VLC-Enabled UAV Network for IoT With Co-Channel Interference: Joint Spatial Deployment and Resource Allocation

Uncrewed aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this article, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent (BCD) method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-03-05
DOI
https://doi.org/10.1109/jiot.2025.3546112
Citations
4
Primary Topic
Optical Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
2.92

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article

VLC-Enabled UAV Network for IoT With Co-Channel Interference: Joint Spatial Deployment and Resource Allocation

Fang Yang, Zhu Han, Jian Song, Nan An et al.
4 citations
IEEE Internet of Things Journal
Optical Wireless Communication Technologies
2.92
article

VLC-Enabled UAV Network for IoT With Co-Channel Interference: Joint Spatial Deployment and Resource Allocation

Fang Yang, Zhu Han, Jian Song, Nan An, Jiaji Liu
article en
4 citations

Abstract

Uncrewed aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this article, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent (BCD) method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated.

IEEE Internet of Things JournalVol. 12(12)
University of Houston (US), Tsinghua University (CN)
National Science Foundation, National Key Research and Development Program of China
Openalex Percentile: Top 10%
Optical Wireless Communication Technologies
2.92
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VLC-Enabled UAV Network for IoT With Co-Channel Interference: Joint Spatial Deployment and Resource Allocation — Fang Yang, Zhu Han, et al. · IEEE Internet of Things Journal (2025) | TGRS Research Map | TGRS