The Rayleigh Fading Channel Prediction via Deep Learning

Algorithm 2

1. The weight matrices , are initialized randomly from 0 to 1. The threshold vectors are initialized to 0. Set the

training goal and the learning rate to a reasonable value, respectively. The intermediate variable is initialized to 1;

2. Input the channel information training set and verification set to train the neural network.

3. For :

4. Calculate the hidden layer , output layer data and cost function of training set and

verification set according to equation (7), respectively;

5. If ;

6. Quit

7. Else do:

8. ;

9. According to (8), calculate the gradient of the output layer weight matrix and threshold vector , respectively;

10. According to (9), calculate the gradient of hidden layer weight matrix and threshold vector , respectively;

11. Update the weight matrix of hidden layer and output layer , and the threshold vector ;

12. End for

13. Input the channel information test set , and calculate the NMSE according to (16)