# Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks

arXiv: https://arxiv.org/abs/2103.11542 · HTML: https://mlsanity.com/paper/2103.11542/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Reinforcement Learning for Scheduling in Cellular Networks](https://mlsanity.com/paper/1905.05914/) (2019)
- [Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning](https://mlsanity.com/paper/1911.05281/) (2019)
- [Reinforcement Learning based Multi-Access Control and Battery Prediction with Energy Harvesting in IoT Systems](https://mlsanity.com/paper/1805.05929/) (2018)

## Built on by (most cited first, 2 of 2)

- [WAIR-D: Wireless AI Research Dataset](https://mlsanity.com/paper/2212.02159/) (2022)
- [Distributed Learning for Time-varying Networks: A Scalable Design](https://mlsanity.com/paper/2108.00231/) (2021)

## Abstract (arXiv)

To improve the system performance towards the Shannon limit, advanced radio resource management mechanisms play a fundamental role. In particular, scheduling should receive much attention, because it allocates radio resources among different users in terms of their channel conditions and QoS requirements. The difficulties of scheduling algorithms are the tradeoffs need to be made among multiple objectives, such as throughput, fairness and packet drop rate. We propose a smart scheduling scheme based on deep reinforcement learning (DRL). We not only verify the performance gain achieved, but also provide implementation-friend designs, i.e., a scalable neural network design for the agent and a virtual environment training framework. With the scalable neural network design, the DRL agent can easily handle the cases when the number of active users is time-varying without the need to redesign and retrain the DRL agent. Training the DRL agent in a virtual environment offline first and using it as the initial version in the practical usage helps to prevent the system from suffering from performance and robustness degradation due to the time-consuming training. Through both simulations and field tests, we show that the DRL-based smart scheduling outperforms the conventional scheduling method and can be adopted in practical systems.