Clustering in the Wireless Channel with a Power Weighted Statistical Mixture Model in Indoor Scenario  

作　　者：Yupeng Li Jianhua Zhang Pan Tang Lei Tian 

机构地区：[1]Key Lab of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China [2]State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

出　　处：《China Communications》2019年第7期83-95,共13页中国通信（英文版）

基　　金：supported by National Science and Technology Major Program of the Ministry of Science and Technology (No.2018ZX03001031);Key program of Beijing Municipal Natural Science Foundation (No. L172030);Beijing Municipal Science & Technology Commission Project (No. Z171100005217001);Key Project of State Key Lab of Networking and Switching Technology (NST20170205);National Key Technology Research and Development Program of the Ministry of Science and Technology of China (NO. 2012BAF14B01)

摘　　要：Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM (PGMM) is introduced to model the channel multipath components (MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization (EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian (VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model(GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM(PGMM)is introduced to model the channel multipath components(MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization(EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian(VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.

关 键 词：channel MULTIPATH CLUSTERING mmWave Gaussian mixture model EXPECTATION MAXIMIZATION VARIATIONAL Bayesian INFERENCE 

分 类 号：TN[电子电信]