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Reinforcement learning models for scheduling in wireless networks

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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.

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Author information

Authors and Affiliations

  1. Faculty of Science and Technology, Sunway University, Selangor, 46150, Malaysia

    Kok-Lim Alvin Yau

  2. R&D Department, Recovision, Selangor, 47650, Malaysia

    Kae Hsiang Kwong

  3. College of Information Science and Technology, Hainan University, Haikou, 570228, China

    Chong Shen

Authors
  1. Kok-Lim Alvin Yau
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  2. Kae Hsiang Kwong
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  3. Chong Shen
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Corresponding author

Correspondence to Kok-Lim Alvin Yau.

Additional information

Kok-Lim Alvin Yau has a BE from Universiti Teknologi Petronas, Malaysia, a MSc from National University of Singapore, and a PhD from Victoria University, New Zealand. His research interests are wireless networks and applied artificial intelligence.

Kae Hsiang Kwong has a BE from Jinan University, China and a PhD from University of Strathclyde, UK. His research interests include network infrastructure design, monitoring, and performance optimization.

Chong Shen has a BE from Wuhan University, China, a MPhil from University of Strathclyde, UK and a PhD from Cork Institute of Technology, Ireland. His research interests cover layers 2 and 3 algorithm and protocol.

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Yau, KL.A., Kwong, K.H. & Shen, C. Reinforcement learning models for scheduling in wireless networks. Front. Comput. Sci. 7, 754–766 (2013). https://doi.org/10.1007/s11704-013-2291-3

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