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

Comsa, Ioan Sorin; Zhang, Sijing; Aydin, Mehmet; Chen, Jianping; Kuonen, Pierre; Wagen, Jean Frederic

Abstract

© 2014 IEEE. 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.

Citation

Comsa, I. S., Zhang, S., Aydin, M., Chen, J., Kuonen, P., & Wagen, J. F. (2014). Adaptive proportional fair parameterization based LTE scheduling using continuous actor-critic reinforcement learning. In 2014 IEEE Global Communications Conference (4387-4393). https://doi.org/10.1109/GLOCOM.2014.7037498

• Conference Name: 2014 IEEE Global Communications Conference, GLOBECOM 2014
• Conference Location: Austin, USA
• Start Date: Dec 8, 2014
• End Date: Dec 12, 2014
• Publication Date: Feb 9, 2014
• Deposit Date: Jun 8, 2015
• Publicly Available Date: Feb 10, 2016
• Peer Reviewed: Peer Reviewed
• Pages: 4387-4393
• Book Title: 2014 IEEE Global Communications Conference
• DOI: https://doi.org/10.1109/GLOCOM.2014.7037498
• Keywords: long term evolution, approximation theory, learning (artificial intelligence), learning automata, multilayer perceptrons, optimisation, telecommunication computing, telecommunication scheduling
• Public URL: https://uwe-repository.worktribe.com/output/807022
• Publisher URL: http://dx.doi.org/10.1109/GLOCOM.2014.7037498
• Additional Information: Additional Information : © © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; Title of Conference or Conference Proceedings : 2014 IEEE Global Communications Conference (GLOBECOM)