10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

Research Article

The Optimal User Scheduling for LTE-A Downlink with Heterogeneous Traffic Types

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  • @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
Samira Niafar1,*, Xiaoqi Tan1, Danny Tsang1
  • 1: HKUST
*Contact email: sniafar@ust.hk

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.