Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
This addresses scheduling efficiency in cellular networks, but it is incremental as it applies existing DRL methods to a specific domain problem.
The paper tackles the downlink packet scheduling problem in cellular networks by jointly optimizing throughput, fairness, and packet drop rate, proposing a deep reinforcement learning framework with A2C that outperforms baseline algorithms and achieves performance similar to genie-aided methods without using future information.
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.