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

Transceivers operating in the range of 0.1 terahertz (THz)-10THz, which are known as THz bands, will enable ultra-high throughput wireless communications. However, actual implementation of the high-speed and reliable THz band communication systems should start with providing extensive knowledge in regards to the propagation channel characteristics. Considering a 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 a proper channel model. In this work, we propose Gamma mixture based channel modeling for the THz band via 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 performance is investigated by using the Weighted relative mean difference (WMRD) error metrics, Kullback-Leibler (KL)-divergence, and Kolmogorov-Smirnov (KS) test to show the difference between the proposed model and the actual probability density functions (PDFs) that are obtained via the designed test environment. To efficiently evaluate the performance of the proposed method in more realistic scenarios, all the analysis is done by examining measurement data from a measurement campaign in the 240GHz to 300GHz frequency range, using a well-isolated anechoic chamber. According to WMRD error metrics, KL-divergence, and KS test results, PDFs generated by the mixture of Gamma distributions fit to the actual histogram of the measurement data. It is shown that instead of taking pseudo-average characteristics of sub-bands in the wide band, using the mixture models allows determining the channel parameters more precisely.