The Communication Relationship Discovery Based on the Spectrum Monitoring Data by Improved DBSCAN

Abstract

The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical positions in the communication network, so as to provide a basis for further speculation on the structure of the communication network. For massive spectrum signals, we can also obtain important information such as communication relationships and behaviors of communication individuals, without cracking the signal content, but by analyzing the physical characteristics and statistical laws of the spectrum signals. In order to overcome the difficulties and costs of analyzing communication behaviors from cracking the signal content in existing research, this paper studies the physical characteristics and statistical laws of spectrum signals based on the features of frequency hopping period, average power and time of signal occurrence. Because the spectrum signals generated by the communication individuals show clustering characteristics, this paper proposes a communication relationship mining method based on improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The method can accurately discover the communication relationship of the radio station from the incomplete spectrum monitoring data, without cracking the content carried by the spectrum signals, which provides a new idea for the mining and analysis of mass spectrum monitoring signals.