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

H. Haralambous, H. Papadopoulos
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引用次数: 4

Abstract

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.
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对高频频谱最低部分的地波用户受到干扰的可能性的短期预测
在实际高频通信系统的设计和性能评估中,必须使用评估其他用户在近实时模式下干扰的有害影响的程序。这些程序可以扩展系统能力,在实时信道评估(RTCE)的背景下估计干扰,以便就典型的干扰占用水平向运营商提供建议,并通过调整通信参数提高无线电通信服务的质量和可靠性。本文提出了一种神经网络方法,用于短波地波通信系统受干扰可能性的短期预测。本文特别介绍了神经网络模型的发展,该模型可以根据当前拥塞程度、一天中的时间、季节和场强阈值,提前1小时表示HF频谱(1.6至4 MHz)最低部分频率分配中的频谱拥塞程度。建模参数“拥塞”被定义为频率分配中信号高于给定阈值的窄频率通道(1khz宽)的相对数量。
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