Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks

We consider a network in which several service providers offer wireless access to their respective subscribed customers through potentially multihop routes. If providers cooperate by jointly deploying and pooling their resources, such as spectrum and infrastructure (e.g., base stations) and agree to serve each others' customers, their aggregate payoffs, and individual shares, may substantially increase through opportunistic utilization of resources. The potential of such cooperation can, however, be realized only if each provider intelligently determines with whom it would cooperate, when it would cooperate, and how it would deploy and share its resources during such cooperation. Also, developing a rational basis for sharing the aggregate payoffs is imperative for the stability of the coalitions. We model such cooperation using the theory of transferable payoff coalitional games. We show that the optimum cooperation strategy, which involves the acquisition, deployment, and allocation of the channels and base stations (to customers), can be computed as the solution of a concave or an integer optimization. We next show that the grand coalition is stable in many different settings, i.e., if all providers cooperate, there is always an operating point that maximizes the providers' aggregate payoff, while offering each a share that removes any incentive to split from the coalition. The optimal cooperation strategy and the stabilizing payoff shares can be obtained in polynomial time by respectively solving the primals and the duals of the above optimizations, using distributed computations and limited exchange of confidential information among the providers. Numerical evaluations reveal that cooperation substantially enhances individual providers' payoffs under the optimal cooperation strategy and several different payoff sharing rules.

[1]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[2]  Yixin Chen A Duality Theory with Zero Duality Gap for Nonlinear Programming , 2007 .

[3]  R. Srikant,et al.  Regulated Maximal Matching: A Distributed Scheduling Algorithm for Multi-Hop Wireless Networks With Node-Exclusive Spectrum Sharing , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[4]  J. George Shanthikumar,et al.  Convex separable optimization is not much harder than linear optimization , 1990, JACM.

[5]  Vincent Conitzer,et al.  Complexity of determining nonemptiness of the core , 2003, EC '03.

[6]  Zhu Han,et al.  Cooperative Game Theory for Distributed Spectrum Sharing , 2007, 2007 IEEE International Conference on Communications.

[7]  A. Rubinstein,et al.  A Course in Game Theory , 1995 .

[8]  Ness B. Shroff,et al.  The impact of imperfect scheduling on cross-layer rate control in wireless networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[9]  Evangelos Markakis,et al.  On the core of the multicommodity flow game , 2003, EC '03.

[10]  L. Shapley,et al.  The assignment game I: The core , 1971 .

[11]  H. Scarf The Core of an N Person Game , 1967 .

[12]  A. Kumar,et al.  A coalitional game model for spectrum pooling in wireless data access networks , 2008, 2008 Information Theory and Applications Workshop.

[13]  Marco A. López,et al.  On the core of transportation games , 2001, Math. Soc. Sci..

[14]  A. Kumar,et al.  A coalitional game framework for cooperative secondary spectrum access , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[15]  Bruce E. Hajek,et al.  Link scheduling in polynomial time , 1988, IEEE Trans. Inf. Theory.

[16]  N.B. Mandayam,et al.  Coalitional Games in Cooperative Radio Networks , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[17]  Dov Samet,et al.  On the Core and Dual Set of Linear Programming Games , 1984, Math. Oper. Res..