Manifold learning-based automatic signal identification in cognitive radio networks
Manifold learning-based automatic signal identification in cognitive radio networks
- Author(s): S. Li ; X. Wang ; J. Wang
- DOI: 10.1049/iet-com.2010.0590
For access to this article, please select a purchase option:
Buy article PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
Thank you
Your recommendation has been sent to your librarian.
- Author(s): S. Li 1, 2 ; X. Wang 1 ; J. Wang 2
-
-
View affiliations
-
Affiliations:
1: College of Engineering, Swansea University, Swansea, UK
2: Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
-
Affiliations:
1: College of Engineering, Swansea University, Swansea, UK
- Source:
Volume 6, Issue 8,
22 May 2012,
p.
955 – 963
DOI: 10.1049/iet-com.2010.0590 , Print ISSN 1751-8628, Online ISSN 1751-8636
Adaptive signal identification has been an important issue in cognitive radio networks (CRNs). Most existing techniques require high-level signal-to-noise ratio (SNR) for signal identification. This study presents an intelligent technique that focuses on a theoretical and experimental study of the signal identification by using manifold learning algorithm in CRNs. The authors pose the problem of signal identification in CRNs as signal classification by using manifold learning on high dimensions, and a novel manifold learning algorithm named as SIEMAP is proposed, which is able to identify signals in a low-dimensional space. Simulation results indicate that SIEMAP outperforms classical methods in low dimensions and is capable of identifying signal types from the received signals.
Inspec keywords: telecommunication computing; cognitive radio; learning (artificial intelligence); signal classification
Other keywords:
Subjects: Knowledge engineering techniques; Digital signal processing; Communications computing; Signal processing and detection; Radio links and equipment
References
-
-
1)
- Ebrahimzadeh, A., Seyedin, S.A.: `A novel hierarchical method for digital signal type classification', Int. Conf. on Applied Informatics and Communications, June 2006, WI, USA, p. 388–393.
-
2)
- A.N. Mody , S.R. Blatt , D.G. Mills , T.P. McElwain , J.D. Thammakhoune . Recent advances in cognitive communications. IEEE Commun. Mag. , 10 , 54 - 61
-
3)
- G. Xin , D. Zhan , Z. Zhou . Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. B, Cybern. , 6 , 1098 - 1107
-
4)
- B. Le , T.W. Rondeau , C.W. Bostian . Cognitive radio realities. Wirel. Commun. Mobile Comput. , 9 , 1037 - 1048
-
5)
- T. Yücek , H. Arslan . A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. , 1
-
6)
- J. Wang , Z. Zhou , A. Zhou . (2006) Machine learning and it's application.
-
7)
- Xu, Y., Ge, L., Wang, B.: `Digital modulation recognition method based on tree-structured neural networks', Int. Conf. on Communication Software and Networks, February 2009, Macau, China, p. 708–712.
-
8)
- M. Boutin , G. Kemper . On reconstructring n-point configurations from the distribution of distances or areas. Adv. Appl. Math. , 4 , 709 - 735
-
9)
- B. Ramkumar . Automatic modulation classification for cognitive radios using cyclic feature detection. IEEE Circuits Syst. Mag. , 2 , 27 - 45
-
10)
- S.T. Roweis , L.K. Saul . Nonlinear dimensionality reduction by locally linear embedding. Science. , 5500 , 2323 - 2326
-
11)
- Bixio, L., Ottonello, M., Sallam, H., Raffetto, M., Regazzoni, C.S.: `Signal classification based on spectral redundancy and neural network ensembles', Fourth Int. Conf. on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), June 2009, Hannover, Germany, p. 1–6.
-
12)
- Martin, H.C., Zhang, N., Jain, A.K.: `Nonlinear manifold learning for data stream', Proc. SIAM Int. Conf. for Data Mining, 2004, p. 33–44.
-
13)
- J. Mitola , G.Q. Maguire . Cognitive radio: making software radios more personal. IEEE Pers. Commun. , 9 , 13 - 18
-
14)
- A. Attar , O. Holland , M.R. Nakhai , A.H. Aghvami . Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks. IET Commun. , 6 , 806 - 814
-
15)
- Zhang, Q.: `Cognitive radio on a reconfigurable MPSoC platform', Report of University of Twente, 2009.
