Deep Reinforcement Learning Based Power Control for Wireless Multicast Systems

We consider a multicast scheme recently proposed for a wireless downlink 1. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network. We show that optimal power control can be learnt for reasonably large systems via this approach. The average power constraint is ensured via a Lagrange multiplier, which is also learnt. In the longer version of the paper 2, we also demonstrate that our learning algorithm can be modified to allow the optimal control to track the time varying system statistics