Machine learning in Radio resource scheduling

Comsa, Ioan-Sorin, Zhang, Sijing, Aydin, Mehmet Emin, Kuonen, Pierre, Trestian, Ramona ORCID logoORCID: https://orcid.org/0000-0003-3315-3081 and Ghinea, Gheorghita (2019) Machine learning in Radio resource scheduling. In: Next-generation wireless networks meet advanced machine learning applications. Comsa, Ioan-Sorin and Trestian, Ramona ORCID logoORCID: https://orcid.org/0000-0003-3315-3081, eds. IGI Global, pp. 24-56. ISBN 9781522574583. [Book Section] (doi:10.4018/978-1-5225-7458-3.ch002)

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.

Item Type: Book Section
Additional Information: ** From Crossref via Jisc Publications Router.
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 26115
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Depositing User: Jisc Publications Router
Date Deposited: 29 Jan 2019 13:55
Last Modified: 29 Jan 2019 15:08
URI: https://eprints.mdx.ac.uk/id/eprint/26115

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