-
16)
- Liu, B., Ho, K.C.: `Identification of CDMA signal and GSM signal using the wavelet transform', Proc. 42nd Midwest Symp. on Circuits and Systems Las Cruces, 1999, NM, USA, 2, p. 678–681.
-
17)
- S. Li , D. Zhang . A novel manifold learning algorithm for localization estimation in wireless sensor networks. IEICE Trans. Commun. , 12 , 3469 - 3500
-
18)
- E. Azzouz , A. Nandi . (1996) Automatic modulation recognition of communication signals.
-
19)
- T. Kohonen . (1995) Self-organizing maps.
-
20)
- S. Li , D. Zhang , X. Wang . Node localization in Wireless sensor networks based on self-organizing isometric embedding. Enterprise Inf. Syst. , 3 , 259 - 273
-
21)
- A.K. Nandi , E.E. Azzouz . Automatic analogue modulations recognition. Signal Process. , 2 , 211 - 222
-
22)
- J.B. Tenenbaum , V. de Silva , J.C. Langford . A global geometric framework for nonlinear dimensionality reduction. Science , 5500 , 2319 - 2323
-
23)
- Li, J., Wang, J., Fan, X., Zhang, Y.: `Automatic digital modulation recognition using feature subset selection', Progress in Electromagnetics Research Symp., March 2008, Hangzhou, China, p. 24–28.
-
24)
- J. Li , H. Chen , J. Chen , D. Wang . Automatic digital modulation recognition based on Euclidean distance in hyperspace. IEICE Trans. Commun. , 2 , 2245 - 2248
-
25)
- Mustafa, H., Doroslovacki, M.: `Digital modulation recognition using support vector machine classifier', 38thAsilomar Conf. on Signals, Systems and Computers, 2004, Piscataway, NJ, USA, 2, p. 2238–2242.
-
26)
- Dobre, O.A., Abdi, A., Bar-Ness, Y., Su, W.: `Cyclostationarity-based blind classification of analog and digital modulations', IEEE Proc. Military Communications Conf., 2006, WA, DC, USA, p. 1–7.
-
27)
- Y. Hou , Z. Ding , P. He . Self-organizing isometric embedding. J. Comput. Res. Dev. , 2 , 188 - 195
-
28)
- Park, C.S., Jang, W., Nah, S.P., Kim, D.Y.: `Automatic modulation recognition using support vector machine in software radio applications', Proc. Ninth Int. Conf. on Advanced Communication Technology, 2007, Gangwon-Do, South Korea, p. 9–12.
-
29)
- Bixio, L., Oliveri, G., Ottonello, M., Regazzoni, S.: `OFDM recognition based on cyclostationary analysis in an Open Spectrum scenario', IEEE 69th Vehicular Technology Conf., April 2009, Barcelona, Spain, p. 1–5.
-
30)
- A. Ebrahimzadeh , A. Ranjbar . Intelligent digital signal-type identification. Artif. Intell. , 4 , 569 - 577
-
31)
- Li, X., Han, H., Fan, D., Zhang, R., Liu, G.: `A digital modulation signals recognition method under the lower SNR', Seventh World Congress on Intelligent Control and Automation, June 2008, Chongqing, China, p. 3958–3962.
-
32)
- L. Cayton . (2008) Algorithms for manifold learning.
-
33)
- Cho, S., Lee, C.H., Chun, J., Ahn, D.: `Classification of digital modulations using the LPC', IEEE Proc. National Aerospace and Electronics, 2000, Dayton, OH, USA, p. 774–778.
-
34)
- B. Scholkopf , A. Smola , K.R. Muller . Nonlinear component analysis as a kernel eigvalue problem. Neural Comput. , 1299 - 1319
-
35)
- A. Swami , B.M. Sadler . Hierarchical digital modulation classification using cumulants. IEEE Trans. Commun. , 416 - 429
-
1)