Modulation classification of single-input multiple-output signals using asynchronous sensors

IEEE Sensors Journal

Volumn 15, Issue 1, 2015, Pages 346-357

  Su, Wei ^a  

^a US Army Communications and Electronics Command   (United States)

Author keywords

Cognitive radio; distributed sensors; high order statistics; modulation classification; sensor network; single input multiple output.; spectrum sensing

Indexed keywords

AMPLITUDE MODULATION; COGNITIVE RADIO; SENSOR NETWORKS;

DISTRIBUTED SENSOR; HIGH ORDER STATISTICS; MODULATION CLASSIFICATION; SINGLE INPUT MULTIPLE OUTPUTS; SPECTRUM SENSING;

MODULATION;

EID: 84910604353     PISSN: 1530437X     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSEN.2014.2343832     Document Type: Article

References (49)

1. W. Su, J. A. Kosinski, and M. Yu, Dual-use of modulation recognition techniques for digital communication signals, in Proc. IEEE LISAT, Long Island, NY, USA, May 2006, pp. 1-6
2. F. Liedtke, Computer simulation of an automatic classification procedure for digitally modulated communication signals with unknown parameters, Signal Process., vol. 6, no. 4, pp. 311-323, Aug. 1984
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., vol. 1, no. 2, pp. 137-156, Apr. 2007
4. J. A. Sills, Maximum-likelihood modulation classification for PSK/QAM, in Proc. IEEE Military Commun. Conf. (MILCOM), vol. 1. Atlantic City, NJ, USA, Oct. 1999, pp. 217-220
5. C. Y. Hwang and A. Polydoros, Likelihood classification of QAM signals and comparisons, in Proc. IEEE Inf. Theory Int. Symp., McLean, VA, USA, Nov. 1991, p. 65
6. P. Panagiotou, A. Anastasopoulos, and A. Polydoros, Likelihood ratio tests for modulation classification, in Proc. Military Commun. Conf. (MILCOM), vol. 2. Los Angeles, CA, USA, Oct. 2000, pp. 670-674
7. W. Wen and J. M. Mendel, A new maximum-likelihood method for modulation classification, in Proc. 29th IEEE Signals, Syst. Comput., vol. 2. Pacific Grove, CA, USA, Nov. 1995, pp. 1132-1136
8. O. Ozdemir, P. Varshney, and W. Su, Asynchronous hybrid maximum likelihood classification of linear modulations, in Proc. IEEE Globe Commun. Conf. (GLOBECOM), Atlanta, GA, USA, Dec. 2013, pp. 3235-3240
9. J. Xu, W. Su, and M. Zhou, Likelihood-ratio approaches to automatic modulation classification, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 4, pp. 455-469, Jul. 2011
10. A. Swami and B. M. Sadler, Modulation classification via hierarchical agglomerative cluster analysis, in Proc. 1st IEEE Signal Process. Workshop Signal Process. Adv. Wireless Commun., Paris, France, Apr. 1997, pp. 141-144
11. A. Swami and B. M. Sadler, Hierarchical digital modulation classification using cumulants, IEEE Trans. Commun., vol. 48, no. 3, pp. 416-429, Mar. 2000
12. C. M. Spooner, On the utility of sixth-order cyclic cumulants for RF signal classification, in Proc. 35th IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, USA, Nov. 2001, pp. 890-897
13. C. M. Spooner, Classification of co-channel communication signals using cyclic cumulants, in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, USA, Nov. 1995, pp. 531-536
14. W. Su and J. Kosinski, Data mining of modulation features using higher-order statistical transform for battlefield awareness, in Proc. Int. Conf. Data Mining, Las Vegas, NV, USA, Jun. 2007, pp. 404-408
15. W. Su and J. Kosinski, Higher-order blind signal feature separation: An enabling technology for battlefield awareness, in Proc. 25th Army Sci. Conf., Orlando, FL, USA, Nov. 2006
16. O. A. Dobre, Y. BarNess, and W. Su, Higher-order cyclic cumulants for high order modulation classification, in Proc. Military Commun. Conf. (MILCOM), Oct. 2003, pp. 112-117
17. O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, Cyclostationarity-based modulation classification of linear digital modulations in flat fading channels, Wireless Personal Commun., vol. 54, no. 4, pp. 699-717, Sep. 2010
18. O. A. Dobre, M. Oner, S. Rajan, and R. Inkol, Cyclostationarity-based robust algorithms for QAM signal identification, IEEE Commun. Lett., vol. 16, no. 1, pp. 12-15, Jan. 2012
19. P. Marchard, J.-L. Lacoume, and C. Le Martret, Multiple hypothesis modulation classification based on cyclic cumulants of different orders, in Proc. ICASSP, 1998, pp. 2157-2160
20. W. Su, Feature space analysis of modulation classification using very high-order statistics, IEEE Commun. Lett., vol. 17, no. 9, pp. 1688-1691, Sep. 2013
21. A. K. Das, P. Arabshahi, T.Wen, and W. Su, On feature based automatic classification of single and multitone signals, in Proc. IASTED Int. Conf. Wireless Opt. Commun., Banff, AB, Canada, Jul. 2009
22. A. Abdi, O. A. Dobre, R. Choudhry, Y. Bar-Ness, and W. Su, Modulation classification in fading channels using antenna arrays, in Proc. IEEE Military Commun. Conf. (MILCOM), Monterey, CA, USA, Nov. 2004, pp. 211-217
