Reverse-Engineering MAC: A Non-Cooperative Game Model

This paper reverse-engineers backoff-based random-access MAC protocols in ad-hoc networks. We show that the contention resolution algorithm in such protocols is implicitly participating in a non-cooperative game. Each link attempts to maximize a selfish local utility function, whose exact shape is reverse-engineered from the protocol description, through a stochastic subgradient method in which the link updates its persistence probability based on its transmission success or failure. We prove that existence of a Nash equilibrium is guaranteed in general. Then we establish the minimum amount of backoff aggressiveness needed, as a function of density of active users, for uniqueness of Nash equilibrium and convergence of the best response strategy. Convergence properties and connection with the best response strategy are also proved for variants of the stochastic-subgradient-based dynamics of the game. Together with known results in reverse-engineering TCP and BGP, this paper further advances the recent efforts in reverse-engineering layers 2-4 protocols. In contrast to the TCP reverse-engineering results in earlier literature, MAC reverse-engineering highlights the non-cooperative nature of random access.

[1]  A. Robert Calderbank,et al.  Utility-Optimal Medium Access Control: Reverse and Forward Engineering , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[2]  Stephen B. Wicker,et al.  Stability of multipacket slotted Aloha with selfish users and perfect information , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  Richard J. La,et al.  Utility-based rate control in the Internet for elastic traffic , 2002, TNET.

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

[5]  A. Robert Calderbank,et al.  Utility-optimal random-access control , 2007, IEEE Transactions on Wireless Communications.

[6]  D. M. Topkis Equilibrium Points in Nonzero-Sum n-Person Submodular Games , 1979 .

[7]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[8]  Roger Wattenhofer,et al.  The Complexity of Connectivity in Wireless Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[9]  Steven H. Low,et al.  A duality model of TCP and queue management algorithms , 2003, TNET.

[10]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[11]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[12]  R. Abraham,et al.  Manifolds, Tensor Analysis, and Applications , 1983 .

[13]  Derong Liu The Mathematics of Internet Congestion Control , 2005, IEEE Transactions on Automatic Control.

[14]  Yuri Ermoliev,et al.  Numerical techniques for stochastic optimization , 1988 .

[15]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[16]  David D. Yao,et al.  S-modular games, with queueing applications , 1995, Queueing Syst. Theory Appl..

[17]  Peter Marbach,et al.  Price-based rate control in random access networks , 2005, IEEE/ACM Transactions on Networking.

[18]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .

[19]  Antonio Alonso Ayuso,et al.  Introduction to Stochastic Programming , 2009 .

[20]  Gordon T. Wilfong,et al.  The stable paths problem and interdomain routing , 2002, TNET.

[21]  Jean C. Walrand,et al.  Fair end-to-end window-based congestion control , 2000, TNET.