  Deep learning has demonstrated the important roles in improving the system
performance and reducing computational complexity for $5$G-and-beyond networks.
In this paper, we propose a new channel estimation method with the assistance
of deep learning in order to support the least squares estimation, which is a
low-cost method but having relatively high channel estimation errors. This goal
is achieved by utilizing a MIMO (multiple-input multiple-output) system with a
multi-path channel profile used for simulations in the 5G networks under the
severity of Doppler effects. Numerical results demonstrate the superiority of
the proposed deep learning-assisted channel estimation method over the other
channel estimation methods in previous works in terms of mean square errors.
