Joint Pricing and Power Allocation for Dynamic Spectrum Access Networks with Stackelberg Game Model

Wu, Yuan; Zhang, Tengyi; Tsang, Danny H. K.

doi:10.1109/twc.2010.120310.091430

Cited by 76 publications

(64 citation statements)

References 5 publications

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“…Specifically, when = 0, the critical rate threshold (which is characterized in Proposition 1) is zero according to eq. (22). Correspondingly, the interferencecap 0 at each AP is also zero, meaning that the SUs' access is completely forbidden.…”

Section: Revenue Sharing In the Top Layer Via A Nashmentioning

confidence: 99%

“…Besides leasing the idle resource to the SUs in an interference-free manner, the PUs can also allow concurrent transmission of the SUs and then charge the suffered interference from them. Related papers [19]- [22] investigated the charging of SUs' interference to optimize the revenue of PU. Specifically, in [19], [20], the PU aimed at maximizing its revenue by charging the SUs' interference, which was subject to a fixed interference-cap.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, in [19], [20], the PU aimed at maximizing its revenue by charging the SUs' interference, which was subject to a fixed interference-cap. In comparison, in [21], [22], the PU's interference-cap was considered to be a tunable parameter, which worked together with the power allocation to optimize the revenue of PU. Auction strategy is also an effective strategy to motivate the resource leasing of primary network [23]- [27].…”

Section: Introductionmentioning

confidence: 99%

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Revenue Sharing Based Resource Allocation for Dynamic Spectrum Access Networks

Zhu

Huang

et al. 2014

IEEE J. Select. Areas Commun.

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Abstract-We propose a revenue sharing based resource allocation scheme for dynamic spectrum access (DSA) networks. In our scheme, based on a mutually agreed revenue sharing scheme, a primary network operator (PNO) actively shares its radio resource with a secondary network operator (SNO), which provides access service to secondary users (SUs) for its revenue maximization. To investigate the coupling effect between the revenue sharing and resource allocation, we formulate the interaction between PNO and SNO as a two-layered game, which includes a top layer game to model their revenue sharing and a bottom layer game to model their joint resource allocations. Specifically, in the top layer, based on their joint resource allocation decisions, the PNO and SNO form a Nash bargaining game to determine the revenue sharing scheme such that both of them can benefit from cooperation satisfactorily. Then, in the bottom layer, under the given revenue sharing scheme, the PNO and SNO form a Stackelberg game to determine their joint resource allocation decisions, which also influence their respective revenues. The two games work iteratively such that the PNO and SNO reach a final equilibrium state at which neither PNO nor SNO will change its decisions unilaterally in both layers. We propose efficient algorithms to solve both the top layer and bottom layer games and compute the final equilibrium of the two-layered game. Specifically, despite the non-convexity of joint resource allocation optimization problem in the bottom layer, we identify its hidden monotonic structure and propose an efficient algorithm, which is based on the polyblock approximation, to achieve the optimal solutions. Moreover, in the top layer, to tackle with the difficulty due to the lack of an analytical objective function for the revenue sharing problem, we explore its hidden unimodal property and propose a Brent's method based algorithm to achieve the optimal solution. Numerical results are presented to verify the performance of our algorithms and show that our revenue sharing based resource allocation scheme yields a winwin situation for the PNO and SNO.

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Section: Revenue Sharing In the Top Layer Via A Nashmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revenue Sharing Based Resource Allocation for Dynamic Spectrum Access Networks

Zhu

Huang

et al. 2014

IEEE J. Select. Areas Commun.

Self Cite

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“…In Ref. [8], the authors proposed a joint-pricing and power allocation scheme for the CRNs. We proposed a novel price-based power control algorithm based on convex optimization to maximize the revenue of the CDMA based CRNs in Ref.…”

Section: Introductionmentioning

confidence: 99%

Price-based power control algorithm in cognitive radio networks based on monotone optimization

Wang

Jiang

2015

J. Shanghai Jiaotong Univ. (Sci.)

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This paper considers a price-based power control problem in the cognitive radio networks (CRNs). The primary user (PU) can admit secondary users (SUs) to access if their interference powers are all under the interference power constraint. In order to access the spectrum, the SUs need to pay for their interference power. The PU first decides the price for each SU to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on a non-cooperative game. The interaction between the PU and the SUs is modeled as a Stackelberg game. Using the backward induction, a revenue function of the PU is expressed as a non-convex function of the transmit power of the SUs. To find the optimal price for the PU, we rewrite the revenue maximization problem of the PU as a monotone optimization by variable substitution. Based on the monotone optimization, a novel price-based power control algorithm is proposed. Simulation results show the convergence and the effectiveness of the proposed algorithm compared to the non-uniform pricing algorithm.

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“…Game theory is an effective tool for resource allocation in multiuser communication systems, which has been applied to CRNs, and [1] summarized the advance of game theory for CRNs. Recently, Stackelberg game has been formulated for resource allocation which addresses the relationship between PUs and SUs in [2][3][4][5][6][7][8][9]. In these two-tier femtocell networks, the PUs were served by the mobile operators, whereas the SUs are deployed by indoor users for their own interests.…”

Section: Introductionmentioning

confidence: 99%

Stackelberg Game Based Power Control with Outage Probability Constraints for Cognitive Radio Networks

Yang

Xie

Vasilakos

2015

International Journal of Distributed Sensor Networks

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This paper firstly investigates the problem of uplink power control in cognitive radio networks (CRNs) with multiple primary users (PUs) and multiple second users (SUs) considering channel outage constraints and interference power constraints, where PUs and SUs compete with each other to maximize their utilities. We formulate a Stackelberg game to model this hierarchical competition, where PUs and SUs are considered to be leaders and followers, respectively. We theoretically prove the existence and uniqueness of robust Stackelberg equilibrium for the noncooperative approach. Then, we apply the Lagrange dual decomposition method to solve this problem, and an efficient iterative algorithm is proposed to search the Stackelberg equilibrium. Simulation results show that the proposed algorithm improves the performance compared with those proportionate game schemes.

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Joint Pricing and Power Allocation for Dynamic Spectrum Access Networks with Stackelberg Game Model

Cited by 76 publications

References 5 publications

Revenue Sharing Based Resource Allocation for Dynamic Spectrum Access Networks

Revenue Sharing Based Resource Allocation for Dynamic Spectrum Access Networks

Price-based power control algorithm in cognitive radio networks based on monotone optimization

Stackelberg Game Based Power Control with Outage Probability Constraints for Cognitive Radio Networks

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