Game theoretic outage compensation in next generation mobile networks

Changes in network dynamics (e.g., link failure, congestion, buffer overflow and so on) may render ubiquitous service access untenable in the Next Generation Mobile Network (NGMN). Since the commercial viability of a network in a competitive market depends on the perceived user satisfaction, to atone for the loss of the guaranteed service access, it is desirable to compensate the users either with future quality-of-service (QoS) enhancements and/or price reductions. Focusing on the price reduction aspect, this paper proposes a non-cooperative game theory based compensation algorithm that derives the best outage compensation (i.e., price reduction for the outage period t) over different service types. Taking into account all-IP based applications in the future, the service types are categorized into different classes such as flat rate based (i.e., cents for the entire session(s)), time based (i.e., cents per minute), volume based (i.e., cents per MB), etc., whereupon the compensation algorithm is translated into an n-player game, based on the current subscription profiles. With step sized cost reductions, the selection of the outage compensation is governed by the Nash equilibrium points and fairly allocates cost reduction among the ongoing service types.

[1]  I. Fisher.  Cournot and Mathematical Economics , .

[2]  J. Nash.  NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[3]  William Poundstone,et al.  Prisoner's Dilemma: John Von Neumann, Game Theory and the Puzzle of the Bomb , 1992 .

[4]  Victor O. K. Li,et al.  Utility-oriented adaptive QoS and bandwidth allocation in wireless networks , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[5]  Sajal K. Das,et al.  User-satisfaction based differentiated services for wireless data networks , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[6]  Fernando Paganini,et al.  Game theoretic approach to power control in cellular CDMA , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[7]  I. Glicksberg.  A FURTHER GENERALIZATION OF THE KAKUTANI FIXED POINT THEOREM, WITH APPLICATION TO NASH EQUILIBRIUM POINTS , 1952 .

[8]  Gerard Debreu,et al.  A Social Equilibrium Existence Theorem* , 1952, Proceedings of the National Academy of Sciences.

[9]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[10]  Abbas Jamalipour,et al.  On designing issues of the next generation mobile network , 2007, IEEE Network.

[11]  R. Srikant,et al.  Revenue-maximizing pricing and capacity expansion in a many-users regime , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[12]  Von Seggern,et al.  CRC standard curves and surfaces , 1993 .

[13]  Roy D. Yates,et al.  Service outage based power and rate allocation , 2003, IEEE Trans. Inf. Theory.

[14]  Frank Kelly,et al.  Models for a self–managed Internet , 2000, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[15]  Adam Brandenburger,et al.  Knowledge and Equilibrium in Games , 1992 .

[16]  Dusit Niyato,et al.  Radio resource management games in wireless networks: an approach to bandwidth allocation and admission control for polling service in IEEE 802.16 [Radio Resource Management and Protocol Engineering for IEEE 802.16] , 2007, IEEE Wireless Communications.

[17]  T. Basar,et al.  Differentiated Internet pricing using a hierarchical network game model , 2004, Proceedings of the 2004 American Control Conference.

[18]  J. Nash.  THE BARGAINING PROBLEM , 1950, Classics in Game Theory.