# 5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul

arXiv: https://arxiv.org/abs/2202.02006 · HTML: https://mlsanity.com/paper/2202.02006/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020)
- [Artificial Intelligence for UAV-enabled Wireless Networks: A Survey](https://mlsanity.com/paper/2009.11522/) (2020)
- [Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique](https://mlsanity.com/paper/1901.10832/) (2019)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [On Integrated Access and Backhaul Networks: Current Status and Potentials](https://mlsanity.com/paper/2006.14216/) (2020)
- [5G New Radio for Public Safety Mission Critical Communications](https://mlsanity.com/paper/2103.02434/) (2021)
- [Backhaul-aware Robust 3D Drone Placement in 5G+ Wireless Networks](https://mlsanity.com/paper/1702.08395/) (2017)
- [On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach](https://mlsanity.com/paper/2102.07252/) (2021)
- [Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization](https://mlsanity.com/paper/1911.01506/) (2019)
- [Interference Management in UAV-assisted Integrated Access and Backhaul Cellular Networks](https://mlsanity.com/paper/1907.02585/) (2019)
- [Deployable Networks for Public Safety in 5G and Beyond: A Coverage and Interference Study](https://mlsanity.com/paper/2109.00228/) (2021)
- [Towards Providing Connectivity When and Where It Counts: An Overview of Deployable 5G Networks](https://mlsanity.com/paper/2110.05360/) (2021)
- [Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning](https://mlsanity.com/paper/2112.07313/) (2021)

## Abstract (arXiv)

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.