Guaranteeing user rates with reinforcement learning in 5G Radio access networks

Comsa, Ioan-Sorin and Zhang, Sijing and Aydin, Mehmet Emin and Kuonen, Pierre and Trestian, Ramona and Ghinea, Gheorghita (2019) Guaranteeing user rates with reinforcement learning in 5G Radio access networks. In: Next-generation wireless networks meet advanced machine learning applications. Comsa, Ioan-Sorin and Trestian, Ramona, eds. IGI Global, pp. 163-198. ISBN 9781522574583

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Abstract

The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectiveness of the proposed techniques for a variety of data rates' requirements and network conditions.

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: 26116
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SWORD Depositor: Jisc Publications Router
Depositing User: Jisc Publications Router
Date Deposited: 29 Jan 2019 15:07
Last Modified: 29 Jan 2019 15:09
URI: http://eprints.mdx.ac.uk/id/eprint/26116

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