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Journal ArticleDOI

Queuing-Based Dynamic Channel Selection for Heterogeneous Multimedia Applications Over Cognitive Radio Networks

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TLDR
A novel priority virtual queue interface that determines the required information exchanges and evaluates the expected delays experienced by various priority traffics, and a dynamic strategy learning (DSL) algorithm deployed at each user that exploits the expected delay and dynamically adapts the channel selection strategies to maximize the user's utility function.
Abstract
In this paper, we propose a dynamic channel-selection solution for autonomous wireless users transmitting delay-sensitive multimedia applications over cognitive radio networks. Unlike prior works that seldom consider the requirement of the application layer, our solution explicitly considers various rate requirements and delay deadlines of heterogeneous multimedia users. Note that the users usually possess private utility functions, application requirements, and distinct channel conditions in different frequency channels. To efficiently manage available spectrum resources in a decentralized manner, information exchange among users is necessary. Hence, we propose a novel priority virtual queue interface that determines the required information exchanges and evaluates the expected delays experienced by various priority traffics. Such expected delays are important for multimedia users due to their delay-sensitivity nature. Based on the exchanged information, the interface evaluates the expected delays using priority queuing analysis that considers the wireless environment, traffic characteristics, and the competing users' behaviors in the same frequency channel. We propose a dynamic strategy learning (DSL) algorithm deployed at each user that exploits the expected delay and dynamically adapts the channel selection strategies to maximize the user's utility function. We simulate multiple video users sharing the cognitive radio network and show that our proposed solution significantly reduces the packet loss rate and outperforms the conventional single-channel dynamic resource allocation by almost 2 dB in terms of video quality.

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Citations
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Journal ArticleDOI

Primary radio user activity models for cognitive radio networks: A survey

TL;DR: This paper combines all the primary radio user activity models for cognitive radio networks at a single place to provide a single source in the form of survey paper by which a reader can get an idea about which primary radio users activity models have been used in the literature for Cognitive radio networks and how the modeling is performed.
Journal ArticleDOI

A Spectrum Decision Framework for Cognitive Radio Networks

TL;DR: A minimum variance-based spectrum decision is proposed for real-time applications, which minimizes the capacity variance of the decided spectrum bands subject to the capacity constraints, and a dynamic resource management scheme is developed to coordinate the spectrum decision adaptively dependent on the time-varying cognitive radio network capacity.
Journal ArticleDOI

Modeling and Analysis for Spectrum Handoffs in Cognitive Radio Networks

TL;DR: An analytical framework to evaluate the latency performance of connection-based spectrum handoffs in cognitive radio (CR) networks and proposes the preemptive resume priority (PRP) M/G/1 queuing network model to characterize the spectrum usage behaviors with all the three design features.
Journal ArticleDOI

Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey

TL;DR: This paper presents a comprehensive survey of WMCRNs, and covers the quality-of-experience design and security requirements for transmitting multimedia content over CRNs, as well as survey the major spectrum sensing approaches used for the communications of bandwidth hungry and time-critical data overCRNs.
Journal ArticleDOI

QoE-Driven Channel Allocation Schemes for Multimedia Transmission of Priority-Based Secondary Users over Cognitive Radio Networks

TL;DR: The results show that the proposed channel allocation approach can significantly improve the QoE of the priority-based SUs over the cognitive radio networks.
References
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