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Adaptive proportional fair parameterization based LTE scheduling using continuous actor-critic reinforcement learning

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

Abstract

Maintaining a desired trade-off performance between system throughput maximization and user fairness satisfaction constitutes a problem that is still far from being solved. In LTE systems, different tradeoff levels can be obtained by using a proper parameterization of the Generalized Proportional Fair (GPF) scheduling rule. Our approach is able to find the best parameterization policy that maximizes the system throughput under different fairness constraints imposed by the scheduler state. The proposed method adapts and refines the policy at each Transmission Time Interval (TTI) by using the Multi-Layer Perceptron Neural Network (MLPNN) as a non-linear function approximation between the continuous scheduler state and the optimal GPF parameter(s). The MLPNN function generalization is trained based on Continuous Actor-Critic Learning Automata Reinforcement Learning (CACLA RL). The double GPF parameterization optimization problem is addressed by using CACLA RL with two continuous actions (CACLA-2). Five reinforcement learning algorithms as simple parameterization techniques are compared against the novel technology. Simulation results indicate that CACLA-2 performs much better than any of other candidates that adjust only one scheduling parameter such as CACLA-1. CACLA-2 outperforms CACLA-1 by reducing the percentage of TTIs when the system is considered unfair. Being able to attenuate the fluctuations of the obtained policy, CACLA-2 achieves enhanced throughput gain when severe changes in the scheduling environment occur, maintaining in the same time the fairness optimality condition.

Publication Information

Output type

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

Original language

English

Pages from-to (Number of pages)

Pages 4387-4393

Publication milestones

  • Published - 12/02/2015

Publication status

Published - 12/02/2015

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781479935116

External Publication IDs

  • handle.net: 10547/624328
  • Scopus: 84949922965

Host publication title

2014 IEEE Global Communications Conference

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