3: 1500–1506.
Ghani N, Lamontagne R: Neural networks applied to the classification of spectral features for automatic modulation recognition. Proceedings of IEEE Military Communications Conference (MILCOM '93), October 1993, Boston, Mass, USA 1: 111–115.
Nandi AK, Azzouz EE: Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications 1998,46(4):431-436. 10.1109/26.664294
Louis C, Sehier P: Automatic modulation recognition with a hierarchical neural network. Proceedings of IEEE Military Communications Conference (MILCOM '94), October 1994, Fort Monmouth, NJ, USA 3: 713–717.
Mingquan L, Xianci X, Leming L: Cyclic spectral features based modulation recognition. Proceedings of International Conference on Communication Technology (ICCT '96), May 1996, Beijing, China 2: 792–795.
Avci E, Hanbay D, Varol A: An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition. Expert Systems with Applications 2007,33(3):582-589. 10.1016/j.eswa.2006.06.001
Abrahamzadeh A, Seyedin SA: Automatic modulation type identification using WPA and SVM. International Journal of Tomography & Statistics 2006, 4: 17–28.
Wu Z, Wang X, Gao Z, Ren G: Automatic digital modulation recognition based on support vector machines. Proceedings of International Conference on Neural Networks and Brain (ICNNB '05), October 2005, Beijing, China 2: 1025–1028.
Mustafa H, Doroslovački M: Digital modulation recognition using support vector machine classifier. Proceedings of the 38th Asilomar Conference on Signals, Systems and Computers, November 2004, Pacific Grove, Calif, USA 2: 2238–2242.
Gang H, Jiandong L, Donghua L: Study of modulation recognition based on HOCs and SVM. Proceedings of the 59th IEEE Vehicular Technology Conference (VTC '04), May 2004, Milan, Italy 2: 898–902.
El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM: A support vector machine approach for detection of microcalcifications. IEEE Transactions on Medical Imaging 2002,21(12):1552-1563. 10.1109/TMI.2002.806569
Gürcan MN, Yardimci Y, Çetin AE, Ansari R: Detection of microcalcifications in mammograms using higher order statistics. IEEE Signal Processing Letters 1997,4(8):211-216.
McCullagh P: Tensor Methods in Statistics. Chapman & Hall, London, UK; 1987.
Cortes C, Vapnic V: Support vector network. Machine Learning 1995,20(3):273-297.
Schölkopf B, Burges C, Vapnik V: Extracting support data for a given task. Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (KDD '95), August 1995, Montreal, Canada 252–257.
Chapelle O, Vapnik V, Bousquet O, Mukherjee S: Choosing multiple parameters for support vector machines. Machine Learning 2002,46(1–3):131-159.
Michalewicz Z: Genetic Algorithms+Data Structures=Evolution Programs. 3rd edition. Springer, New York, NY, USA; 1999.