Mobile Networks and Applications

, Volume 21, Issue 3, pp 390–401 | Cite as

Optimal Downlink Scheduling for Heterogeneous Traffic Types in LTE-A Based on MDP and Chance-Constrained Approaches



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 applications with different quality of service (QoS) requirements. In this paper, we consider two related packet scheduling problems, i.e., long-term and short-term approaches in the 4th generation partnership project (3GPP) long term evolution-advanced (LTE-A) system. In the long-term approach, the long-term average revenue of SP subject to the long-term QoS constraints for heterogeneous traffic demands is optimized. The 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, in the short-term approach, we consider the short-term revenue optimization problem which stochastically guarantees the short-term QoS for heterogeneous traffic demands through a set of chance constraints. To make the proposed chance-constrained programming problem computationally tractable, we use the Bernstein approximation technique to analytically approximate the chance constraint as a convex conservative constraint. Finally, the proposed packet scheduling schemes and solution methods are validated via numerical simulations.


Chance-constrained Constrained Markov decision process Bernstein approximation LTE-A Heterogeneous delay requirements 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.The Hong Kong University of Science and TechnologyClear Water BayHong Kong

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