Cognitive Radio Networks With Heterogeneous Users: How to Procure and Price the Spectrum?

Cao, Xuanyu; Chen, Yan; Liu, K. J. Ray

doi:10.1109/twc.2014.2371818

Cited by 26 publications

(18 citation statements)

References 23 publications

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“…Niyato and Hossain adopted noncooperative game to model the price competition among numerous service providers in a heterogeneous network for total revenue maximization. A differentiated pricing scheme for secondary users was proposed in the work of Cao et al to improve the benefit of a cognitive mobile virtual network operator. A game‐theoretic approach was proposed in the work of Chen et al to describe the relationships between service providers and users, in which the performance‐cost ratio of a user is maximized by a pricing‐based network selection scheme.…”

Section: Related Workmentioning

confidence: 99%

Optimal pricing strategy for resource allocation in 5G heterogeneous cellular networks

Chen

Liao

et al. 2018

Trans Emerging Tel Tech

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Heterogeneous networks provide an effective solution to bridge the gap between enormous global traffic demands and limited spectrum resources. In this paper, we investigate the resource allocation problem in fifth‐generation heterogeneous cellular networks from an economic perspective. We consider the open access scenario where any cellular user equipment is allowed to access either the high‐power node or the low‐power node based on its individual payoff. The resource allocation problem is formulated as a pricing problem, which aims at maximizing the total revenue of the service providers under the user admission constraint. Then, the optimal pricing strategies for both the high‐power node and the low‐power node are developed based on the decisions of cellular user equipments. Simulation results demonstrate that the proposed pricing algorithm can achieve the maximum revenue for service providers under various scenarios.

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Section: Related Workmentioning

confidence: 99%

Optimal pricing strategy for resource allocation in 5G heterogeneous cellular networks

Chen

Liao

et al. 2018

Trans Emerging Tel Tech

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“…In this case, the SC is reduced to a cognitive (secondary) network while the MC can be regarded as a primary network [27].…”

Section: Remarkmentioning

confidence: 99%

“…In [26], joint energy and spectrum cooperation between two neighbouring cellular networks are considered to minimize the total costs on the pre-priced bandwidth and power given the QoS requirement. However, the monetary based spectrum sharing and energy cooperation are unable to capture the instantaneous characteristics of wireless channels [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Small Cell With Spectrum-Power Trading

Chen

et al. 2016

IEEE J. Select. Areas Commun.

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In this paper, we investigate spectrum-power trading between a small cell (SC) and a macro cell (MC), where the SC consumes power to serve the macro cell users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service of each MU as well as small cell users (SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractionalform objective function. We first show that the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocation is obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of the trading EE to characterize the data rate obtained as well as the power consumed for serving an MU. We then reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint: the trading EE of the MU should be higher than the system EE of the SC. Based on this insight, we propose a low complexity MU selection method and also investigate the optimality condition. Simulation results verify our theoretical findings and demonstrate that the proposed resource allocation achieves near-optimal performance.

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“…These studies focused primarily on the static pricing with complete information in the spectrum database system, without considering the heterogeneous and stochastic SU demands as in our work. Besides the studies on spectrum database economics, there are also some recent related studies on secondary spectrum sharing and allocation (e.g., [8]- [11]), which focused primarily on static pricing and static access control. The focus of our study is the pricing and dynamic admission control with time domain heterogeneity.…”

Section: Related Workmentioning

confidence: 99%

Optimal Pricing and Admission Control for Heterogeneous Secondary Users

Jiang

Duan

Huang

2016

IEEE Trans. Wireless Commun.

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This paper studies how to maximize a spectrum database operator's expected revenue in sharing spectrum to secondary users, through joint pricing and admission control of spectrum resources. A unique feature of our model is the consideration of the stochastic and heterogeneous nature of secondary users' demands. We formulate the problem as a stochastic dynamic programming problem, and present the optimal solutions under both static and dynamic pricing schemes. In the case of static pricing, the prices do not change with time, although the admission control policy can still be time-dependent. In this case, we show that a stationary (time-independent) admission policy is in fact optimal under a wide range of system parameters. In the case of dynamic pricing, we allow both prices and admission control policies to be time-dependent. We show that the optimal dynamic pricing can improve the operator's revenue by more than 30% over the optimal static pricing, when secondary users' demands for spectrum opportunities are highly elastic.

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Cognitive Radio Networks With Heterogeneous Users: How to Procure and Price the Spectrum?

Cited by 26 publications

References 23 publications

Optimal pricing strategy for resource allocation in 5G heterogeneous cellular networks

Optimal pricing strategy for resource allocation in 5G heterogeneous cellular networks

Energy-Efficient Small Cell With Spectrum-Power Trading

Optimal Pricing and Admission Control for Heterogeneous Secondary Users

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