An Uplink Throughput Optimization Scheme for UAV-Enabled Urban Emergency Communications

Hu, Bo; Wang, Lei; Chen, Shanzhi; Cui, Jian; Chen, Liang‐Yu

doi:10.1109/jiot.2021.3103892

Cited by 49 publications

(18 citation statements)

References 37 publications

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“…In this subsection, we present a formulation for quantifying communication fairness. Prior studies have measured fairness in terms of the throughput of each UE [36], [37], but this metric is not appropriate for our model because no UE is always in the UAV's coverage area. Therefore, in this paper, communication fairness is determined from the amount of data transmitted by a UE when the UAV makes one round of its trajectory.…”

Section: B Formulation Of the Amount Of Transmitted Datamentioning

confidence: 99%

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Mitsui

Nishiyama

2023

IEEE Access

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The use of aerial base stations on unmanned aerial vehicles (UAVs) is a notable solution for providing disaster victims with communication services because they can be quickly deployed immediately after a disaster. In this study, we investigate how a single circling UAV with an aerial base station can be used for collecting uplink information from users on the ground. In this system, users within the coverage area of an aerial base station share the available system bandwidth. Consequently, since a UAV's flying speed is constant, a fairness issue becomes significant among ground users distributed over the service area when their spatial density differs largely from place to place. To solve this fairness issue, we focus on a speed-control technique for the UAV. We begin by formulating the amount of transmitted data from each user equipment (UE) as a function of the communication time, during which the user is within the coverage of the aerial base station, and which depends on the speed of the UAV. The objective function can be defined as a maximization of the minimum amount of data transmitted by each UE. Then, we develop the proposed speed-control technique by analyzing the quantitative relationship between the UAV's flying speed and the amount of data transmitted by each UE. Finally, we use computer simulations to demonstrate the effectiveness of our technique.INDEX TERMS unmanned aerial vehicle (UAV), speed control, fairness, disaster Many communication systems using UAV swarms to cover large areas have been proposed [23], [24], such as the information collection system in [25], in which UAVs fly in a circular trajectory. Timeliness and fairness are two important

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Section: B Formulation Of the Amount Of Transmitted Datamentioning

confidence: 99%

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Mitsui

Nishiyama

2023

IEEE Access

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“…To ensure the QoS and reliability of multi-UAV networks, authors in [38] propose an uplink throughput optimization scheme for UAV-enabled urban emergency communications. Their approach uses a joint power allocation and UAV altitude control scheme to maximize the uplink throughput of the UAV network.…”

Section: Qos Aware Uav Deploymentmentioning

confidence: 99%

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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“…The disastrous aftermath of floods has reinforced the need for complementary access networks for critical recovery communications from the flood-stricken area to unaffected areas [21]. In the event where many conventional base stations spanning several cities are damaged, unlicensed, high-bandwidth wireless access technology is required for complementary post-flood recovery communications, to expedite communications to the floodstricken area.…”

Section: Contributionsmentioning

confidence: 99%

“…Data transmission from a HAP involves the control of the HAP flight trajectory and the transmit power of the HAP antenna [18][19][20]. Recently, the deployment of unmanned aerial vehicles (UAVs) during natural disasters has become increasingly prevalent for forwarding information from the affected area to undamaged ground base stations during rescue operations [21]. UAVs generally have limited onboard energy [22].…”

Section: Introductionmentioning

confidence: 99%

Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity

et al. 2022

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The deployment of unmanned aerial vehicles (UAVs) for free space optical communications is an attractive solution for forwarding the vital health information of victims from a flood-stricken area to neighboring ground base stations during rescue operations. A critical challenge to this is maintaining an acceptable signal quality between the ground base station and UAV-based free space optics relay. This is largely unattainable due to rapid UAV propeller and body movements, which result in fluctuations in the beam alignment and frequent link failures. To address this issue, linearly polarized Laguerre–Gaussian modes were leveraged for spatial mode diversity to prevent link failures over a 400 m link. Spatial mode diversity successfully improved the bit error rate by 38% to 55%. This was due to a 10% to 19% increase in the predominant mode power from spatial mode diversity. The time-varying channel matrix indicated the presence of nonlinear deterministic chaos. This opens up new possibilities for research on state-space reconstruction of the channel matrix.

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An Uplink Throughput Optimization Scheme for UAV-Enabled Urban Emergency Communications

Cited by 49 publications

References 37 publications

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

Speed-Control Technique for Achieving Fair Uplink Communications With a UAV-Aided Flying Base Station

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

Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity

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