Fair and Efficient Resource Sharing for Selfish Cooperative Communication Networks Using Cooperative Game Theory

In this paper, a cooperative game is proposed to perform a fair and efficient resource allocation for the time division multiple access (TDMA) based cooperative communication networks. In the considered system, two selfish user nodes can act as a source as well as a potential relay for each other. A transmission node with energy limitation is willing to seek cooperative relaying only if the data-rate achieved through cooperation is not lower than that achieved without cooperation by consuming the same amount of energy. The cooperative strategy of a node can be defined as the number of data-symbols and power that it is willing to contribute for relaying purpose. We formulate this two-node fair and efficient resource sharing problem as a bargaining game. Since the Nash bargaining solution (NBS) to the game is computationally complex to obtain, a low-complexity algorithm to search the suboptimal NBS is proposed. Simulation results show that the NBS results are fair in that both nodes could experience better performance than if they work independently. And the NBS results are efficient in that the performance loss of the game to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.

[1]  Elza Erkip,et al.  User cooperation diversity. Part I. System description , 2003, IEEE Trans. Commun..

[2]  Raviraj S. Adve,et al.  Selection cooperation in multi-source cooperative networks , 2008, IEEE Transactions on Wireless Communications.

[3]  K. J. Ray Liu,et al.  Cooperative communications with relay-selection: when to cooperate and whom to cooperate with? , 2008, IEEE Transactions on Wireless Communications.

[4]  Gregory W. Wornell,et al.  Cooperative diversity in wireless networks: Efficient protocols and outage behavior , 2004, IEEE Transactions on Information Theory.

[5]  Andrea J. Goldsmith,et al.  Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[6]  Mohsen Guizani,et al.  A Cooperation Strategy Based on Nash Bargaining Solution in Cooperative Relay Networks , 2008, IEEE Transactions on Vehicular Technology.

[7]  Li Cong,et al.  Competitive Resource Sharing Based on Game Theory in Cooperative Relay Networks , 2009 .

[8]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  Zhu Han,et al.  Auction-Based Resource Allocation for Cooperative Communications , 2008, IEEE Journal on Selected Areas in Communications.

[10]  Zhu Han,et al.  Lifetime maximization via cooperative nodes and relay deployment in wireless networks , 2007, IEEE Journal on Selected Areas in Communications.

[11]  Larry J. Greenstein,et al.  Data throughputs using multiple-input multiple-output (MIMO) techniques in a noise-limited cellular environment , 2002, IEEE Trans. Wirel. Commun..

[12]  Zhu Han,et al.  Fair multiuser channel allocation for OFDMA networks using Nash bargaining solutions and coalitions , 2005, IEEE Transactions on Communications.

[13]  Liqiang Zhao,et al.  A Stackelberg game for resource allocation in multiuser cooperative transmission networks , 2011, Wirel. Commun. Mob. Comput..

[14]  Zhu Han,et al.  Distributed Relay Selection and Power Control for Multiuser Cooperative Communication Networks Using Stackelberg Game , 2009, IEEE Transactions on Mobile Computing.

[15]  Catherine Rosenberg,et al.  A game theoretic framework for bandwidth allocation and pricing in broadband networks , 2000, TNET.

[16]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.