面向非合作无线网络的频谱态势预测方法

Spectrum situation prediction for non-cooperative wireless networks

在复杂电磁环境背景下,针对非合作无线网络的频谱态势预测问题展开研究。借助机器学习理论,提取已侦测到频谱态势数据的时、空、频三维特性,并充分挖掘其三维特征内在的相关性,构建有针对性的频谱预测框架,从而有效预判非合作方通信节点的频率调整行为。相关研究结果表明,当非合作无线网络通信过程中存在频率调整行为时,只要能够截获足够的频谱数据,利用开发的频谱预测框架对未来时刻的频率调整行为有效地进行单步或多步预测,就可实现对目标系统未来可能使用的工作频率的精准锁定。精确地瞄准锁定目标系统未来可能使用的工作频率,可为后续通信跟踪及干扰等任务提供关键的技术支持。

The spectrum situation prediction of non-cooperative wireless network in the complex electromagnetic environment is investigated. Based on machine learning theory, the three-dimensional characteristics of time, space, and frequency of collected spectrum situation data are extracted;the inherent correlations in the three-dimensional characteristics are fully data mined;and the spectrum prediction frameworks are built to predict frequency adjustment behavior of non-cooperative communication nodes. The results show that the single-step or multi-step prediction for the frequency can be performed on the frequency adjustment for future moments by exploiting the spectrum prediction frameworks as long as sufficient spectrum situation data can be intercepted when the frequency adjustment exists in the communication process of non-cooperative wireless networks. Therefore, the possible frequency used in the future for the target system can be accurately locked in. This work can provide key technical support for the subsequent communication tracking and interference tasks.