Modeling and Analysis of sub-Terahertz Communication Channel via Mixture of Gamma Distribution

TL;DR

This paper introduces a Gamma mixture-based statistical model for THz wireless channels, providing a more accurate characterization of the propagation environment essential for high-speed, reliable communication system design.

Contribution

The work develops a Gamma mixture model fitted with EM algorithm for THz channels, improving accuracy over traditional methods in capturing channel variability.

Findings

Gamma mixture model fits measurement data well

Model provides more precise channel parameters

Outperforms single distribution models in accuracy

Abstract

With the recent developments on opening the terahertz (THz) spectrum for experimental purposes by the Federal Communications Commission, transceivers operating in the range of 0.1THz-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation of the high-speed and high-reliability THz band communication systems should start with providing extensive knowledge in regards to the propagation channel characteristics. Considering the huge bandwidth and the rapid changes in the characteristics of THz wireless channels, ray tracing and one-shot statistical modeling are not adequate to define an accurate channel model. In this work, we propose Gamma mixture-based channel modeling for the THz band via the expectation-maximization (EM) algorithm. First, maximum likelihood estimation (MLE) is applied to characterize the Gamma mixture model parameters, and then EM algorithm is used to compute MLEs of the unknown parameters of the measurement data. The accuracy of the proposed model is investigated by using the Weighted relative mean difference (WMRD) error metrics, Kullback-Leibler (KL)-divergence, and Kolmogorov-Smirnov test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wideband, using the mixture models allows for determining channel parameters more precisely.