This is simulation code for cognitive capacity harvested network (CCHN) .
In the network, there is one PU, one SU, and a set of CR-routers.
The goal is SU need to learn the power control model of PU with the help of CR-routers.
Finally, SU can make the perfect power-decision with the trained DQN algorithm,
while meeting the specific QoS of PU and SU.
The DQN network model:
input layer: 10 (states: the sensed power in CR-routes)
hidden layer 1: 256
relu()
hidden layer 2: 256
relu()
hidden layer 3: 512
tanh()
output layer: 8 (actions: the power mode for SU)