Using Markov decision process in cognitive radio networks towards the optimal reward
Said Lakhal, Zouhair Guennoun · 2017
The Learning is an indispensable phase in the cognition cycle of cognitive radio network. It corresponds between the executed actions and the estimated rewards. Based on this phase, the agent learns from past experiences to improve his actions in the next interventions. In the literature, there are several methods that treat the artificial learning. Among them, we cite the reinforcement learning that look for the optimal policy, for ensuring the maximum reward.