Reinforcement learning models for scheduling in wireless networks

Authors:
Kok-Lim Alvin Yau;Kae Hsiang Kwong;Chong Shen
Affiliations:
Faculty of Science and Technology, Sunway University, Selangor, Malaysia 46150;R&D Department, Recovision, Selangor, Malaysia 47650;College of Information Science and Technology, Hainan University, Haikou, China 570228
Venue:
Frontiers of Computer Science: Selected Publications from Chinese Universities
Year:
2013

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Abstract

The dynamicity of available resources and network conditions, such as channel capacity and traffic characteristics, have posed major challenges to scheduling in wireless networks. Reinforcement learning (RL) enables wireless nodes to observe their respective operating environment, learn, and make optimal or near-optimal scheduling decisions. Learning, which is the main intrinsic characteristic of RL, enables wireless nodes to adapt to most forms of dynamicity in the operating environment as time goes by. This paper presents an extensive review on the application of the traditional and enhanced RL approaches to various types of scheduling schemes, namely packet, sleep-wake and task schedulers, in wireless networks, as well as the advantages and performance enhancements brought about by RL. Additionally, it presents how various challenges associated with scheduling schemes have been approached using RL. Finally, we discuss various open issues related to RL-based scheduling schemes in wireless networks in order to explore new research directions in this area. Discussions in this paper are presented in a tutorial manner in order to establish a foundation for further research in this field.