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Towards 5G: a reinforcement learning-based scheduling solution for data traffic management

  • Ioan-Sorin Comşa
    ,
  • Sijing Zhang
    ,
  • Mehmet Emin Aydin
    ,
  • Pierre Kuonen
    ,
  • Yao Lu
    ,
  • Ramona Trestian
  • Brunel University London
    ,
  • University of the West of England
    ,
  • University of Applied Sciences Western Switzerland
    ,
  • University of Fribourg
    ,
  • Middlesex University
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

8425580

Pages from-to (Number of pages)

Pages 1661-1675 (15 pages)

Journal (Volume, Issue Number)

IEEE Transactions on Network and Service Management (Volume 15, Issue 4)

Publication milestones

  • Accepted/In press - 22/07/2018
  • Published - 06/08/2018

Publication status

Published - 06/08/2018

External Publication IDs

  • handle.net: 10547/622852
  • Scopus: 85051023360

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