  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.
