A stackelberg game approach to distributed spectrum management

We consider a cognitive radio system with one primary (licensed) user and multiple secondary (unlicensed) users. Given the interference temperature constraint, the secondary users compete for the available spectrum to fulfill their own communication need. Borrowing the concept of price from market theory, we develop a decentralized Stackelberg game formulation for power allocation. In this scheme, the primary user (leader) announces prices for the available tones such that a system utility is maximized. Using the announced prices, secondary users (followers) compete for the available bandwidth to maximize their own utilities. We show that this Stackelberg game is polynomial time solvable under certain channel conditions. When the individual power constraints of secondary users are inactive (due to strict interference temperature constraint), the proposed distributed power control method is decomposable across the tones and unlike normal water-filling it respects the interference temperature constraints of the primary user. When individual power constraints are active, we propose a distributed approach that solves the problem under an aggregate interference temperature constraint. Moreover, we propose a dual decomposition based power control method and show that it solves the Stackelberg game asymptotically when the number of tones becomes large.

[1]  Francisco Facchinei,et al.  Design of cognitive radio systems under temperature-interference constraints: a variational inequality approach , 2010, IEEE Trans. Signal Process..

[2]  Jean-Pierre Aubin,et al.  Estimates of the Duality Gap in Nonconvex Optimization , 1976, Math. Oper. Res..

[3]  S. T. Chung,et al.  A game-theoretic approach to power allocation in frequency-selective gaussian interference channels , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[4]  J. Rabaey,et al.  A Revenue Enhancing Stackelberg Game for Owners in Opportunistic Spectrum Access , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[5]  Yeheskel Bar-Ness,et al.  Spectrum Leasing via Distributed Cooperation in Cognitive Radio , 2008, 2008 IEEE International Conference on Communications.

[6]  Richard W. Cottle,et al.  Linear Complementarity Problem. , 1992 .

[7]  Zhong Fan,et al.  Noncooperative Equilibrium Solutions for Spectrum Access in Distributed Cognitive Radio Networks , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[8]  Shuguang Cui,et al.  Dynamic Resource Allocation in Cognitive Radio Networks , 2010, IEEE Signal Processing Magazine.

[9]  Zhi-Quan Luo,et al.  Dynamic Spectrum Management: Complexity and Duality , 2008, IEEE Journal of Selected Topics in Signal Processing.

[10]  Shuguang Cui,et al.  Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective , 2010, ArXiv.

[11]  Jianwei Huang,et al.  Revenue management for cognitive spectrum underlay networks: An interference elasticity perspective , 2009, 2009 15th Asia-Pacific Conference on Communications.

[12]  Wei Yu,et al.  Distributed multiuser power control for digital subscriber lines , 2002, IEEE J. Sel. Areas Commun..

[13]  Wei Yu,et al.  Optimal multiuser spectrum management for digital subscriber lines , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[14]  Zhi-Quan Luo,et al.  Analysis of Iterative Waterfilling Algorithm for Multiuser Power Control in Digital Subscriber Lines , 2006, EURASIP J. Adv. Signal Process..

[15]  D. Blackwell On a Theorem of Lyapunov , 1951 .

[16]  Bethany L. Nicholson,et al.  Mathematical Programs with Equilibrium Constraints , 2021, Pyomo — Optimization Modeling in Python.

[17]  W. Utschick,et al.  Distributed resource allocation schemes , 2009, IEEE Signal Processing Magazine.

[18]  G. Scutari,et al.  Flexible design of cognitive radio wireless systems , 2009, IEEE Signal Processing Magazine.

[19]  Wei Yu,et al.  Dual methods for nonconvex spectrum optimization of multicarrier systems , 2006, IEEE Transactions on Communications.

[20]  Sachin Agarwal,et al.  A stackelberg game for pricing uplink power in wide-band cognitive radio networks , 2008, 2008 47th IEEE Conference on Decision and Control.