Scheduling policies based on dynamic throughput and fairness tradeoff control in LTE-A networks

Com?a, Ioan Sorin; Aydin, Mehmet; Zhang, Sijing; Kuonen, Pierre; Wagen, Jean Frederic; Lu, Yao

Abstract

© 2014 IEEE. In LTE-A cellular networks there is a fundamental trade-off between the cell throughput and fairness levels for preselected users which are sharing the same amount of resources at one transmission time interval (TTI). The static parameterization of the Generalized Proportional Fair (GPF) scheduling rule is not able to maintain a satisfactory level of fairness at each TTI when a very dynamic radio environment is considered. The novelty of the current paper aims to find the optimal policy of GPF parameters in order to respect the fairness criterion. From sustainability reasons, the multi-layer perceptron neural network (MLPNN) is used to map at each TTI the continuous and multidimensional scheduler state into a desired GPF parameter. The MLPNN non-linear function is trained TTI-by-TTI based on the interaction between LTE scheduler and the proposed intelligent controller. The interaction is modeled by using the reinforcement learning (RL) principle in which the LTE scheduler behavior is modeled based on the Markov Decision Process (MDP) property. The continuous actor-critic learning automata (CACLA) RL algorithm is proposed to select at each TTI the continuous and optimal GPF parameter for a given MDP problem. The results indicate that CACLA enhances the convergence speed to the optimal fairness condition when compared with other existing methods by minimizing in the same time the number of TTIs when the scheduler is declared unfair.

Citation

Comşa, I. S., Aydin, M., Zhang, S., Kuonen, P., Wagen, J. F., & Lu, Y. (2014). Scheduling policies based on dynamic throughput and fairness tradeoff control in LTE-A networks. In 39th Annual IEEE Conference on Local Computer Networks (418-421). https://doi.org/10.1109/LCN.2014.6925806

• Conference Name: Proceedings - Conference on Local Computer Networks, LCN
• Conference Location: Edmonton
• Start Date: Sep 8, 2014
• End Date: Sep 11, 2014
• Publication Date: Jan 1, 2014
• Deposit Date: Jun 8, 2015
• Publicly Available Date: Nov 15, 2016
• Peer Reviewed: Peer Reviewed
• Pages: 418-421
• Book Title: 39th Annual IEEE Conference on Local Computer Networks
• DOI: https://doi.org/10.1109/LCN.2014.6925806
• Keywords: approximation algorithms, reinforcement learning, dynamic scheduling, heuristic algorithms, linear programming, optimization, telecommunication traffic, throughput
• Public URL: https://uwe-repository.worktribe.com/output/812630
• Publisher URL: http://dx.doi.org/10.1109/LCN.2014.6925806
• 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 39th Conference on Local Computer Networks (LCN)