Research On Lossy Compression And Parallel Algorithm For Spectrum Monitoring Data

With the improvement of hardware level,the performance of radio spectrum monitoring equipment has been greatly improved,which also brings difficulties in the transmission of monitoring data and the storage pressure of historical monitoring data.In order to solve this problem and ensure the stability of data transmission and reduce the data storage space,this paper proposes a lossy compression algorithm for spectrum monitoring equipment,and addresses the impact of data loss on spectrum analysis.Correspondingly,this paper also proposes This lossy compression algorithm is integrated with the distributed spectrum monitoring system.This paper focuses on the lossy compression algorithm for spectrum monitoring data.1.Lossy compression algorithm for spectrum monitoring.Spectrum monitoring data has its own characteristics.In view of this characteristic,this paper proposes two lossy compression algorithms from the perspective of numerical compression and inter-frame compression.They are lossy compression algorithm based on piecewise linear of continuous time and lossy compression algorithm based on two-dimensional matrix dimension transformation.This article respectively explains the implementation process of the two algorithms and makes a brief analysis of the parameter selection.On this basis,compare the compression performance of the two algorithms on the same data file,analyze the advantages and disadvantages of the algorithm,and finally determine that the lossy compression algorithm based on two-dimensional matrix dimension transformation can better meet the actual situation of the spectrum monitoring data.Under the circumstances,the compression rate can reach as low as 68.2% and as high as 97.2%,which shows that this algorithm is of great significance for the transmission and storage of spectrum monitoring data.2.The impact of data loss on spectrum analysis information extraction.The biggest difference between lossy compression and lossless compression is that lossy compression will have data loss,while lossless compression will not,so it is necessary to analyze the impact of data loss.For this algorithm,it is to analyze the reconstruction of the data pair under different quantization parameters.The impact of spectrum information extraction.Finally,the conclusions of the analysis are summarized,and a quantization parameter selection algorithm based on a two-dimensional matrix dimension transformation algorithm is proposed to ensure that the lossy compression algorithm proposed in this paper has better adaptability in the field of spectrum monitoring.3.Integration of distributed spectrum monitoring system and lossy compression algorithm.The current spectrum analysis system is generally divided into C / S structure and distributed structure.Through the analysis of the two station structures,the C / S structure is too backward in performance and design.Therefore,the distributed spectrum monitoring system will be developed in the future.In the meantime,it can also conveniently integrate compression algorithms.This paper proposes a design scheme for distributed spectrum monitoring systems,and combines it with a parallel optimizationbased lossy compression algorithm based on two-dimensional matrix dimension transformation to propose a middleware based Distributed spectrum monitoring system.After comparing the algorithm's test and data,the lossy compression algorithm proposed in this paper has strong adaptability in the field of large-scale spectrum monitoring and meets the expected requirements.