# Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1911.05281 · HTML: https://mlsanity.com/paper/1911.05281/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [WAIR-D: Wireless AI Research Dataset](https://mlsanity.com/paper/2212.02159/) (2022)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks](https://mlsanity.com/paper/2103.11542/) (2021)
- [Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information](https://mlsanity.com/paper/2108.12198/) (2021)

## Abstract (arXiv)

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.