The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning in Radio Resource Scheduling
|
|
Author(s): Ioan-Sorin Comşa (Brunel University London, UK), Sijing Zhang (University of Bedfordshire, UK), Mehmet Emin Aydin (University of the West of England, UK), Pierre Kuonen (University of Applied Sciences of Western Switzerland, Switzerland), Ramona Trestian (Middlesex University London, UK)and Gheorghiţă Ghinea (Brunel University London, UK)
Copyright: 2019
Pages: 33
Source title:
Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
Source Author(s)/Editor(s): Ioan-Sorin Comşa (Brunel University London, UK)and Ramona Trestian (Middlesex University, UK)
DOI: 10.4018/978-1-5225-7458-3.ch002
Purchase
|
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.
Related Content
|
J. Mangaiyarkkarasi, J. Shanthalakshmi Revathy.
© 2024.
34 pages.
|
|
Gummadi Surya Prakash, W. Chandra, Shilpa Mehta, Rupesh Kumar.
© 2024.
22 pages.
|
|
Duygu Nazan Gençoğlan.
© 2024.
35 pages.
|
|
Smrity Dwivedi.
© 2024.
20 pages.
|
|
Pallavi Sapkale, Shilpa Mehta.
© 2024.
21 pages.
|
|
Pardhu Thottempudi, Vijay Kumar.
© 2024.
43 pages.
|
|
Sathish Kumar Danasegaran, Elizabeth Caroline Britto, S. Dhanasekaran, G. Rajalakshmi, S. Lalithakumari, A. Sivasangari, G. Sathish Kumar.
© 2024.
18 pages.
|
|
|