# A Reinforcement Learning Approach for Scheduling in mmWave Networks

arXiv: https://arxiv.org/abs/2108.00548 · HTML: https://mlsanity.com/paper/2108.00548/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization](https://mlsanity.com/paper/1709.07080/) (2017)
- [Coverage and Rate Analysis for Millimeter Wave Cellular Networks](https://mlsanity.com/paper/1402.6430/) (2014)
- [Deep Reinforcement Learning for Scheduling in Cellular Networks](https://mlsanity.com/paper/1905.05914/) (2019)
- [Where, When, and How mmWave is Used in 5G and Beyon](https://mlsanity.com/paper/1704.08131/) (2017)
- [Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks](https://mlsanity.com/paper/1805.07743/) (2018)
- [Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications](https://mlsanity.com/paper/1907.12616/) (2019)
- [Optimal Joint Routing and Scheduling in Millimeter-Wave Cellular Networks](https://mlsanity.com/paper/1712.04059/) (2017)

## Built on by (most cited first, 3 of 3)

- [Using Deep Reinforcement Learning for mmWave Real-Time Scheduling](https://mlsanity.com/paper/2210.01423/) (2022)
- [Proactive Resilient Transmission and Scheduling Mechanisms for mmWave Networks](https://mlsanity.com/paper/2211.09307/) (2022)
- [Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks](https://mlsanity.com/paper/2404.16920/) (2024)

## Abstract (arXiv)

We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e.g., in a hostile military environment). To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm, that adapts the information flow through the network, without using knowledge of the link capacities or network topology. Numerical evaluations show that our algorithm can achieve the desired rate even in dynamic environments and it is robust against blockage.