# Using Deep Reinforcement Learning for mmWave Real-Time Scheduling

arXiv: https://arxiv.org/abs/2210.01423 · HTML: https://mlsanity.com/paper/2210.01423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [A Reinforcement Learning Approach for Scheduling in mmWave Networks](https://mlsanity.com/paper/2108.00548/) (2021)
- [A Machine Learning Approach to Routing](https://mlsanity.com/paper/1708.03074/) (2017)
- [Integrated mmWave Access and Backhaul in 5G: Bandwidth Partitioning and Downlink Analysis](https://mlsanity.com/paper/1710.06255/) (2017)
- [Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1901.01119/) (2018)
- [5G mmWave Module for ns-3 Network Simulator](https://mlsanity.com/paper/1506.08801/) (2015)
- [Path Selection and Rate Allocation in Self-Backhauled mmWave Networks](https://mlsanity.com/paper/1802.02085/) (2018)

## Built on by (most cited first, 1 of 1)

- [Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems](https://mlsanity.com/paper/2507.00268/) (2025)

## Abstract (arXiv)

We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time slot constraints of typical 5G mmWave networks. AARL can handle a variety of network topologies, network loads, and interference models, it can also adapt to different workloads. We demonstrate the operation of AARL on several topologies: a small topology with 10 links, a moderately-sized mesh with 48 links, and a large topology with 96 links. We show that for each topology, we compare the throughput obtained by AARL to that of a benchmark algorithm called RPMA (Residual Profit Maximizer Algorithm). The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot. In addition, the quality of the scheduling decisions made by AARL outperforms those made by RPMA.