Neural network-based learning schemes for cognitive radio systems

Computer Communications

Volumn 31, Issue 14, 2008, Pages 3394-3404

  Tsagkaris, K ^a   Katidiotis, A ^a   Demestichas, P ^a  

^a UNIVERSITY OF PIRAEUS   (Greece)

Author keywords

Artificial neural networks; Cognition cycle; Cognitive radio; Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELLULAR RADIO SYSTEMS; COGNITIVE SYSTEMS; COMPUTER SOFTWARE; EDUCATION; IMAGE CLASSIFICATION; INTELLIGENT SYSTEMS; MODULATION; NEURAL NETWORKS; RADIO; RADIO EQUIPMENT; SOFTWARE PACKAGES; STRUCTURAL DESIGN; TELECOMMUNICATION; VEGETATION; WIRELESS TELECOMMUNICATION SYSTEMS;

ARTIFICIAL NEURAL NETWORKS; CARRIER FREQUENCIES; COGNITION CYCLE; COGNITIVE RADIO; COGNITIVE RADIO SYSTEMS; CONTINUOUS PROCESSING; DATA RATES; INTELLIGENT SOFTWARES; LEARNING; LEARNING MECHANISMS; LEARNING SCHEMES; NETWORK STRUCTURES; NETWORK-BASED LEARNING; OPERATING PARAMETERS; RADIO ACCESS TECHNOLOGIES; RADIO CONFIGURATION; RADIO SPECTRUMS; RAPID EVOLUTION; WIRELESS COMMUNICATIONS;

RADIO SYSTEMS;

EID: 49649129002     PISSN: 01403664     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.comcom.2008.05.040     Document Type: Article

References (22)

1. European Radiocommunications Committee (ERC), European table of frequency allocations and utilizations frequency range 9 kHz to 275 GHz, ERC Report 25, January 2002.
2. Moore S. Managing the radio spectrum: hands-on or back-off?. IT Prof. 6 2 (2004) 49-55
3. Leaves P., Moessner K., Tafazolli R., Grandblaise D., Bourse D., Tonjes R., and Breveglieri M. Dynamic spectrum allocation in composite reconfigurable wireless networks. Commun. Mag. IEEE 42 5 (2004) 72-81
4. Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE J. Select. Areas Commun. 23 2 (2005) 201-220
5. Mitola J., et al. Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6 4 (1999) 13-18
6. J. Mitola, Cognitive radio: an integrated agent architecture for software defined radio, Doctor of Technology, Royal Institute of Technology (KTH), Stockholm, Sweden, 2000.
7. C. Clancy, J. Hecker, E. Stuntebeck, T. O'Shea, Applications of Machine Learning to Cognitive Radio Networks, Wireless Communications, IEEE [see also IEEE Personal Communications], vol. 14, no. 4, August 2007, pp. 47-52.
8. Federal Communication Commission, Facilitating opportunities for flexible, efficient and reliable spectrum use employing cognitive radio technologies', ET Docket 03-108, Notice of Inquiry and Proposed Rulemaking and Order, May 2003.
9. Le B., Rondeau T.W., and Bostian C.W. Cognitive radio realities. Wirel. Commun. Mob. Comput. 7 9 (2007) 1037-1048
10. A.P. Rewagad, V.L. Soanawane, Artificial neural network based short term load forecasting, TENCON '98, in: Proceedings of the IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control, vol. 2, 1998, pp. 588-595.
11. H. Demuth, M. Beale, M. Hagan, Matlab Neural Network Toolbox User's Guide, Version 5.1, The MathWorks Inc., 2007.
12. Rosenblatt, Frank The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review, vol. 65, no. 6, 1958, pp. 386-408.
13. Widrow B., and Sterns S.D. Adaptive Signal Processing (1985), Prentice-Hall, New York
14. Elman J.L. Finding structure in time. Cogn. Sci. 14 (1990) 179-211
15. Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79 (1982) 2554-2558
16. B. Le, T.W. Rondeau, D. Maldonado, C.W. Bostian, Modulation identification using neural network for cognitive radios, SDR Forum Technical Conference, Anaheim, CA, 2005.
17. M. Gandetto, M. Guainazzo C.S. Regazzoni, Use of time-frequency analysis and neural networks for mode identification in a wireless software-defined radio approach, Eurasip JASP, Special Issue on Non Linear Signal Processing and Image Processing, vol. 13, 2004, pp. 1778-1790.
18. NIST/SEMATECH e-Handbook of Statistical Methods, available at http://www.itl.nist.gov/div898/handbook/, September 2002.
19. Masters T. Practical Neural Network Recipes in C++ (1993), Academic Press
20. Haykin S. Neural Networks, A Comprehensive Foundation. second ed. (1999), Prentice Hall, Upper Saddle River, NJ
21. B. Hassibi, D. Stork, G. Wolff, Optimal Brain Surgeon and general network pruning, in: Proceedings of the IEEE International Conference on Neural Networks, 1993, pp. 293-299.
22. E. Coersmeier, M. Hoffmann, H. Bothe, Combining cognitive radio and software radio approach for low complexity receiver architecture, ISART 2007, Boulder, Colorado, US, February 27, 2007.