Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning

Wireless channel scenarios identification is of pivotal significance for dedicated wireless communication design, especially for the heterogeneous network covering rich propagation environments. In this paper, the identification problem is investigated by machine learning approaches. To enhance the identification performance, some preprocessing methods, mainly referring to the data normalization and dimension reduction, are adopted. Then, both supervised and unsupervised learning algorithms, including <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN), support vector machine (SVM), <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means, and Gaussian mixture model (GMM) are used to realize the scenarios identification, respectively. Finally, the identification performance of these four approaches are validated both on the actual measured HSR wireless channel data sets and the QuaDRiGa channel emulation platform with the ability of multiple scenarios emulation. Most of the results indicate that <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN and SVM approaches can achieve an accuracy over 90%. As for those two unsupervised learning approaches, the GMM proves to be a promising approach by presenting a performance close to the former two approaches without training process, whereas the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means yields an accuracy about 80%.