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.

翻译：第五代（5G）网络的到来为提升连接能力开辟了新途径，尤其在偏远地区或灾区的复杂环境中。无人机（UAV）凭借其通用性，在通过5G集成接入与回传（IAB）特性改善网络性能方面展现出重要作用。然而，现有无人机辅助网络增强方法在动态适应变化用户位置与网络需求方面存在局限性。本文提出一种创新方法，利用深度强化学习（DRL）实时优化无人机部署，动态适应网络条件与用户需求的变化。本方法聚焦于前传链路与回传链路的复杂平衡——这一常被现有方案忽视的关键环节。该研究的独特贡献在于：使无人机能够自主定位，既保障对地面用户的稳定连接，又能无缝集成至核心网络基础设施。通过多种仿真场景，我们验证了该方法如何有效应对上述挑战，提升关键区域的覆盖范围与网络性能。本研究填补了无人机辅助5G网络领域的显著空白，为未来移动网络提供了可扩展的自适应解决方案。