  A low-complexity neural network based approach for channel estimation was
proposed recently, where assumptions on the channel model were incorporated
into the design procedure of the estimator. Instead of using data from a
measurement campaign as done in previous work, we evaluate the performance of
the convolutional neural network (CNN) based channel estimator by using a
reproducible mmWave environment of the DeepMIMO dataset. We further propose a
neural network based predictor which is derived by starting from the linear
minimum mean squared error (LMMSE) predictor. We start by deriving a weighted
sum of LMMSE predictors which is motivated by the structure of the optimal MMSE
predictor. This predictor provides an initialization (weight matrices, biases
and activation function) to a feed-forward neural network based predictor. With
a properly learned neural network, we show that it is possible to easily
outperform the LMMSE predictor based on the Jakes assumption of the underlying
Doppler spectrum in an reproducible indoor scenario of the DeepMIMO dataset.
