Aero5GBS—Deep Learning-Empowered Ground Users Handover in Aerial 5G and Beyond Systems

Queiroz, Anderson Apolônio Lira; Barbosa, Maria Katarine Santana; Dias, Kelvin Lopes

doi:10.1109/access.2023.3328531

Cited by 5 publications

(2 citation statements)

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“…The use of UAVs in wireless networks has been a topic of significant interest, with potential applications as aerial base stations (BSs) in 5G networks [12]- [14], mobile edgecloud computing systems [15]- [17], scalable nodes in ad hoc networks [18]- [20], and data collection and surveillance [21]- [23]. Key challenges in this field include optimizing UAV placement, trajectory control, and maintaining quality of service (QoS) for ground users.…”

Section: Related Workmentioning

confidence: 99%

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Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Wang

Deng

et al. 2022

IEEE Trans. Commun.

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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.

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Section: Related Workmentioning

confidence: 99%

“…end while 31: end while percentile data rates Ĉ(t). The Q table is then updated using equation (14). At last, the ϵ value is decayed and the position of the UAV BS is updated.…”

mentioning

confidence: 99%