Abstract
The Amateur Service is allocated approximately 3 MHz of spectrum in the HF band (3-30MHz) which is primarily used for long range communications via the ionosphere. However only a fraction of this resource is usually available due to unfavourable propagation conditions in the ionosphere imposed by solar activity on the HF channel. In this respect interference is considered a significant problem to overcome, in order to establish viable links at low transmission power. This paper presents the development of a set of Neural Network ensembles which can serve as a tool for predicting the likelihood of interference in the frequency allocations utilized by amateur users. The proposed approach successfully captures the temporal and long-term solar dependent variability of congestion, formally defined as the fraction of channels within a certain frequency allocation with signals exceeding a given threshold.
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
Haralambous, H., Papadopoulos, H.: Neural network prediction of HF spectral occupancy. In: Proceedings of the 8th Nordic HF Conference, HF 2007 (2007)
Haralambous, H., Papadopoulos, H., Economou, L.: Using neural networks for predicting the likelihood of interference to groundwave users in the HF spectrum. In: Proceedings of the 10th International Conference on Engineering Applications of Neural Networks (EANN 2007), pp. 200–209 (2007)
Haralambous, H., Papadopoulos, H.: 24-hour neural network congestion models for high-frequency broadcast users. IEEE Transactions on Broadcasting 55(1), 145–154 (2009)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Zhou, Z.H., Jiang, Y., Yang, Y.B., Chen, S.F.: Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine 24(1), 25–36 (2002)
Huang, F.J., Zhou, Z.H., Zhang, H.J., Chen, T.H.: Pose invariant face recognition. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 245–250. IEEE Computer Society Press, Los Alamitos
Mao, J.: A case study on bagging, boosting and basic ensembles of neural networks for OCR. In: Proceedings of the 1998 IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1828–1833. IEEE Computer Society Press, Los Alamitos
Chan, S.K., Gott, G.F., Laycock, P.J., Poole, C.R.: Hf spectral occupancy - a joint british/swedish experiment. In: Proceedings of HF 1992, Nordic Shortwave Conference (August 1992)
Economou, L., Haralambous, H., Green, P., Gott, G., Laycock, P., Broms, M., Boberg, S.: Aspects of hf spectral occupancy. In: Proceedings of the Eighth International Conference on hf Radio Systems and Techniques (IEE Conf. Publ. no.474), pp. 367–372 (2000)
Maslin, N.: HF Communications, a Systems Approach. Pitman (1987)
Demuth, H., Beale, M.: Neural Network Toolbox User’s Guide: For use with MATLAB. The MathWorks (1998)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Advances in Neural Information Processing Systems, pp. 231–238. MIT Press, Cambridge (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Papadopoulos, H., Haralambous, H. (2009). Predicting the Occupancy of the HF Amateur Service with Neural Network Ensembles. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)