# Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer

arXiv: https://arxiv.org/abs/2003.11003 · HTML: https://mlsanity.com/paper/2003.11003/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Learning Generalized Wireless MAC Communication Protocols via Abstraction](https://mlsanity.com/paper/2206.06331/) (2022)
- [Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients](https://mlsanity.com/paper/2304.09488/) (2023)
- [Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information](https://mlsanity.com/paper/2108.12198/) (2021)
- [Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care](https://mlsanity.com/paper/2111.06680/) (2021)
- [Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace](https://mlsanity.com/paper/2203.03718/) (2022)
- [Online Frequency Scheduling by Learning Parallel Actions](https://mlsanity.com/paper/2406.05041/) (2024)
- [AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling](https://mlsanity.com/paper/2606.03664/) (2026)
- [RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models](https://mlsanity.com/paper/2610.04296/) (2026)

## Abstract (arXiv)

Network management tools are usually inherited from one generation to another. This was successful since these tools have been kept in check and updated regularly to fit new networking goals and service requirements. Unfortunately, new networking services will render this approach obsolete and handcrafting new tools or upgrading the current ones may lead to complicated systems that will be extremely difficult to maintain and improve. Fortunately, recent advances in AI have provided new promising tools that can help solving many network management problems. Following this interesting trend, the current article presents LEASCH, a deep reinforcement learning model able to solve the radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is developed and trained in a sand-box and then deployed in a 5G network. The experimental results validate the effectiveness of LEASCH compared to conventional baseline methods in many key performance indicators.