A Market Game for Dynamic Multi-Band Sharing in Cognitive Radio Networks

Li, D.; Xu, Yin; Liu, J.; Wang, X.; Han, Zhu

doi:10.1109/icc.2010.5502755

Cited by 22 publications

(15 citation statements)

References 11 publications

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“…We note that the secondary utility form in (10) is no longer quasi-concave. However u kℓ (τ kℓ , P kℓ ) is still decreasing in τ kℓ and increasing in P kℓ , while the opposite is true for v kℓ (τ kℓ , P kℓ ) 20 . Let us consider another negotiation and aspiration update process in this context.…”

Section: Blma Application To a Cognitive Radio Network With Generamentioning

confidence: 84%

“…see [16] for a comprehensive literature review, [11] for a tutorial, and [17]- [20] and the references therein for various applications of matching theory to wireless networks.…”

Section: B Application Of Matching Theory In Cognitive Radio Networkmentioning

confidence: 99%

“…However, we will bypass this difficulty by focusing on one dimension at any given instant while only requiring the less stringent assumption that the interests of the agents on the two sides of the market are opposed. 1) BLMA2: The PU-SU negotiation process 20 While it is obvious v kℓ (τ kℓ , P kℓ ) is decreasing in P kℓ , it is not immediately clear whether it is increasing in τ kℓ . However taking the derivative of v kℓ (τ kℓ , P kℓ ) with respect to τ kℓ yields…”

Section: A Blma2mentioning

confidence: 99%

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BLMA: A Blind Matching Algorithm With Application to Cognitive Radio Networks

Hamza

Shamma

2017

IEEE J. Select. Areas Commun.

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We consider a two-sided matching problem with a defined notion of pairwise stability. We propose a distributed blind matching algorithm (BLMA) to solve the problem. We prove the solution produced by BLMA will converge to an -pairwise stable outcome with probability one. We then consider a matching problem in cognitive radio networks. Secondary users (SUs) are allowed access time to the spectrum belonging to the primary users (PUs) provided that they relay primary messages. We propose a realization of the BLMA to produce an -pairwise stable solution assuming quasi-convex and quasiconcave utilities. In the case of more general utility forms, we show another BLMA realization to provide a stable solution. Furthermore, we propose negotiation mechanism to bias the algorithm towards one side of the market. We use this mechanism to protect the exclusive rights of the PUs to the spectrum. In all such implementations of the BLMA, we impose a limited information exchange in the network so that agents can only calculate their own utilities, but no information is available about the utilities of any other users in the network.

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Section: Blma Application To a Cognitive Radio Network With Generamentioning

confidence: 84%

“…see [16] for a comprehensive literature review, [11] for a tutorial, and [17]- [20] and the references therein for various applications of matching theory to wireless networks.…”

Section: B Application Of Matching Theory In Cognitive Radio Networkmentioning

confidence: 99%

Section: A Blma2mentioning

confidence: 99%

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BLMA: A Blind Matching Algorithm With Application to Cognitive Radio Networks

Hamza

Shamma

2017

IEEE J. Select. Areas Commun.

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“…None of these works try to balance the PUs' revenues and the QoS for multiple classes. Moreover, the dynamic behavior to adapt to the network conditions was ignored in these strategies [4,[14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

“…A new framework was proposed in [15] to model the competition among multiple SUs to access the radio spectrum. Multiple SUs buy spectrums from multiple owners in [16]. A game theoretic framework is used to model the dynamic spectrum sharing in multi-owners and multi-users cognitive radio networks.…”

Section: Related Workmentioning

confidence: 99%

Profit optimization in multi-service cognitive mesh network using machine learning

Alsarhan

Agarwal

2011

J Wireless Com Network

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Cognitive technology enables licensed users (primary users, PUs) to trade the surplus spectrum and to transfer temporarily spectrum usage right to the unlicensed users (secondary users, SUs) to get some reward. The rented spectrum is used to establish secondary network. However, the rented spectrum size influences the quality of service (QoS) for the PU and the gained rewards. Therefore, the PU needs a resource management scheme that helps it to allocate optimally a given amount of the offered spectrum among multiple service classes and to adapt to changes in the network conditions. The PU should support different classes of SUs that pay different prices for their usage of spectrum. We propose a novel approach to maximize a PU reward and to maintain QoS for the PUs and for the different classes of SUs. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed to derive resource adaptations to changing network conditions, so that PUs' profit can continuously be maximized. Available spectrum is managed by the PU that executes the optimal control policy, which is extracted using RL. Performance evaluation of the proposed RL solution shows that the scheme is able to adapt to different conditions and to guarantee the required QoS for PUs and to maintain the QoS for a multiple classes of SUs, while maximizing PUs profits. The results have shown that cognitive mesh network can support additional SUs traffic while still ensuring PUs QoS. In our model, PUs exchange channels based on the spectrum demand and traffic load. The solution is extended to the case in which there are multiple PUs in the network where a new distributed algorithm is proposed to dynamically manage spectrum allocation among PUs.

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Dynamic decentralised algorithms for cognitive radio relay networks with multiple primary and secondary users utilising matching theory

Bayat

Louie

Vucetic

et al. 2013

Trans. Emerging Tel. Tech.

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We propose a distributed spectrum access algorithm for cognitive radio relay networks with multiple primary users (PUs) and multiple secondary users (SUs). The key idea behind the proposed algorithm is that the PUs negotiate with the SUs on both the amount of monetary compensation, and the amount of time the SUs are either (i) allowed spectrum access, or (ii) cooperatively relaying the PU's data, such that both the PUs' and the SUs' minimum rate requirements are satisfied. The proposed algorithm is shown to be flexible in prioritising either the PUs or the SUs. We prove that the proposed algorithm will result in the best possible stable matching and is weak Pareto optimal. Numerical analysis also reveals that the distributed algorithm can achieve a performance comparable to an optimal centralised solution, but with significantly less overhead and complexity. Copyright © 2013 John Wiley & Sons, Ltd.

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A Market Game for Dynamic Multi-Band Sharing in Cognitive Radio Networks

Cited by 22 publications

References 11 publications

BLMA: A Blind Matching Algorithm With Application to Cognitive Radio Networks

BLMA: A Blind Matching Algorithm With Application to Cognitive Radio Networks

Profit optimization in multi-service cognitive mesh network using machine learning

Dynamic decentralised algorithms for cognitive radio relay networks with multiple primary and secondary users utilising matching theory

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