Abstract
Rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments asks for a technology for its intelligent handling. Cognitive radio systems (CRSs) have been proposed as one. A typical CRS implements a so called “cognition cycle”, during which it senses its environment, evaluates a set of candidate radio configurations to operate with and finally decides and adjusts its operating parameters expecting to move the radio toward an optimized operational state. As the process is often proved to be rather arduous and time consuming, learning mechanisms that are capable of exploiting measurements sensed from the environment, gathered experience and stored knowledge can be judged as rather beneficial in terms of speeding it up. Framed within this statement, this paper introduces and evaluates a mechanism which is based on a well-known unsupervised learning technique, called Self-Organizing maps (SOM), and is used for assisting a CRS to predict the bit-rate that can be obtained, when it senses specific input data from its environment, such as Received Signal Strength Identification (RSSI), number of input/output packets etc. Results show that the proposed method is successful up to a percent of 75.4%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mitola III, J., Maguire Jr., G.: Cognitive radio: making software radios more personal. IEEE Personal Commun. 6(4), 13–18 (1999)
Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications 23(2), 201–220 (2005)
Demestichas, P., Katidiotis, A., Tsagkaris, K., Adamopoulou, E., Demestichas, K.: Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks. Wireless Personal Communications 49(1), 87–105 (2009)
Katidiotis, A., Tsagkaris, K., Demestichas, K.: Performance Evaluation of Artificial Neural Network-Based Learning Schemes for Cognitive Radio Systems. In: Computers & Electrical Engineering. Elsevier, Amsterdam (in press)
Tsagkaris, K., Katidiotis, A., Demestichas, P.: Neural Network-based Learning schemes for Cognitive Radio systems. Computer Communications 31(14), 3394–3404 (2008)
Liang, M., Shen, J., Wang, G.: Identification of illicit drugs by using SOM neural networks. Journal of Physics D: Applied Physics 41 (2008)
Tokutaka, H., Yoshihara, K., Fujimura, K., Obu-Cann, K., Iwamoto, K.: Application of self-organizing maps to chemical analysis. Applied Surface Science, 144–145, 59–63 (1999)
Kaski, S., Hankela, T., Lagus, K., Kohonen, T.: WEBSOM – Self-organizing maps of document collections. Neurocomputing 21, 101–117 (1998)
Kohonen, T., Somervuo, P.: Self-Organizing Maps of Symbol Strings with Application to Speech Recognition (1997)
Matsuura, Y., Tuoya, S.M., Tokutaka, H., Ohkita, M.: The Identification of a Cancer Cell Gene by using SOM. In: 16th International Conference on Genome Informatics, Yokohama Pacifico, Japan, December 19-21 (2005)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907–2912 (1999)
Kohonen, T.: Self-Organizing Maps, 2nd edn. Series in Information Sciences, vol. 30. Springer, Heidelberg (1997)
Kohonen, T.: The Self-Organizing map. Neurocomputing 21, 1–6 (1998)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bantouna, A., Tsagkaris, K., Demestichas, P. (2010). Self-Organizing Maps for Improving the Channel Estimation and Predictive Modelling Phase of Cognitive Radio Systems. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-15822-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
Online ISBN: 978-3-642-15822-3
eBook Packages: Computer ScienceComputer Science (R0)