학술논문

Reinforcement-Learning-Based Optimization on Energy Efficiency in UAV Networks for IoT
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 10(3):2767-2775 Feb, 2023
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Resource management
Optimization
Autonomous aerial vehicles
Trajectory
NOMA
Wireless communication
Programming
Energy efficiency (EE)
Internet of Things (IoT)
nonorthogonal multiplex access (NOMA)
power allocation optimization
unmanned aerial vehicles (UAVs)
Language
ISSN
2327-4662
2372-2541
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.