Title

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

Authors

Authors

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

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

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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