Research On Clustering Algorithm For 5G Channel Model

Due to the wide use of multiple input and multiple output system,the amount of data in the wireless communication system grows exponentially,and how to accurately and easily model the wireless channel has also become an important research topic.Meanwhile,the cluster-based channel model is widely used because it can maintain the accuracy of the channel model while reducing the complexity,and is also written into the channel standard model by most organizations.It is also an important step to cluster multipath channels using clustering algorithm.At present,the Gaussian Mixture Model(Gaussian Mixture Model,GMM)which is commonly used as clustering algorithm is unable to cluster channel multipath accurately due to the mechanism of the algorithm.Therefore,based on the channel data obtained from the channel measurement in the actual scene,this paper studies the clustering algorithm used in the future wireless channel,verifies and analyzes several algorithms in the measured data,and makes a statistical analysis of the clustering results.On this basis,the evolution of clusters in time-varying channels is further studied to lay a foundation for the application of channel modeling in high-speed scenarios in the future.The specific research contents are as follows:1.Wireless channel measurement at 3.5GHz in 5G candidate band.In order to obtain the channel multipath data used to study the clustering algorithm,the channel measurement in the real environment was carried out,the 5G candidate band 3.5GHz is selected to carry out the channel measurement in the outdoor to indoor scene.After obtaining the original data by measurement,the parameter estimation algorithm(Sapce-alternating Generalized Expection-Maximization,SAGE)is used to extract the small-scale parameters of the channel multipath from the channel impulse response obtained from the actual measurement,so the channel multipath data for later clustering research is obtained.2.Clustering algorithm in static channel.The GMM is introduced to solve the disadvantages of the traditional KMeans clustering algorithm,which has low accuracy and is not suitable for large amounts of data.This algorithm is based on the principle of Bayes,which can significantly improve the clustering accuracy and is suitable for scenes with large amounts of data.In order to further improve the accuracy of clustering,this paper puts forward the gamma beta mixture model(Gamma Beta Mixture Model,GBMM),the model is a kind of non gaussian mixture model,beta and gamma distribution were used respectively to the fitting of Angle data and time delay,making more accord with the characteristics of the channel multipath data distribution,and the clustering performance of GMM model and GBMM model is verified in the actual measurement parameters,meanwhile,the cluster parameters obtained by the two algorithms were analyzed for the statistical characteristics within the cluster.3.Research on multi-channel cluster tracking algorithm for time-varying channels.At present,the tracking algorithms of clustering multipaths in time-varying channels are relatively scarce.To improve this phenomenon,a tracking framework combining gamma beta mixture model and maximum weight matching weight of bipartite graph method is proposed to cluster and track the channel multipath in time-varying channels.In order to further improve the accuracy of tracking and clustering,a probabilistic tracking algorithm is proposed,which can effectively solve the defects of biparticiple graph based tracking algorithm,such as too strong subjectivity and low accuracy.In order to verify the performance of the algorithm,the 3GPP(3rd Generation Partnership Project,3GPP)TR38.901 standard channel model is used to generate the simulation data with time-varying characteristics,and the data is used to verify and analyze the tracking algorithm,meanwhile,the cluster parameters obtained by the two algorithms were analyzed for the statistical characteristics within the cluster.Above all,for the future wireless communication systems,this paper carried out clustering research on static channel and time-varying channel,in order to validate the proposed algorithm,the channel measurement in the real scene is carried out.Based on the measured data and the simulation data,the clustering algorithm in static channel and the clustering tracking algorithm in time-varying channel are verified and analyzed respectively.The research of this paper provides a reference for the application of channel model in the future scenarios with large amounts of data and mobile scenarios.