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research-article

Cognitive Capacity Harvesting Networks: Architectural Evolution Toward Future Cognitive Radio Networks

Authors:

Savo GlisicAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 19, Issue 3

Pages 1902 - 1923

https://doi.org/10.1109/COMST.2017.2677082

Published: 01 July 2017 Publication History

Abstract

Cognitive radio technologies enable users to opportunistically access unused licensed spectrum and are viewed as a promising way to deal with the current spectrum crisis. Over the last 15 years, cognitive radio technologies have been extensively studied from algorithmic design to practical implementation. One pressing and fundamental problem is how to integrate cognitive radios into current wireless networks to enhance network capacity and improve users’ experience. Unfortunately, existing solutions to cognitive radio networks (CRNs) suffer from many practical design issues. To foster further research activities in this direction, we attempt to provide a tutorial for CRN architecture design. Noticing that an effective architecture for CRNs is still lacking, in this tutorial, we systematically summarize the principles for CRN architecture design and present a novel flexible network architecture, termed cognitive capacity harvesting network (CCHN), to elaborate on how a CRN architecture can be designed. Unlike existing architectures, we introduce a new network entity, called secondary service provider, and deploy cognitive radio capability enabled routers, called cognitive radio routers, in order to effectively and efficiently manage resource harvesting and mobile traffic while enabling users without cognitive radios to access and enjoy CCHN services. Our analysis shows that our CCHN aligns well to industrial standardization activities and hence provides a viable approach to implementing future CRNs. We hope that our proposed design approach opens a new venue to future CRN research.

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cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 19, Issue 3

thirdquarter 2017

647 pages

ISSN:1553-877X

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1553-877X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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Published: 01 July 2017

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https://dl.acm.org/doi/10.1109/COMST.2024.3370169
Li XZhang YDing HFang Y(2023)Intelligent Spectrum Sensing and Access With Partial Observation Based on Hierarchical Multi-Agent Deep Reinforcement LearningIEEE Transactions on Wireless Communications10.1109/TWC.2023.330556723:4(3131-3145)Online publication date: 22-Aug-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3305567
Chen XZhu GDeng YFang Y(2022)Federated Learning Over Multihop Wireless Networks With In-Network AggregationIEEE Transactions on Wireless Communications10.1109/TWC.2022.316853821:6(4622-4634)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1109/TWC.2022.3168538
Deng YChen XZhu GFang YChen ZDeng X(2022)Actions at the Edge: Jointly Optimizing the Resources in Multi-Access Edge ComputingIEEE Wireless Communications10.1109/MWC.006.210069929:2(192-198)Online publication date: 1-Apr-2022
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Tzeng SChou H(2021)Multiple Access Control in a Centralized Full-Duplex Cognitive Machine Type Network with RF Energy HarvestingWireless Personal Communications: An International Journal10.1007/s11277-020-08053-z118:2(949-960)Online publication date: 1-May-2021
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Tzeng SLin Y(2021)Wireless energy transfer policies for cognitive radio based MAC in energy-constrained IoT networksTelecommunications Systems10.1007/s11235-021-00771-477:3(435-449)Online publication date: 1-Jul-2021
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Ai YWang LHan ZZhang PHanzo L(2018)Social Networking and Caching Aided Collaborative Computing for the Internet of ThingsIEEE Communications Magazine10.1109/MCOM.2018.170108956:12(149-155)Online publication date: 7-Dec-2018
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