Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning |
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Authors: | Ramkumar Raghu Mahadesh Panju Vaneet Aggarwal Vinod Sharma |
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Institution: | 1.Indian Institute of Science, Karnataka 560012, India; (R.R.); (M.P.); (V.S.);2.School of Industrial Engineering and School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA |
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Abstract: | Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest. |
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Keywords: | multicasting scheduling queuing deep reinforcement learning quality of service power control dynamics tracking multi-timescale stochastic optimization |
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