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

• The combination of Non-Orthogonal Multiplex Access and Unmanned Aerial Vehicles (UAV) can improve theenergy 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. Firstly, with given UAV trajectory, the Dinkelbach method based fractional programming is adopted to obtain theoptimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithmis adopted in the second stage to update the system parameters. Finally, reinforcement learning based optimization is introducedto obtain the best UAV trajectory. © 2022 IEEE

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Autonomous aerial vehicles

energy efficiency

Internet-of-Things (IoT)

NOMA

Optimization

power allocation optimization

Programming

Resource management

Trajectory

Unmanned Aerial Vehicles

Wireless communication

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)