Deep Reinforcement Learning Based Power control for Wireless Multicast Systems

Deep Reinforcement Learning Based Power control for Wireless Multicast Systems Ramkumar Raghu 1, Pratheek Upadhyaya 1, Mahadesh Panju 1, V aneet Aggarwal 1,2, and Vinod Sharma 1 1 Indian Institute of Science, Bangalore, INDIA. Abstract -- We consider a multicast scheme recently proposed for a wireless downlink in [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. Finally, we demonstrate that a slight modification of the learning algorithm allows the optimal control to track the time varying system statistics. I NTRODUCTION Wireless networks are being constantly refined to cater for seamless delivery of huge amount of data to the end users. With increased user generated contents and proliferation of social networking sites, almost 78% of mobile data traffic is expected to be due to mobile videos [2]. Also, the requested traffic for these contents is ridden with redundant requests [3]. Thus, multicasting is a natural way to address these requests. A multicast queue with network coding is studied in [4], [5] with infinite library of files.