23. H. Li, A. Abdi, Y. Bar-Ness, and W. Su, A qHLRT modulation classifier with antenna array and two-stage CFO estimation in fading channels, in Proc. IEEE Sarnoff Symp., Mar. 2006, pp. 1-4
24. H. Li, A. Abdi, Y. Bar-Ness, and W. Su, Carrier frequency offset estimation in qHLRT modulation classifier with antenna arrays, in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Apr. 2006, pp. 1465-1470
25. W. Su, J. L. Xu, and M. Zhou, Real-time modulation classification based on maximum likelihood, IEEE Commun. Lett., vol. 12, no. 11, pp. 801-803, Nov. 2008
26. J. Xu, W. Su, and M. Zhou, Software defined radio equipped with rapid modulation recognition, IEEE Trans. Veh. Technol., vol. 59, no. 4, pp. 1659-1667, May 2010
27. E. Kanterakis and W. Su, OFDM signal classification in frequency selective Rayleigh channels, in Proc. Military Commun. Conf. (MILCOM), Baltimore, MD, USA, Nov. 2011, pp. 1-6
28. E. Kanterakis and W. Su, Blind OFDM parameter estimation techniques in frequency-selective Rayleigh channels, in Proc. IEEE Radio Wireless Symp., Phoenix, AZ, USA, Jan. 2011, pp. 150-153
29. V. Choqueuse, S. Azou, K. Yao, L. Collin, and G. Burel, Blind modulation recognition for MIMO Systems, in Proc. MTA Rev., Jun. 2009, pp. 183-195
30. H. Agirman-Tosun, A. M. Haimovich, O. Simeone, W. Su, and J. Dabin, Modulation classification of MIMO-OFDM signals by independent component analysis and support vector machines, in Proc. IEEE Asilomar Conf., Pacific Grove, CA, USA, Nov. 2011, pp. 1903-1907
31. E. Kanterakis and W. Su, Modulation classification in MIMO systems, in Proc. Military Commun. Conf. (MILCOM), San Diego, CA, USA, Nov. 2013, pp. 35-39
32. M. Muhlhaus, M. Oners, O. A. Dobre, and F. K. Joudral, A low complexity modulation classification algorithm for MIMO systems, IEEE Commun. Lett., vol. 17, no. 10, pp. 1881-1884, Oct. 2013
33. W. Su, Multi-sensor signal fusion for modulation classification of weak signals, U.S. Patent 8 085 882, Dec. 27, 2011
34. W. Su and J. Kosinski, Framework of network centric signal sensing for automatic modulation classification, in Proc. IEEE Int. Conf. Netw., Sensing Control, Chicago, IL, USA, Apr. 2010, pp. 543-539
35. J. Xu, W. Su, and M. Zhou, Distributed automatic modulation classification with multiple sensors, IEEE Sensors J., vol. 10, no. 11, pp. 1779-1785, Nov. 2010
36. Y. Zhang, N. Ansari, and W. Su, Optimal decision fusion based automatic modulation classification by using wireless sensor networks in multipath fading channel, in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), Huston, TX, USA, Dec. 2011, pp. 1-5
37. J. L. Xu, W. Su, and M. Zhou, Asynchronous and high-accuracy digital modulated signal detection by sensor networks, in Proc. Military Commun. Conf. (MILCOM), Baltimore, MD, USA, Nov. 2011, pp. 589-594
38. J. L. Xu, W. Su, and M. Zhou, Likelihood function-based modulation classification in bandwidth-constrained sensor networks, in Proc. IEEE Int. Conf. Netw., Sensing Control (ICNSC), Chicago, IL, USA, Apr. 2010, pp. 530-533
39. P. A. Forero, A. Cano, and G. B. Giannakis, Distributed featurebased modulation classification using wireless sensor networks, in Proc. Military Commun. Conf. (MILCOM), San Diego, CA, USA, Nov. 2008, pp. 1-7
40. G. B. Markovic and M. L. Dukic, Cooperative AMC schemes using cumulants with hard and soft decision fusion, in Proc. 20th IEEE Telecommun. Forum (TELFOR), Belgrade, Serbia, Nov. 2012, pp. 400-403
41. Y. Zhang, N. Ansari, and W. Su, Multi-sensor signal fusion based modulation classification by using wireless sensor networks, in Proc. IEEE Int. Conf. Commun. (ICC), Kyoto, Japan, Jun. 2011, pp. 1-5
42. W. Su, Signal sensing and modulation classification using pervasive sensor networks, in Proc. IEEE Int. Conf. Pervas. Comput. Commun. Conf. (PERCOM), San Diego, CA, USA, Mar. 2013, pp. 441-446
43. O. Ozdemir, R. Li, and P. Varshney, Hybrid maximum likelihood modulation classification using multiple radios, IEEE Commun. Lett., vol. 17, no. 10, pp. 1889-1892, Oct. 2013
44. S. Foulke et al., Multisensor modulation classification implementation considerations-USRP case study, in Proc. Military Commun. Conf. (MILCOM), Baltimore, MD, USA, Oct. 2014
45. P. M. Woodward, Probability and Information Theory With Applications to Radar. Norwood, MA, USA: Artech House, 1980
46. Interoperability and Performance Standards for Data Modems, Standard MIL-STD-188-110B, Apr. 2000
47. MARS Working Group. MIL-STD/STANAG Data Modem Primer, Data Modem Primer, v1.03 [Online]. Available: http://www.n2ckh.com/ MARS-ALE-FORUM/MIL-STD-MODEM-PRIMER.pdf
48. A. Mudry and B. Poulin. (1993, Oct.). Facsimile transmission standards, Defense Research Establishment Ottawa, Ottawa, ON, Canada, Tech. Note 93-18 [Online]. Available http://www.dtic.mil/dtic
49. J. Mendel, Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications, Proc. IEEE, vol. 79, no. 3, pp. 278-305, Mar. 1991