Actor-Critic Wavelet Neural Network based Scheduler Technique for LTE-Advanced

This work presents an efficient and intelligent resource scheduling strategy for the Long Term Evolution- Advanced (LTE-A) downlink transmission using Reinforcement learning and wavelet neural network. Resource scheduling in LTE-A suffers the problem of uncertainty and accuracy for large scale network. Also the performance of scheduling in conventional methods solely depends upon the scheduling algorithm which was fixed for the entire transmission session. This issue has been addressed and resolved in this paper through Actor-Critic architecture based reinforcement learning to provide the best suited scheduling method out of the rule set for every transmission time interval (TTI) of communication. The actor network will take the decision on scheduling and the critic network will evaluate this decision and update the actor network adaptively through the optimal tuning laws so as to get the desired performance in scheduling. Wavelet neural network(WNN) is derived here by using wavelet function as activation function in place of sigmoid function in conventional neural network to attain better learning capabilities, faster convergence and efficient decision making in scheduling. The actor and critic networks are created through these WNNs and are trained with the LTE parameters dataset. The efficacy of the presented work is evaluated through simulation analysis.