| Title | LSTM-based Frequency Hopping Sequence Prediction |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Gao, Xu, Jianliang, Shen, Weiguo, Wang, Wei, Liu, Zitong, Ding, Guoru |
| Conference Name | 2020 International Conference on Wireless Communications and Signal Processing (WCSP) |
| Keywords | Deep Learning, frequency control, frequency-hopping (FH), Logic gates, LSTM, Predictive Metrics, Predictive models, pubcrawl, Resiliency, Scalability, Sequence Prediction, spread spectrum communication, Time Frequency Analysis and Security, time series, Time series analysis, Time-frequency Analysis, Training |
| Abstract | The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction. |
| DOI | 10.1109/WCSP49889.2020.9299717 |
| Citation Key | li_lstm-based_2020 |
LSTM-based Frequency Hopping Sequence Prediction