Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 060-190241-5093
- Type
- D - Journal article
- DOI
-
10.1109/TNSM.2018.2863563
- Title of journal
- IEEE Transactions on Network and Service Management
- Article number
- -
- First page
- 1661
- Volume
- 15
- Issue
- 4
- ISSN
- 1932-4537
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
http://bura.brunel.ac.uk/handle/2438/17748
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
2 - Software, Systems & Security (SSS)
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work proposes an innovative scheduling framework that integrates ideas from reinforcement leaning and neural networks, which has been shown to outperform conventional scheduling strategies for resource management in 5G access networks. The paper is one of a select few exploring the challenging nature of real-time scheduling in 5G and forms part of Prof. Ghinea’s research harnessing the ability of 5G to deliver mulsemedia content.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
