Visual Analysis Of Real-Time Radio Spectrum Landscape

The radio spectrum refers to the collection of radio signals in different frequency ranges and is one of the fundamental resources for wireless communication.In modern society,wireless communication has a wide range of applications in various fields,such as military,aviation,broadcasting,and mobile communication.The radio spectrum landscape refers to the usage and distribution of spectrum resources in the radio environment.Understanding and mastering the radio spectrum landscape is crucial for the reasonable utilization and management of radio spectrum.Traditional radio spectrum monitoring methods largely rely on the analysis of radio spectrum data,observing the spectrum and spectrogram plotted from the usage spectrum data to perceive the surrounding spectrum environment,and monitor and analyze the usage of radio frequency bands.However,this requires a lot of domain experience and is difficult to handle complex and changing signal types and interference sources.Therefore,introducing new data sources and advanced analysis methods into radio spectrum monitoring has become very important.Signal data refers to structured data extracted from spectrum data through signal detection algorithms,including signal characteristic parameters such as center frequency,bandwidth,strength,signal-to-noise ratio,etc.In this thesis,a visualization analysis of the radio spectrum landscape is performed by combining spectrum data with signal data.To address the problem of repeated recording of signal data,a density-based incremental clustering algorithm is proposed to improve computing speed while ensuring accuracy.A real-time radio spectrum landscape monitoring system is designed and implemented to help managers better understand and grasp changes in the electromagnetic environment,aiming to provide effective tools to ensure the safety and stability of wireless communication.The main work of this thesis is as follows:1.Incorporating wireless signal data into the analysis of spectrum situation,a densitybased incremental clustering algorithm was designed to address the issue of duplicated signal data recording.The algorithm can improve the clustering speed while ensuring the completion of signal data clustering task,making the signal data available in real-time spectrum monitoring systems.This provides a new perspective for observing and analyzing spectrum situations.2.A new method for visual analysis of the radio spectrum situation is proposed.With the introduction of new data sources,this thesis designs various visualization views that showcase the features of the radio spectrum situation and radio signal data from multiple perspectives.The aim is to assist radio monitoring personnel in comprehensively understanding and mastering the surrounding spectrum situation.3.Designed and implemented a real-time wireless radio spectrum visual analysis system.The system is based on software-defined radio and is capable of collecting wireless radio spectrum data in real-time.By combining the signal data with visual analysis methods,the system can analyze the wireless radio spectrum situation and gain a comprehensive understanding of the electromagnetic environment,in order to better manage and allocate spectrum resources.