TY - JOUR A2 - Falcone, Francisco AU - Li, Yupeng AU - Zhang, Jianhua AU - He, Ruisi AU - Tian, Lei AU - Wei, Hewen PY - 2019 DA - 2019/12/27 TI - Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering SP - 4639612 VL - 2019 AB - In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance. SN - 1687-5869 UR - https://doi.org/10.1155/2019/4639612 DO - 10.1155/2019/4639612 JF - International Journal of Antennas and Propagation PB - Hindawi KW - ER -