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Machine Learning in Radio Resource Scheduling

Machine Learning in Radio Resource Scheduling

Ioan-Sorin Comşa, Sijing Zhang, Mehmet Emin Aydin, Pierre Kuonen, Ramona Trestian, Gheorghiţă Ghinea
Copyright: © 2019 |Pages: 33
ISBN13: 9781522574583|ISBN10: 1522574581|ISBN13 Softcover: 9781522585954|EISBN13: 9781522574590
DOI: 10.4018/978-1-5225-7458-3.ch002
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MLA

Comşa, Ioan-Sorin, et al. "Machine Learning in Radio Resource Scheduling." Next-Generation Wireless Networks Meet Advanced Machine Learning Applications, edited by Ioan-Sorin Comşa and Ramona Trestian, IGI Global, 2019, pp. 24-56. https://doi.org/10.4018/978-1-5225-7458-3.ch002

APA

Comşa, I., Zhang, S., Aydin, M. E., Kuonen, P., Trestian, R., & Ghinea, G. (2019). Machine Learning in Radio Resource Scheduling. In I. Comşa & R. Trestian (Eds.), Next-Generation Wireless Networks Meet Advanced Machine Learning Applications (pp. 24-56). IGI Global. https://doi.org/10.4018/978-1-5225-7458-3.ch002

Chicago

Comşa, Ioan-Sorin, et al. "Machine Learning in Radio Resource Scheduling." In Next-Generation Wireless Networks Meet Advanced Machine Learning Applications, edited by Ioan-Sorin Comşa and Ramona Trestian, 24-56. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7458-3.ch002

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Abstract

In access networks, the radio resource management is designed to deal with the system capacity maximization while the quality of service (QoS) requirements need be satisfied for different types of applications. In particular, the radio resource scheduling aims to allocate users' data packets in frequency domain at each predefined transmission time intervals (TTIs), time windows used to trigger the user requests and to respond them accordingly. At each TTI, the scheduling procedure is conducted based on a scheduling rule that aims to focus only on particular scheduling objective such as fairness, delay, packet loss, or throughput requirements. The purpose of this chapter is to formulate and solve an aggregate optimization problem that selects at each TTI the most convenient scheduling rule in order to maximize the satisfaction of all scheduling objectives concomitantly TTI-by-TTI. The use of reinforcement learning is proposed to solve such complex multi-objective optimization problem and to ease the decision making on which scheduling rule should be applied at each TTI.

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