Title

A Learning-Based Qoe-Driven Spectrum Handoff Scheme For Multimedia Transmissions Over Cognitive Radio Networks

Keywords

Cognitive Radio Networks; Multimedia Transmission; QoE; Queueing Model; Reinforcement Learning; Spectrum Handoff

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.

Publication Date

11-1-2014

Publication Title

IEEE Journal on Selected Areas in Communications

Volume

32

Issue

11

Number of Pages

2134-2148

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/JSAC.2014.141115

Socpus ID

84919653549 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84919653549

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