Equalizing service probability in UAV-assisted wireless powered mmWave networks for post-disaster rescue

Jin, Nansen; Gui, Jinsong; Zhou, Xinran

doi:10.1016/j.comnet.2023.109644

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Communication reconstruction is crucial in disaster relief scenarios because road destruction, power interruptions, and the paralysis of terrestrial base stations will isolate the affected area, leading to an "information island" phenomenon. Unmanned aerial vehicles are extensively utilized in disaster scenarios to offer communication coverage for ground user equipment (UE), including affected residents and first responder rescue teams, when ground network infrastructure is disconnected from the power supply or destroyed by sudden disaster events [72][73][74][75]. UAVs employed as aerial communication nodes are essential for disaster management, facilitating communication recovery and information exchange.…”

Section: Unmanned Aerial Vehicles Application In Ntnsmentioning

confidence: 99%

“…Furthermore [73], investigated an aerial communication scheme utilizing multi-UAVs for multi-purpose disaster relief operations, with a focus on device-to-device communication and energy efficiency. Moreover [74], examined the fairness of service provision in a post-disaster millimeter-wave communication system. Researchers [75] highlighted the topological challenges in UAV-assisted communication during post-disaster rescue operations.…”

Section: Unmanned Aerial Vehicles Application In Ntnsmentioning

confidence: 99%

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Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

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Section: Unmanned Aerial Vehicles Application In Ntnsmentioning

confidence: 99%

Section: Unmanned Aerial Vehicles Application In Ntnsmentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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“…UAVs have several advantages, including low life-cycle cost (Lei et al, 2021 ), low personnel risk (Rodriguez-Fernandez et al, 2017 ), long duration of flight (Ge et al, 2022 ; Pasha et al, 2022 ), and maneuverability, size, and speed (Poudel and Moh, 2022 ). These UAVs are increasingly being used in various fields such as tracking targets (Hu et al, 2023 ), agriculture (Liu et al, 2022b ), rescue (Jin et al, 2023 ), and transportation (Li et al, 2021 ) for “Dull, Dirty, Dangerous, and Deep” (4D) missions (Aleksander, 2018 ; Chamola et al, 2021 ). The applications of UAVs are illustrated in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

MW-MADDPG: a meta-learning based decision-making method for collaborative UAV swarm

Zhao,

Wang,

et al. 2023

Front. Neurorobot.

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Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.

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