Multiplexing B5G/6G Services over Aerial VLC Networks: A Comprehensive Radio Resource Management Framework
Zarini, Hosein; Gholipoor, Narges; Mili, Mohammad Robat; Rasti, Mehdi; Movaghar, Ali; Choi, Jinho; Chae, Chan Byoung (2024-12-26)
IEEE
26.12.2024
H. Zarini et al., "Multiplexing B5G/6G Services Over Aerial VLC Networks: A Comprehensive Radio Resource Management Framework," in IEEE Internet of Things Journal, vol. 12, no. 9, pp. 11600-11621, 1 May1, 2025, doi: 10.1109/JIOT.2024.3523016.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503101931
https://urn.fi/URN:NBN:fi:oulu-202503101931
Tiivistelmä
Abstract
Downlink transmission of a non-orthogonal visible light communication system, empowered by unmanned aerial vehicles (UAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of UAVs is formulated, with the goal of characterizing a multi-objective trade-off as a weighted sum of the power consumption of each UAV and the perceived quality of experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information, we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multi-agent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each UAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for UAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.
Downlink transmission of a non-orthogonal visible light communication system, empowered by unmanned aerial vehicles (UAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of UAVs is formulated, with the goal of characterizing a multi-objective trade-off as a weighted sum of the power consumption of each UAV and the perceived quality of experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information, we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multi-agent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each UAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for UAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.
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