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

TL;DR

This paper introduces a hierarchical Dirichlet process Gamma mixture model (DPGMM) for statistically characterizing Terahertz (THz) wireless channels, demonstrating its accuracy and flexibility over traditional EM algorithms in modeling complex distributions.

Contribution

The paper proposes a novel DPGMM approach with a revised EM algorithm for THz channel modeling, capable of determining the number of mixture components without prior knowledge.

Findings

DPGMM accurately models THz channels across 240-300 GHz.

DPGMM outperforms EM algorithm in representing complex distributions.

The method is versatile for modeling various wireless channels.

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.