Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

2019/11/13 by Xu Chen, Jian Wang, Xu, Chen +13
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1911.05281

openalex publication_date 2019/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• High-Dimensional Continuous Control Using Generalized Advantage\n Estimation

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