# Deep Reinforcement Learning for Scheduling in Cellular Networks

arXiv: https://arxiv.org/abs/1905.05914 · HTML: https://mlsanity.com/paper/1905.05914/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 7 of 7)

- [WAIR-D: Wireless AI Research Dataset](https://mlsanity.com/paper/2212.02159/) (2022)
- [Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks](https://mlsanity.com/paper/2209.13532/) (2022)
- [A Reinforcement Learning Approach for Scheduling in mmWave Networks](https://mlsanity.com/paper/2108.00548/) (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)
- [Scheduling the NASA Deep Space Network with Deep Reinforcement Learning](https://mlsanity.com/paper/2102.05167/) (2021)
- [Deep Reinforcement Learning for Uplink Multi-Carrier Non-Orthogonal Multiple Access Resource Allocation Using Buffer State Information](https://mlsanity.com/paper/2208.14689/) (2022)

## Abstract (arXiv)

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all modules in the conventional systems, which is often lack of efficiency and robustness due to their ignoring of expert knowledge. In this paper, we take deep reinforcement learning (DRL) based scheduling as an example to investigate how expert knowledge can help with AI module in cellular networks. A simulation platform, which has considered link adaption, feedback and other practical mechanisms, is developed to facilitate the investigation. Besides the traditional way, which is learning directly from the environment, for training DRL agent, we propose two novel methods, i.e., learning from a dual AI module and learning from the expert solution. The results show that, for the considering scheduling problem, DRL training procedure can be improved on both performance and convergence speed by involving the expert knowledge. Hence, instead of replacing conventional scheduling module in the system, adding a newly introduced AI module, which is capable to interact with the conventional module and provide more flexibility, is a more feasible solution.