Deep Reinforcement Learning Based Placement for Integrated Access
  Backhauling in UAV-Assisted Wireless Networks
Yuhui Wang Junaid Farooq
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.