Deep Reinforcement Learning Applications For Coexistence in Television Whitespace: A Mini-Review
No Thumbnail Available
Date
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Description
The ever-increasing demand for wireless communication services, coupled with the scarcity
of available radio frequency spectrum, necessitates innovative approaches to spectrum
management. Television White Space (TVWS) and Cognitive radio (CR) technology have
emerged as a pivotal solution, enabling intelligent and dynamic spectrum sharing among
secondary users while respecting the rights of primary, licensed users. However, a notable
challenge to its effective utilization lies in the interference between primary and secondary
users, as well as interference among secondary users themselves. In such networks,
network entities must make local decisions to optimize network performance in the face of
unknown network conditions. Reinforcement learning has effectively been utilised to help
network entities choose the best policies, such as decisions or actions, based on their
states when the state and action spaces are limited. However, in complex and large-scale
networks, the state and action spaces are typically vast. Deep reinforcement learning, a
fusion of reinforcement learning and deep learning, has been created to address these
limitations. This paper explores the coexistence issue and evaluates the use of deep
reinforcement learning (DRL) methods to enhance spectrum sharing in cognitive radio
networks.
Keywords
QA75 Electronic computers. Computer science