24-Hour Neural Network Congestion Models for High-Frequency Broadcast Users

Abstract

This paper presents the development of Neural Network models to predict the likelihood of interference experienced by Broadcast users in the HF spectrum (3-30 MHz). The models are based upon several years of measurements recorded at Linkoping (Sweden) across the HF band, covering a substantial part of a sunspot cycle. The dataset used for the model development is a result of a long-term project being undertaken jointly by the University of Manchester and by the Swedish Defence Research Establishment, to measure systematically and to analyze the occupancy of the entire HF spectrum. The measure of occupancy used is congestion, which is defined as the fraction of channels within a certain frequency allocation with signals exceeding a given threshold. The procedures for measuring and modeling congestion as a function of solar activity, time of day, day of year and incident field strength threshold are briefly presented. The accuracy of the predictions produced by the developed models demonstrate their ability to successfully capture the 24-hour, seasonal and long-term trend in the variability of congestion. These models can be used to advise operators on typical interference occupancy levels and assist the HF broadcast service in the planning of frequency usage and management by assessing the interference effect to short-wave broadcasting in an effort to alleviate spectral congestion in the HF broadcast bands.