Adaptive resource allocation in jamming teams using game theory

In this work, we study the problem of power allocation and adaptive modulation in teams of decision makers. We consider the special case of two teams with each team consisting of two mobile agents. Agents belonging to the same team communicate over wireless ad hoc networks, and they try to split their available power between the tasks of communication and jamming the nodes of the other team. The agents have constraints on their total energy and instantaneous power usage. The cost function adopted is the difference between the rates of erroneously transmitted bits of each team. We model the adaptive modulation problem as a zero-sum matrix game which in turn gives rise to a a continuous kernel game to handle power control. Based on the communications model, we present sufficient conditions on the physical parameters of the agents for the existence of a pure strategy saddle-point equilibrium (PSSPE).

[1]  Guevara Noubir,et al.  Linear programming models for jamming attacks on network traffic flows , 2008, WiOpt 2008.

[2]  A. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1996, Proceedings of Vehicular Technology Conference - VTC.

[3]  Tamer Basar,et al.  Power allocation in team jamming games in wireless ad hoc networks , 2011, VALUETOOLS.

[4]  Andrea J. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1997, IEEE Trans. Commun..

[5]  T. Başar,et al.  Dynamic Noncooperative Game Theory, 2nd Edition , 1998 .

[6]  Tamer Basar,et al.  Graph-theoretic approach for connectivity maintenance in mobile networks in the presence of a jammer , 2010, 49th IEEE Conference on Decision and Control (CDC).

[7]  Rachid El Azouzi,et al.  Introducing hierarchy in energy games , 2009, IEEE Transactions on Wireless Communications.

[8]  Tamer A. ElBatt,et al.  Joint scheduling and power control for wireless ad hoc networks , 2002, IEEE Transactions on Wireless Communications.

[9]  Guevara Noubir,et al.  Linear programming models for jamming attacks on network traffic flows , 2008, 2008 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops.

[10]  Mérouane Debbah,et al.  Power allocation games in wireless networks of multi-antenna terminals , 2010, Telecommun. Syst..

[11]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[12]  Björn E. Ottersten,et al.  Minimum BER linear transceivers for MIMO channels via primal decomposition , 2005, IEEE Transactions on Signal Processing.

[13]  Andrew McLennan,et al.  Gambit: Software Tools for Game Theory , 2006 .

[14]  Sampath Kannan,et al.  Randomized Pursuit-Evasion with Local Visibility , 2006, SIAM J. Discret. Math..

[15]  Andrea J. Goldsmith,et al.  Design challenges for energy-constrained ad hoc wireless networks , 2002, IEEE Wirel. Commun..

[16]  Francisco Facchinei,et al.  Distributed Power Allocation With Rate Constraints in Gaussian Parallel Interference Channels , 2007, IEEE Transactions on Information Theory.

[17]  S Bhattacharya,et al.  Game-theoretic analysis of an aerial jamming attack on a UAV communication network , 2010, Proceedings of the 2010 American Control Conference.

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

[19]  J. Tobias,et al.  Signal Jamming Mediates Sexual Conflict in a Duetting Bird , 2009, Current Biology.

[20]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[21]  D. Luenberger Optimization by Vector Space Methods , 1968 .