Campus Units
Electrical and Computer Engineering
Document Type
Conference Proceeding
Conference
28th International Conference on Computer Communication and Networks (ICCCN)
Publication Version
Accepted Manuscript
Link to Published Version
https://doi.org/10.1109/ICCCN.2019.8846912
Publication Date
2019
Journal or Book Title
2019 28th International Conference on Computer Communication and Networks (ICCCN)
DOI
10.1109/ICCCN.2019.8846912
Conference Title
28th International Conference on Computer Communication and Networks (ICCCN)
Conference Date
July 29-August 1, 2019
City
Valencia, Spain
Abstract
Energy harvesting communications systems are able to provide high quality communications services using green energy sources. This paper presents an autonomous energy harvesting communications system that is able to adapt to any environment, and optimize its behavior with experience to maximize the valuable received data. The considered system is a point-to-point energy harvesting communications system consisting of a source and a destination, and working in an unknown and uncertain environment. The source is an energy harvesting node capable of harvesting solar energy and storing it in a finite capacity battery. Energy can be harvested, stored, and used from continuous ranges of energy values. Channel gains can take any value within a continuous range. Since exact information about future channel gains and harvested energy is unavailable, an architecture based on actor-critic reinforcement learning is proposed to learn a close-to-optimal transmission power allocation policy. The actor uses a stochastic parameterized policy to select actions at states stochastically. The policy is modeled by a normal distribution with a parameterized mean and standard deviation. The actor uses policy gradient to optimize the policy’s parameters. The critic uses a three layer neural network to approximate the action-value function, and to evaluate the optimized policy. Simulation results evaluate the proposed architecture for actor-critic learning, and shows its ability to improve its performance with experience.
Rights
© 2019 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.
Copyright Owner
IEEE
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Masadeh, Ala'eddin; Wang, Zhengdao; and Kamal, Ahmed E., "An Actor-Critic Reinforcement Learning Approach for Energy Harvesting Communications Systems" (2019). Electrical and Computer Engineering Conference Papers, Posters and Presentations. 105.
https://lib.dr.iastate.edu/ece_conf/105
Comments
This is a manuscript of a proceeding published as Masadeh, Ala'eddin, Zhengdao Wang, and Ahmed E. Kamal. "An Actor-Critic Reinforcement Learning Approach for Energy Harvesting Communications Systems." In 2019 28th International Conference on Computer Communication and Networks (ICCCN). DOI: 10.1109/ICCCN.2019.8846912. Posted with permission.