A Learning-Based QoE-Driven Spectrum Handoff Scheme for Multimedia Transmissions over Cognitive Radio Networks

    Authors

    Y. Q. Wu; F. Hu; S. Kumar; Y. Y. Zhu; A. Talari; N. Rahnavard;J. D. Matyjas

    Comments

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    Abbreviated Journal Title

    IEEE J. Sel. Areas Commun.

    Keywords

    Cognitive Radio Networks; Spectrum Handoff; Queueing Model; QoE; Reinforcement Learning; Multimedia Transmission; AD HOC NETWORKS; WIRELESS NETWORKS; CHANNEL; SELECTION; DECISION; Engineering, Electrical & Electronic; Telecommunications

    Abstract

    Enabling the spectrum handoff for multimedia applications in cognitive radio networks (CRNs) is challenging, due to multiple interruptions from primary users (PUs), contentions among secondary users (SUs), and heterogenous Quality-of-Experience (QoE) requirements. In this paper, we propose a learning-based and QoE-driven spectrum handoff scheme to maximize the multimedia users' satisfaction. We develop a mixed preemptive and non-preemptive resume priority (PRP/NPRP) M/G/1 queueing model for modeling the spectrum usage behavior for prioritized multimedia applications. Then, a mathematical framework is formulated to analyze the performance of SUs. We apply the reinforcement learning to our QoE-driven spectrum handoff scheme to maximize the quality of video transmissions in the long term. The proposed learning scheme is asymptotically optimal, model-free, and can adaptively perform spectrum handoff for the changing channel conditions and traffic load. Experimental results demonstrate the effectiveness of the proposed queueing model for prioritized traffic in CRNs, and show that the proposed learning-based QoE-driven spectrum handoff scheme improves quality of video transmissions.

    Journal Title

    Ieee Journal on Selected Areas in Communications

    Volume

    32

    Issue/Number

    11

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    2134

    Last Page

    2148

    WOS Identifier

    WOS:000348856700014

    ISSN

    0733-8716

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