Short-term forecasting of the likelihood of interference to groundwave users in the lowest part of the HF spectrum

In the design and performance evaluation of practical HF communication systems, it is essential to use procedures that assess the detrimental effect of interference from other users in a near real time mode. These procedures can extend system capability to estimate interference background, in the context of real time channel evaluation (RTCE) in order to advise operators on typical interference occupancy levels and to improve the quality and reliability of radio communication services through adaptation of communication parameters. In this study a Neural Network approach is proposed for the short-term forecasting of the likelihood of interference experienced by HF groundwave communication systems. In particular this paper describes the development of neural network models to indicate the degree of spectral congestion in frequency allocations in the lowest part of the HF spectrum (1.6 to 4 MHz) 1 hour in advance, as a function of the present congestion level, time of day, season, and field strength threshold. The modeled parameter, congestion, is defined as the relative number of narrow frequency channels (1 kHz wide) within a frequency allocation that have signals above a given threshold.