1st Annual Conference on Broadband Networks

Research Article

Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning

  • @INPROCEEDINGS{10.1109/BROADNETS.2004.39,
        author={Fei  Yu and Vincent W.S. Wong and Victor C.M. Leung},
        title={Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning},
        proceedings={1st Annual Conference on Broadband Networks},
        publisher={IEEE},
        proceedings_a={BROADNETS},
        year={2004},
        month={12},
        keywords={},
        doi={10.1109/BROADNETS.2004.39}
    }
    
  • Fei Yu
    Vincent W.S. Wong
    Victor C.M. Leung
    Year: 2004
    Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning
    BROADNETS
    IEEE
    DOI: 10.1109/BROADNETS.2004.39
Fei Yu1,*, Vincent W.S. Wong1,*, Victor C.M. Leung1,*
  • 1: Department of Electrical and Computer Engineering, the University of British Columbia, 2356 Main Mall, Vancouver, BC, Canada V6T 1Z4
*Contact email: feiy@ece.ubc.ca, vincentw@ece.ubc.ca, vleung@ece.ubc.ca

Abstract

The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where it is possible to increase or decrease the bandwidth of individual ongoing flows. This paper studies the issues of quality of service (QoS) provisioning in such systems. In particular, call admission control and bandwidth adaptation are formulated as a constrained Markov decision problem. The rapid growth in the number of states and the difficulty in estimating state transition probabilities in practical systems make it very difficult to employ classical methods to find the optimal policy. We present a novel approach that uses a form of discounted reward reinforcement learning known as Q-learning to solve QoS provisioning for wireless adaptive multimedia. Q-learning does not require the explicit state transition model to solve the Markov decision problem, therefore more general and realistic assumptions can be applied to the underlying system model for this approach than in the previous schemes. Moreover, the proposed scheme can efficiently handle the large state space and action set of the wireless adaptive multimedia QoS provisioning problem. Handoff dropping probability and average allocated bandwidth are considered as QoS constraints in our model and can be guaranteed simultaneously. Simulation results demonstrate the effectiveness of the proposed scheme in adaptive multimedia mobile communication networks.