# Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2112.07313 · HTML: https://mlsanity.com/paper/2112.07313/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [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)
- [Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization](https://mlsanity.com/paper/1911.01506/) (2019)

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

- [5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul](https://mlsanity.com/paper/2202.02006/) (2022)

## Abstract (arXiv)

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.