Brokering and Pricing Architecture over Cognitive Radio Wireless Networks

Isiklar, G.; Bener, Ayşe

doi:10.1109/ccnc08.2007.230

Cited by 7 publications

(10 citation statements)

References 12 publications

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“…The competition problem is formulated as a non-cooperative game and new iterative bidding scheme that achieves Nash equilibrium of the operator game is proposed. In the proposed system in [14], two spectrum brokers offer a spectrum for a group of PUs. The broker wants to maximize its own revenue.…”

Section: Related Work Of Spectrum Trading Using Crsmentioning

confidence: 99%

Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

Alsarhan

Agarwal

2012

Journal of Electrical and Computer Engineering

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In a cognitive wireless mesh network, licensed users (primary users, PUs) may rent surplus spectrum to unlicensed users (secondary users, SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their requirements. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs’ profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor’s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty-line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently.

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Section: Related Work Of Spectrum Trading Using Crsmentioning

confidence: 99%

Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

Alsarhan

Agarwal

2012

Journal of Electrical and Computer Engineering

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“…In our model, the PU y revenues sensitivity to the number of the offered spectrum size can be derived from equation (10):…”

Section: Resource Adaptation Using Cognitive Network Spectrum Size Admentioning

confidence: 99%

“…The competition problem is formulated as a non-cooperative game and a new iterative bidding scheme that achieves Nash equilibrium of the operator game is proposed. Two spectrum brokers offer a spectrum for PUs in [10]. The key objective of the broker is maximizing its own revenue.…”

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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“…To coordinate a large number of CRs, some research papers have integrated brokers into the telecommunication business model [2], [3]. Such dynamic frequency brokers (DFBs) are responsible for allocating frequency bands to radio nodes within their geographic area.…”

Section: Introductionmentioning

confidence: 99%

“…DFBs work in a hierarchical manner, with national level DFBs on top of the regional level DFBs. The frequency band allocation is enforced from top to bottom [3]. Bidding procedures (between users and service providers (SPs), or between SPs and spectrum brokers) have been modeled based on game theory in the literature [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Wireless network virtualization

Wang

Krishnamurthy

Tipper

2013

2013 International Conference on Computing, Networking and Communications (ICNC)

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Abstract-The virtualization of wired networks and end computing systems has become one of the leading trends in networked information and communication technology (ICT) systems. In contrast relatively little virtualization has occurred in infrastructure based wireless networks, but the idea of virtualizing wireless access is gaining attention as it has the potential to improve spectrum utilization and perhaps create new services. In this paper we survey the state of the current research in virtualizing wireless networks. We define and describe possible architectures, the issues, hurdles and trends towards implementation of wireless network virtualization.

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Brokering and Pricing Architecture over Cognitive Radio Wireless Networks

Cited by 7 publications

References 12 publications

Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning

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

Wireless network virtualization

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