Analysis of an on-off jamming situation as a dynamic game

The process of communication jamming can be modeled as a two-person zero-sum noncooperative dynamic game played between a communicator (a transmitter-receiver pair) and a jammer. We consider a one-way time-slotted packet radio communication link in the presence of a jammer, where the data rate is fixed and (1) in each slot, the communicator and jammer choose their respective power levels in a random fashion from a zero and a positive value; (2) both players are subject to temporal energy constraints which account for protection of the communicating and jamming transmitters from overheating. The payoff function is the time average of the mean payoff per slot. The game is solved for certain ranges of the players' transmitter parameters. Structures of steady-state solutions to the game are also investigated. The general behavior of the players' strategies and payoff increment is found to depend on a parameter related to the payoff matrix, which me call the payoff parameter, and the transmitters' parameters. When the payoff parameter is lower than a threshold, the optimal steady-state strategies are mixed and the payoff increment constant over time, whereas when it is greater than the threshold, the strategies are pure, and the payoff increment exhibits oscillatory behavior.

[1]  Charles R. Baker,et al.  Coding capacity for a class of additive channels , 1989, IEEE Trans. Inf. Theory.

[2]  Lars-Henning Zetterberg Signal detection under noise interference in a game situation , 1962, IRE Trans. Inf. Theory.

[3]  Ian W. McKeague,et al.  The coding capacity of mismatched Gaussian channels , 1986, IEEE Trans. Inf. Theory.

[4]  Imre Csiszár,et al.  Capacity and decoding rules for classes of arbitrarily varying channels , 1989, IEEE Trans. Inf. Theory.

[5]  Robert J. McEliece,et al.  A Study of Optimal Abstract Jamming Strategies vs. Noncoherent MFSK , 1983, MILCOM 1983 - IEEE Military Communications Conference.

[6]  Tamer Basar,et al.  Optimum linear causal coding schemes for Gaussian stochastic processes in the presence of correlated jamming , 1989, IEEE Trans. Inf. Theory.

[7]  Wayne E. Stark,et al.  On the capacity of channels with unknown interference , 1989, IEEE Trans. Inf. Theory.

[8]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Stochastic Control , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Robert A. Scholtz,et al.  On the steady state solution of a two-by-two dynamic jamming game with cumulative power constraints , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[10]  Tamer Basar,et al.  With the Capacity 0.461(bits) and the Optimal Opd Being 'q = , 1998 .

[11]  Morton D. Davis,et al.  Games of Strategy: Theory and Application. , 1962 .

[12]  Ranjan K. Mallik,et al.  On the existence of a steady state solution to a dynamic jamming game , 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

[13]  RUDOLF AHLSWEDE Arbitrarily varying channels with states sequence known to the sender , 1986, IEEE Trans. Inf. Theory.

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

[15]  M. Degroot Game Theory: Mathematical Models of Conflict , 1980 .

[16]  Thomas H. E. Ericson A min-max theorem for antijamming group codes , 1984, IEEE Trans. Inf. Theory.

[17]  Robert A. Scholtz,et al.  A simple dynamic jamming game , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[18]  Imre Csisz Arbitrarily Varying Channels with General Alphabets and States , 1992 .

[19]  Prakash Narayan,et al.  The capacity of a vector Gaussian arbitrarily varying channel , 1988, IEEE Trans. Inf. Theory.

[20]  A. Nayfeh,et al.  Applied nonlinear dynamics : analytical, computational, and experimental methods , 1995 .

[21]  Hans-Martin Wallmeier,et al.  Games with informants , 1988 .

[22]  Ranjan K. Mallik,et al.  Cyclotomic cosets and steady state solutions to a dynamic jamming game , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[23]  Prakash Narayan,et al.  Gaussian arbitrarily varying channels , 1987, IEEE Trans. Inf. Theory.

[24]  Tamer Basar,et al.  A complete characterization of minimax and maximin encoder- decoder policies for communication channels with incomplete statistical description , 1985, IEEE Trans. Inf. Theory.

[25]  Evaggelos Geraniotis,et al.  Signal detection games with power constraints , 1994, IEEE Trans. Inf. Theory.

[26]  Imre Csiszár,et al.  Capacity of the Gaussian arbitrarily varying channel , 1991, IEEE Trans. Inf. Theory.

[27]  H. M. Wallmeier,et al.  Games with informants: an information-theoretical approach towards a game-theoretical problem , 1983 .

[28]  Ranjan K. Mallik,et al.  On grid solutions of a dynamic jamming game , 1997, Proceedings of IEEE International Symposium on Information Theory.

[29]  R. McEliece,et al.  Some Information Theoretic Saddlepoints , 1985 .

[30]  Charles R. Baker,et al.  Capacity of the mismatched Gaussian channel , 1987, IEEE Trans. Inf. Theory.