Hierarchical Dirichlet Process Based Gamma Mixture Modeling for Terahertz Band Wireless Communication Channels


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Karakoca E., Karabulut Kurt G. Z. , Gorcin A.

IEEE ACCESS, vol.10, pp.84635-84647, 2022 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10
  • Publication Date: 2022
  • Doi Number: 10.1109/access.2022.3197603
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded, Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.84635-84647
  • Keywords: Wireless communication, Data models, Probability density function, Mixture models, Fading channels, Bandwidth, Adaptation models, Terahertz communications, Channel estimation, Terahertz communications, statistical channel modeling, expectation maximization, Dirichlet process, Gamma mixture model, BAYESIAN DENSITY-ESTIMATION, PROPAGATION, INFERENCE, EM

Abstract

Due to the unique channel characteristics of Terahertz (THz), comprehensive propagation channel modeling is essential to understand the spectrum and develop reliable communication systems in these bands. In this work, we propose the utilization of the hierarchical Dirichlet Process Gamma Mixture Model (DPGMM) to characterize THz channels statistically in the absence of any prior knowledge. DPGMM provides mixture component parameters and the required number of components. A revised expectation-maximization (EM) algorithm is also proposed as a pre-step for DPGMM. Kullback-Leibler Divergence (KL-divergence) is utilized as an error metric to examine the amount of inaccuracy of the EM algorithm and DPGMM when modeling the experimental probability density functions (PDFs). DPGMM and EM algorithm are implemented over the measurements taken at frequencies between 240 GHz and 300 GHz. By comparing the results of the DPGMM and EM algorithms for the measurement datasets, we demonstrate how well the DPGMM fits the target distribution. It is shown that the proposed DPGMM can accurately describe the various THz channels as well as the EM algorithm, and its flexibility allows it to represent more complex distributions better than the EM algorithm. We also demonstrated that DPGMM can be used to model any wireless channel due to its versatility.