  We present a neural network based predictor which is derived by starting from
the linear minimum mean squared error (LMMSE) predictor and by further making
two key assumptions. With these assumptions, we first derive 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 under the given channel model
assumptions it is possible to easily outperform the LMMSE predictor based on
the Jakes assumption of the underlying Doppler spectrum.
