  Terahertz communication is one of the most promising wireless communication
technologies, due to its capability to provide high bitrates. THz frequencies
suffer however from high signal attenuation and signal degradation, which makes
the THz channel modeling and estimation fundamentally hard. On the other hand,
channel estimation of THz transmission system is critical for THz systems to be
practically adopted in wireless communications. We consider the problem of
channel modeling with deterministic channel propagation and the related
physical characteristics of THz bands, and study the effectiveness of various
machine learning algorithms to estimate the channel. We apply different machine
learning algorithms for channel estimation, including neural networks (NN),
logistic regression (LR), and projected gradient ascent (PGA). Numerical
results show that PGA algorithm yields the most promising performance at SNR=0
dB with NMSE of -12.8 dB.
