Towards 5G: a reinforcement learning-based scheduling solution for data traffic management
Authors
Comşa, Ioan-SorinZhang, Sijing
Aydin, Mehmet Emin
Kuonen, Pierre
Lu, Yao
Trestian, Ramona
Ghinea, Gheorghiţă
Affiliation
Brunel UniversityUniversity of Bedfordshire
University of the West of England
University of Applied Sciences of Western Switzerland
University of Fribourg
Middlesex University
Issue Date
2018-08-06Subjects
5G mobile communicationpacket scheduling
optimisation
neural networks
radio resource management
reinforcement Learning
G420 Networks and Communications
Metadata
Show full item recordAbstract
Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher Quality of Service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the Reinforcement Learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.Citation
Comsa I, Zhang S, Aydin M, Kuonen P, Lu Y, Trestian R, Ghinea G (2018) 'Towards 5G: a reinforcement learning-based scheduling solution for data traffic management', IEEE Transactions on Network and Service Management, 15 (4), pp.1661-1675.Publisher
IEEEAdditional Links
https://ieeexplore.ieee.org/document/8425580/Type
ArticleLanguage
enISSN
1932-4537EISSN
1932-4537ae974a485f413a2113503eed53cd6c53
10.1109/TNSM.2018.2863563
Scopus Count
Collections
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF