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Pricing-based decentralized spectrum access control in cognitive radio networks

Authors:

Chee Wei TanAuthors Info & Claims

IEEE/ACM Transactions on Networking (TON), Volume 21, Issue 2

Pages 522 - 535

https://doi.org/10.1109/TNET.2012.2203827

Published: 01 April 2013 Publication History

Abstract

This paper investigates pricing-based spectrum access control in cognitive radio networks, where primary users (PUs) sell the temporarily unused spectrum and secondary users (SUs) compete via random access for such spectrum opportunities. Compared to existing market-based approaches with centralized scheduling, pricing-based spectrum management with random access provides a platform for SUs contending for spectrum access and is amenable to decentralized implementation due to its low complexity. We focus on two market models, one with a monopoly PU market and the other with a multiple-PU market. For the monopoly PU market model, we devise decentralized pricing-based spectrum access mechanisms that enable SUs to contend for channel usage. Specifically, we first consider SUs contending via slotted Aloha. Since the revenue maximization problem therein is nonconvex, we characterize the corresponding Pareto-optimal region and obtain a Pareto-optimal solution that maximizes the SUs' throughput subject to their budget constraints. To mitigate the spectrum underutilization due to the "price of contention," we revisit the problem where SUs contend via CSMA, which results in more efficient spectrum utilization and higher revenue. We then study the tradeoff between the PU's utility and its revenue when the PU's salable spectrum is controllable. Next, for the multiple-PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We explore the existence and the uniqueness of Nash equilibrium, in terms of access prices and the spectrum offered to SUs, and develop an iterative algorithm for strategy adaptation to achieve the Nash equilibrium. Our findings reveal that there exists a unique Nash equilibrium when the number of PUs is less than a threshold determined by the budgets and elasticity of SUs.

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Cited By

Chowdhury S(2022)Resource Allocation in Cognitive Radio Networks Using Stackelberg Game: A SurveyWireless Personal Communications: An International Journal10.1007/s11277-021-08926-x122:1(807-824)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11277-021-08926-x
Ji WZhu W(2020)Profit Maximization for Sponsored Data in Wireless Video Transmission SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2019.291687219:8(1928-1942)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1109/TMC.2019.2916872
(2018)Resource management in cellular base stations powered by renewable energy sourcesJournal of Network and Computer Applications10.1016/j.jnca.2018.03.021112:C(1-17)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1016/j.jnca.2018.03.021
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Pricing-based decentralized spectrum access control in cognitive radio networks

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 21, Issue 2

April 2013

333 pages

ISSN:1063-6692

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Publisher

IEEE Press

Publication History

Published: 01 April 2013

Accepted: 27 May 2012

Revised: 13 February 2012

Received: 21 June 2011

Published in TON Volume 21, Issue 2

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Cited By

Chowdhury S(2022)Resource Allocation in Cognitive Radio Networks Using Stackelberg Game: A SurveyWireless Personal Communications: An International Journal10.1007/s11277-021-08926-x122:1(807-824)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11277-021-08926-x
Ji WZhu W(2020)Profit Maximization for Sponsored Data in Wireless Video Transmission SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2019.291687219:8(1928-1942)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1109/TMC.2019.2916872
(2018)Resource management in cellular base stations powered by renewable energy sourcesJournal of Network and Computer Applications10.1016/j.jnca.2018.03.021112:C(1-17)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1016/j.jnca.2018.03.021
(2018)Editorial BoardComputers and Structures10.1016/S0045-7949(18)30664-3204:C(ii)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1016/S0045-7949%2818%2930664-3
Zhu XGong XTsang D(2018)The optimal macro control strategies of service providers and micro service selection of usersWireless Networks10.1007/s11276-016-1436-324:6(1991-2004)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11276-016-1436-3
Çavdar TSadreddini ZGüler E(2018)Pre-reservation based spectrum allocation for cognitive radio networkTelecommunications Systems10.1007/s11235-018-0424-668:4(723-743)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11235-018-0424-6
Zandi MDong MGrami A(2016)Distributed Stochastic Learning and Adaptation to Primary Traffic for Dynamic Spectrum AccessIEEE Transactions on Wireless Communications10.1109/TWC.2015.249515415:3(1675-1688)Online publication date: 8-Mar-2016
https://dl.acm.org/doi/10.1109/TWC.2015.2495154
Chen XGong XYang LZhang J(2016)Exploiting Social Tie Structure for Cooperative Wireless NetworkingIEEE/ACM Transactions on Networking10.1109/TNET.2016.253007024:6(3593-3606)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1109/TNET.2016.2530070
Tsiropoulos GDobre OAhmed MBaddour K(2016)Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio NetworksIEEE Communications Surveys & Tutorials10.1109/COMST.2014.236279618:1(824-847)Online publication date: 27-Jan-2016
https://dl.acm.org/doi/10.1109/COMST.2014.2362796
(2016)Cognitive radio for M2M and Internet of ThingsComputer Communications10.1016/j.comcom.2016.07.01294:C(1-29)Online publication date: 15-Nov-2016
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