Abstract
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.
Highlights
In three-dimensional (3D) multiple-input multipleoutput (MIMO) channels, adjacent multipaths have similar parameters and always present correlations with each other
Erefore, the cluster of channel multipaths is defined as a set of multipath components (MPCs) with similar parameters consisting of the elevation angle of departure (EOD), the azimuth angle of departure (AOD), the elevation angle of arrival (EOA), the azimuth angle of arrival (AOA), the delay (τ) [8], etc
If [L(T + 1) − L(T)]/L(T) < 10− 8, the iterations are terminated, where L is the log-likelihood of the dataset. e individuals of the differential evolution (DE)-EM is set to 20, which can guarantee a relative high convergence precision with an acceptable computation complexity. ere are some additional parameters to be predefined in the DE-EM: the crossover of the DE-EM is 0.8, formula (10) is chosen as the mutation strategy, and the scale factor Q is 0.8
Summary
In three-dimensional (3D) multiple-input multipleoutput (MIMO) channels, adjacent multipaths have similar parameters and always present correlations with each other. All the abovementioned clustering algorithms do not consider sufficient statistical information of the MPCs. all the abovementioned clustering algorithms do not consider sufficient statistical information of the MPCs To this end, we resort to the Gaussian mixture model (GMM) [13, 14], which delivers the mean information of channel MPCs and their distributions. E expectation-maximization (EM) algorithm is a usually used tool to estimate the GMM parameter set [16] It maximizes the log-likelihood value of observed dataset, given the model via iterating between maximizing the loglikelihood value (called as M-step) and calculating the loglikelihood expectation (called as E-step), whereas the EM algorithm is a deterministic optimization technique, which can reach merely a local optimization depending on the initialization heavily. A maximum likelihood (ML) estimator would solve the global search problem, while its computing complexity may be much higher
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
