  Terahertz communication is one of the most promising wireless communication
technologies for 6G generation and beyond. For THz systems to be practically
adopted, channel estimation is one of the key issues. We consider the problem
of channel modeling and estimation with deterministic channel propagation and
the related physical characteristics of THz bands, and benchmark various
machine learning algorithms to estimate THz channel, 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.
