# Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networks

arXiv: https://arxiv.org/abs/2312.14247 · HTML: https://mlsanity.com/paper/2312.14247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 21 Dec 2023.

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

- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [5G New Radio for Public Safety Mission Critical Communications](https://mlsanity.com/paper/2103.02434/) (2021)
- [Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance](https://mlsanity.com/paper/2312.06225/) (2023)
- [A Multi-Layer Feedback System Approach to Resilient Connectivity of Remotely Deployed Mobile Internet of Things](https://mlsanity.com/paper/1805.05824/) (2018)
- [User Scheduling and Trajectory Optimization for Energy-Efficient IRS-UAV Networks with SWIPT](https://mlsanity.com/paper/2209.05639/) (2022)
- [Joint position and trajectory optimization of flying base station in 5G cellular networks, based on users' current and predicted location](https://mlsanity.com/paper/2202.03832/) (2022)
- [Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users](https://mlsanity.com/paper/2203.06287/) (2022)
- [3D Uplink Channel Modeling of UAV-based mmWave Fronthaul Links for Future Small Cell Networks](https://mlsanity.com/paper/2203.14293/) (2022)
- [Proactive and Resilient UAV Orchestration for QoS Driven Connectivity and Coverage of Ground Users](https://mlsanity.com/paper/2209.04496/) (2022)
- [Zero Touch Coordinated UAV Network Formation for 360deg Views of a Moving Ground Target in Remote VR Applications](https://mlsanity.com/paper/2211.02833/) (2022)

## Built on by (most cited first, 2 of 2)

- [AI-Native Integrated Sensing and Communications for Self-Organizing Wireless Networks: Architectures, Learning Paradigms, and System-Level Design](https://mlsanity.com/paper/2601.02398/) (2025)
- [Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB](https://mlsanity.com/paper/2608.23824/) (2026)

## Abstract (arXiv)

The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.