Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT

Dan Deng; Junxia Li; Rutvij H. Jhaveri; Prayag Tiwari; Muhammad Fazal Ijaz; Jiangtao Ou; Chengyuan Fan

doi:10.1109/JIOT.2022.3214860

Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT

Dan Deng, Junxia Li, Rutvij H. Jhaveri, Prayag Tiwari, Muhammad Fazal Ijaz, Jiangtao Ou, Chengyuan Fan

Research output: Contribution to journal › Article › Scientific › peer-review

9 Citations (Scopus)

Abstract

The combination of nonorthogonal multiplex access and unmanned aerial vehicles (UAVs) can improve the energy efficiency (EE) for Internet of Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. First, with a given UAV trajectory, the Dinkelbach method-based fractional programming is adopted to obtain the optimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithm is adopted in the second stage to update the system parameters. Finally, reinforcement-learning-based optimization is introduced to obtain the best UAV trajectory.

Original language	English
Pages (from-to)	2767-2775
Number of pages	9
Journal	IEEE Internet of Things Journal
Volume	10
Issue number	3
Early online date	2022
DOIs	https://doi.org/10.1109/JIOT.2022.3214860
Publication status	Published - 1 Feb 2023
MoE publication type	A1 Journal article-refereed

Keywords

Autonomous aerial vehicles
energy efficiency
Internet-of-Things (IoT)
NOMA
Optimization
power allocation optimization
Programming
Resource management
Trajectory
Unmanned Aerial Vehicles
Wireless communication

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/JIOT.2022.3214860

Cite this

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title = "Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT",

abstract = "The combination of nonorthogonal multiplex access and unmanned aerial vehicles (UAVs) can improve the energy efficiency (EE) for Internet of Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. First, with a given UAV trajectory, the Dinkelbach method-based fractional programming is adopted to obtain the optimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithm is adopted in the second stage to update the system parameters. Finally, reinforcement-learning-based optimization is introduced to obtain the best UAV trajectory.",

keywords = "Autonomous aerial vehicles, energy efficiency, Internet-of-Things (IoT), NOMA, Optimization, power allocation optimization, Programming, Resource management, Trajectory, Unmanned Aerial Vehicles, Wireless communication",

author = "Dan Deng and Junxia Li and Jhaveri, {Rutvij H.} and Prayag Tiwari and Ijaz, {Muhammad Fazal} and Jiangtao Ou and Chengyuan Fan",

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AU - Jhaveri, Rutvij H.

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AU - Ijaz, Muhammad Fazal

AU - Ou, Jiangtao

AU - Fan, Chengyuan

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AB - The combination of nonorthogonal multiplex access and unmanned aerial vehicles (UAVs) can improve the energy efficiency (EE) for Internet of Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. First, with a given UAV trajectory, the Dinkelbach method-based fractional programming is adopted to obtain the optimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithm is adopted in the second stage to update the system parameters. Finally, reinforcement-learning-based optimization is introduced to obtain the best UAV trajectory.

KW - Autonomous aerial vehicles

KW - energy efficiency

KW - Internet-of-Things (IoT)

KW - NOMA

KW - Optimization

KW - power allocation optimization

KW - Programming

KW - Resource management

KW - Trajectory

KW - Unmanned Aerial Vehicles

KW - Wireless communication

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Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT

Abstract

Keywords

UN SDGs

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