Comparing Capacity Gains of Static and UAV-Based Millimeter-Wave Relays in Clustered Deployments

Gapeyenko, Margarita; Petrov, Vitaly; Moltchanov, Dmitri; Yeh, Shu Ping; Himayat, Nageen; Andreev, Sergey

doi:10.1109/iccworkshops49005.2020.9145216

2020 IEEE International Conference on Communications Workshops (ICC Workshops) 2020

DOI: 10.1109/iccworkshops49005.2020.9145216

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Comparing Capacity Gains of Static and UAV-Based Millimeter-Wave Relays in Clustered Deployments

Margarita Gapeyenko

Vitaly Petrov

Dmitri Moltchanov

et al.

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Cited by 3 publications

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Effects of Vertical Fluctuations on Air-to-Ground mmWave UAV Communications

Xie

et al. 2022

IEEE Wireless Commun. Lett.

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Millimeter wave (mmWave)-based unmanned aerial vehicle (UAV) communication is a promising candidate for future communications due to its flexibility and sufficient bandwidth. However, random fluctuations in the position of hovering UAVs will lead to random variations in the blockage and signal-to-noise ratio (SNR) of the UAV-user link, thus affecting the quality of service (QoS) of the system. To assess the impact of UAV position fluctuations on the QoS of air-to-ground mmWave UAV communications, this paper develops a tractable analytical model that jointly captures the features of three-dimensional (3D) position fluctuations of hovering UAVs and blockages of mmWave (including static, dynamic, and self-blockages). With this model, we derive the closed-form expressions for reliable service probability respective to blockage probability of UAV-user links, and coverage probability respective to SNR, respectively. The results indicate that the greater the position fluctuations of UAVs, the lower the reliable service probability and coverage probability. The degradation of these two evaluation metrics confirms that the performance of air-to-ground mmWave UAV systems largely depends on the UAV position fluctuations, and the stronger the fluctuation, the worse the QoS. Finally, Monte Carlo simulations demonstrate the above results and show UAVs' optimal location to maximize the reliable service and coverage probability, respectively.

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Effects of Vertical Fluctuations on Air-to-Ground mmWave UAV Communications

Xie

et al. 2022

IEEE Wireless Commun. Lett.

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UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

Arani,

Hu,

Zhu

2024

IEEE Open J. Veh. Technol.

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HAP-assisted multi-aerial base station deployment for capacity enhancement via federated deep reinforcement learning

Liu,

He,

et al. 2023

J Cloud Comp

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Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are widely employed in cloud computing, disaster relief, and various applications. How to quickly and efficiently deploy multi-AeBSs for higher capacity gain has become a key research issue. In this paper, we address the 3D deployment optimization problem of multi-AeBSs with the objective of maximizing system capacity. To overcome communication overhead and privacy challenges in multi-agent deep reinforcement learning (MADRL), we propose a federated deep deterministic policy gradient (Fed-DDPG) algorithm for the multi-AeBS deployment decision. Specifically, a high-altitude platform (HAP)-assisted multi-AeBS deployment architecture is designed, in which low-altitude AeBS act as the local nodes to train its own deployment decision model, while the HAP acts as the global node to aggregate the weights of local models. In this architecture, AeBSs do not exchange raw data, addressing data privacy concerns and reducing communication overhead. Simulation results show that the proposed algorithm outperforms fully distributed MADRL algorithms and closely approximates the performance of multi-agent deep deterministic policy gradient (MADDPG), which requires global information during training, but with less training time.

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Comparing Capacity Gains of Static and UAV-Based Millimeter-Wave Relays in Clustered Deployments

Cited by 3 publications

References 17 publications

Effects of Vertical Fluctuations on Air-to-Ground mmWave UAV Communications

Effects of Vertical Fluctuations on Air-to-Ground mmWave UAV Communications

UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

HAP-assisted multi-aerial base station deployment for capacity enhancement via federated deep reinforcement learning

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