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A novel dynamic Q-learning-based scheduler technique for LTE-advanced technologies using neural networks

  • Ioan-Sorin Comşa
    ,
  • Sijing Zhang
    ,
  • Mehmet Emin Aydin
    ,
  • Pierre Kuonen
    ,
  • Jean–Frédéric Wagen
  • University of Applied Sciences Western Switzerland
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

The tradeoff concept between system capacity and user fairness attracts a big interest in LTE-Advanced resource allocation strategies. By using static threshold values for throughput or fairness, regardless the network conditions, makes the scheduler to be inflexible when different tradeoff levels are required by the system. This paper proposes a novel dynamic neural Q-learning-based scheduling technique that achieves a flexible throughput-fairness tradeoff by offering optimal solutions according to the Channel Quality Indicator (CQI) for different classes of users. The Q-learning algorithm is used to adopt different policies of scheduling rules, at each Transmission Time Interval (TTI). The novel scheduling technique makes use of neural networks in order to estimate proper scheduling rules for different states which have not been explored yet. Simulation results indicate that the novel proposed method outperforms the existing scheduling techniques by maximizing the system throughput when different levels of fairness are required. Moreover, the system achieves a desired throughput-fairness tradeoff and an overall satisfaction for different classes of users.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 31/01/2013

Publication status

Published - 31/01/2013

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781467315654

ISBN (Electronic)

9781467315654

External Publication IDs

  • handle.net: 10547/272039
  • Scopus: 84874337199

Host publication title

nan

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