n a deep$-$learning$-$based channel estimation system for OFDM, the training dataset includes channel conditions with low Doppler shifts. However, in a real$-$time scenario, the system encounters channels with high Doppler shifts, leading to poor performance. Which solution would best adapt the model to this environment?

$(2$ Points$)$

Note: Choose the best possible option from given options

Apply real$-$time fine$-$tuning of the DNN using online data collected from the current environment.

Use a least$-$squares $($LS$)$ method as a backup estimator during high Doppler conditions.

Train the model with augmented data that simulates high Doppler shift scenarios.

Increase the number of neurons in the hidden layers of the DNN$.$