Cell Traffic Prediction Based on Convolutional Neural Network for Software-Defined Ultra-Dense Visible Light Communication Networks

Algorithm 1

2D convolutional neural network model training process.

	Input: training set historical data:
Output: trained 2D convolutional neural network model
(1)	//Construct training examples
(2)	D ← φ
(3)	While all available time interval T (1  T  N)
(4)	=
(5)	Put the training instance into D
(6)	// is the actual value at time t
(7)	end
(8)	//Training model
(9)	Initialization of all trainable parameters θ
(10)	Repeat
(11)	Randomly select a batch of instances from D
(12)	Use and Adam optimization to find the best θ (the loss value defined in Section 4.2.4))
(13)	until meet the stop condition (early stopping or completed the training batch)