Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

2019-11-13 · Chen Xu, Jian Wang, Tianhang Yu, Chuili Kong, Yourui Huangfu, Rong Li, Yiqun Ge, Jun Wang

In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic search methods are employed to explore the solution space. A deep reinforcement learning (DRL) framework with A2C algorithm is proposed for the optimization problem. Several methods have been utilized in the framework to improve the sampling and training efficiency and to adapt the algorithm to a specific scheduling problem. Numerical results show that DRL outperforms the baseline algorithm and achieves similar performance as genie-aided methods without using the future information.

📄 PDF Abstract BibTeX arXiv:1911.05281

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Deep Reinforcement LearningFairnessHeuristic Searchreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

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