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.
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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
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DOI: https://doi.org/10.1007/978-3-642-04277-5_34
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