Spectrum Pricing Games with Spatial Reuse in Cognitive Radio Networks

Kasbekar, Gaurav S.; Sarkar, Saswati

doi:10.1109/jsac.2012.120114

Cited by 49 publications

(33 citation statements)

References 19 publications

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“…The framework is aimed at maximizing the localized spectrum utilization within the static spectrum licensed to the wireless service providers. Furthermre, reference [22] analyzes a price competition scenario by identifying a class of conflict graphs, in which the authors refer to as mean valid graphs, such that the conflict graphs of a large number of topologies that commonly arise in practice are mean valid. Besides, in reference [23], a hierarchical spectrum trading model is presented to analyze the interaction among WRAN service providers, TV broadcasters and WRAN users.…”

Section: Introductionmentioning

confidence: 99%

Preference-Based Spectrum Pricing in Dynamic Spectrum Access Networks

Sheng

Hua

et al. 2018

IEEE Trans. Serv. Comput.

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(2016) Preference-based spectrum pricing in dynamic spectrum access networks. IEEE Transactions on Services Computing (99). ISSN 1939ISSN -1374 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/61903/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract-With market-driven secondary spectrum trading, licensed users can receive benefits in terms of monetary rewards or various transmission services, thus setting a fair pricing structure by suitably defining spectrum quality characteristics and accurately addressing participant's requirement is a key issue. In this paper, we investigate the pricing-based spectrum access by casting the problem of spectrum pricing into a Hotelling game model according to spectrum quality diversity. Particularly, we first build a pricing system model where unused spectrum from primary systems with different qualities forms a spectrum pool and can be divided into a number of uniform channels. A secondary user purchases a channel for usage according to its selection preference which is closely related to the channel quality and spectrum evaluation. The secondary user not only needs to consider the channel's quality and price, but also the interference cost on primary system. Detailed analysis on the policy preference of both primary system and secondary buyer are provided. By forming a game problem of spectrum pricing between primary and secondary users, we apply the Hotelling game model to handle the interaction between the participants. Specifically, by fixing Nash equilibrium of the game, an iterative algorithm for spectrum pricing is proposed based on the distribution characteristics of secondary user's preference. Essential analysis for the existence and uniqueness of the Nash equilibrium along with algorithm's convergence conditions are provided. Numerical results are also supplemented to show the effectiveness of the proposed algorithm in ensuring spectrum owner's profit.

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Section: Introductionmentioning

confidence: 99%

Preference-Based Spectrum Pricing in Dynamic Spectrum Access Networks

Sheng

Hua

et al. 2018

IEEE Trans. Serv. Comput.

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“…The closest to our work are [10], [11], which analyze price competition where each seller may be inactive with some probability, as also our prior work [20], [21], [22], [23], [24] in which we analyzed price competition in a CRN. However, all of the above papers assume that the bandwidth (commodity) availabilities and demands of different sellers and buyers are mutually independent.…”

Section: Introductionmentioning

confidence: 99%

Spectrum pricing games with correlated bandwidth availabilities and demands

Kasbekar

Sarkar

2014

2014 Information Theory and Applications Workshop (ITA)

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Abstract-We study price competition among primaries in a Cognitive Radio Network (CRN) with multiple primaries and secondaries located in a large region. In a given slot, some of the primaries have unused bandwidth, which they can potentially lease out to secondaries in exchange for a fee. There is uncertainty in whether a given primary has unused bandwidth in a given slot as well as in the number of secondaries that require bandwidth, with the above random quantities being mutually correlated. Each primary tries to attract secondaries by setting a lower price for its bandwidth than the other primaries. Radio spectrum has the distinctive feature that transmissions at neighboring locations on the same channel interfere with each other, whereas the same channel can be used at far-off locations without mutual interference. So in the above price competition scenario, each primary must jointly select a set of mutually non-interfering locations within the region (which corresponds to an independent set in the conflict graph representing the region) at which to offer bandwidth and the price at each location. In this paper, we analyze this price competition scenario as a game and seek a Nash Equilibrium (NE). We analyze the game at a single location as well as the game at multiple locations. We characterize NE for the cases of (i) symmetric bandwidth availability events of different primaries and (ii) asymmetric bandwidth availability events with a special correlation structure.

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“…And game theory is an effective method to study the behavior of users in distributed schemes [10] . Moreover, pricing mechanism was introduced to improve the efficiency of nash equilibrium [11,12] . Power control based on pricing was used to optimize the PU s utility [13] .…”

Section: Introductionmentioning

confidence: 99%

A Stackelberg Game for Spectrum Leasing in Cooperative Cognitive Radio Networks

Wang

Guan

Han

et al. 2013

Int. J. Autom. Comput.

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According to the property-rights model of cognitive radio, primary users (PUs) who own the spectrum resource have the right to lease part of spectrum to secondary users (SUs) in exchange for appropriate profit. In this paper, we propose a pricing-based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU s transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller-level game and the selected SUs play the buyer-level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.

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Spectrum Pricing Games with Spatial Reuse in Cognitive Radio Networks

Cited by 49 publications

References 19 publications

Preference-Based Spectrum Pricing in Dynamic Spectrum Access Networks

Preference-Based Spectrum Pricing in Dynamic Spectrum Access Networks

Spectrum pricing games with correlated bandwidth availabilities and demands

A Stackelberg Game for Spectrum Leasing in Cooperative Cognitive Radio Networks

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