Regional Refined Long-Term Predictions Method of Usable Frequency for HF Communication Based on Machine Learning Over Asia

Abstract

Due to the unique advantages of long-distance, non-relay, and flexible deployment, high frequency (HF) communication plays a vital role in military communication, disaster relief, and global broadcasting, etc. To satisfying the spectrum planning requirement for the next generation intelligent HF communication system, the machine learning method is introduced to develop the long-term prediction method of the usable working frequency for HF wireless communication. Especially, the refined mapping model of maximum usable frequency (MUF) propagation factor for one hop on the F2 layer is first reconstructed by using the statistical machine learning method. Then, the new mapping models of conversion factors of optimum working frequency (OWF) and the highest probable frequency (HPF) are proposed by using the fine-grained solar activity parameters and coupling with two geomagnetic activity parameters. This proposed model has higher prediction accuracy for the MUF, OWF, and HPF over Asia. Compared with the International Telecommunication Union (ITU) recommended model, the root-mean-square errors of MUF, OWF, and HPF are reduced by 1.18, 1.64, and 1.06 MHz, and the accuracies are improved by 10.89%, 15.47%, and 9.10%, respectively. The proposed model can achieve intelligent frequency planning for HF communication and has great potential in terms of improving HF communication quality, reliability, and efficiency.