Research Article
The Optimal User Scheduling for LTE-A Downlink with Heterogeneous Traffic Types
@INPROCEEDINGS{10.4108/icst.qshine.2014.256404, author={Samira Niafar and Xiaoqi Tan and Danny Tsang}, title={The Optimal User Scheduling for LTE-A Downlink with Heterogeneous Traffic Types}, proceedings={10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={IEEE}, proceedings_a={QSHINE}, year={2014}, month={9}, keywords={resource scheduling; constrained markov decision process; bernstein approximation; lte-a; heterogeneous delay requirements}, doi={10.4108/icst.qshine.2014.256404} }- Samira Niafar
Xiaoqi Tan
Danny Tsang
Year: 2014
The Optimal User Scheduling for LTE-A Downlink with Heterogeneous Traffic Types
QSHINE
IEEE
DOI: 10.4108/icst.qshine.2014.256404
Abstract
The current mobile broadband market experiences major growth in data demand and average revenue loss. To remain profitable from the perspective of a service provider (SP), one needs to maximize revenue as much as possible by making subscribers satisfied within the limited budget. On the other hand, traffic demands are moving toward supporting the wide range of heterogeneous services with different quality of service (QoS) requirements. In this paper, we consider packet scheduling problem in the 4th generation partnership project (3GPP) long term evolution-advanced (LTE-A) system to optimize the long-term average revenue of SPs subject to differential QoS constraints for heterogeneous traffic demands. The QoS-constrained control problem is first formulated as a constrained Markov decision process (CMDP) problem, of which the optimal control policy is achieved by utilizing the channel and queue information simultaneously. Subsequently, based on the proposed CMDP problem, we further formulated an optimization problem which stochastically grantees the QoS through a chance constraint. To make the proposed chance-constraint programming problem computationally tractable, we use Bernstein approximation technique to analytically approximate the chance constraint as a convex conservative constraint. Finally, the proposed scheduling framework and solution methods are validated via numerical simulation.
