A Reinforcement Learning Based Low-Delay Scheduling With Adaptive Transmission

Abstract

As modern communication systems become indispensable, the requirements for communication systems such as delay and power get more stringent. In this paper, we adopt a Reinforcement Learning (RL) based approach to obtain the optimal trade-off between delay and power consumption for a given power constraint in a communication system whose conditions (e.g., channel conditions, traffic arrival rates) can change over time. To this end, we first formulate this problem as an infinite-horizon Markov Decision Process (MDP) and then Q-learning is adopted to solve this problem. To handle the given power constraint, we apply the Lagrange multiplier method that transforms a constrained optimization problem into a non-constrained problem. Finally, via simulation, we show that Q-learning achieves the optimal policy.