User-Centric View of Jamming Games in Cognitive Radio Networks

Jamming games between a cognitive radio enabled secondary user (SU) and a cognitive radio enabled jammer are considered, in which end-user decision making is modeled using prospect theory (PT). More specifically, the interactions between a user and a smart jammer regarding their respective choices of transmit power are formulated as a game under the assumption that the end-user decision making under uncertainty does not follow the traditional objective assumptions stipulated by expected utility theory, but rather follows the subjective deviations specified by PT. Two PT-based static jamming games are formulated to describe how subjective SU and jammer choose their transmit power to maximize their individual signal-to-interference-plus-noise ratio (SINR)-based utilities under uncertainties regarding the opponent's actions and channel states, respectively. The Nash equilibria of the games are presented under various channel models and transmission costs. Moreover, a PT-based dynamic jamming game is presented to investigate the long-term interactions between a subjective and a smart jammer according to a Markov decision process with uncertainty on the SU's future actions and the channel variations. Simulation results show that the subjective view of an SU tends to exaggerate the jamming probabilities and decreases its transmission probability, thus reducing the average SINR. On the other hand, the subjectivity of a jammer tends to reduce its jamming probability, and thus increases the SU throughput.

[1]  H. Vincent Poor,et al.  Prospect theoretic analysis of anti-jamming communications in cognitive radio networks , 2014, 2014 IEEE Global Communications Conference.

[2]  G. Harrison,et al.  Expected utility theory and prospect theory: one wedding and a decent funeral , 2009 .

[3]  Ian F. Akyildiz,et al.  Multiagent jamming-resilient control channel game for cognitive radio ad hoc networks , 2012, 2012 IEEE International Conference on Communications (ICC).

[4]  Xi Fang,et al.  Coping with a Smart Jammer in Wireless Networks: A Stackelberg Game Approach , 2013, IEEE Transactions on Wireless Communications.

[5]  Eitan Altman,et al.  A Bayesian jamming game in an OFDM wireless network , 2012, 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt).

[6]  Peng Ning,et al.  Jamming-Resistant Collaborative Broadcast Using Uncoordinated Frequency Hopping , 2012, IEEE Transactions on Information Forensics and Security.

[7]  Narayan B. Mandayam,et al.  When Users Interfere with Protocols: Prospect Theory in Wireless Networks using Random Access and Data Pricing as an Example , 2014, IEEE Transactions on Wireless Communications.

[8]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

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

[10]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[11]  D. Prelec The Probability Weighting Function , 1998 .

[12]  David D. Clark,et al.  Tussle in cyberspace: defining tomorrow's Internet , 2002, IEEE/ACM Transactions on Networking.

[13]  H. Vincent Poor,et al.  Prospect Theoretic Analysis of Energy Exchange Among Microgrids , 2015, IEEE Transactions on Smart Grid.

[14]  Jianhua Ma,et al.  On Studying Relationship between Altruism and the Psychological Phenomenon of Self-Deception in Rational and Autonomous Networks , 2012, 2012 32nd International Conference on Distributed Computing Systems Workshops.

[15]  Walid Saad,et al.  Integrating energy storage into the smart grid: A prospect theoretic approach , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[17]  K. J. Ray Liu,et al.  Indirect Reciprocity Security Game for Large-Scale Wireless Networks , 2012, IEEE Transactions on Information Forensics and Security.

[18]  K. J. Ray Liu,et al.  Anti-Jamming Games in Multi-Channel Cognitive Radio Networks , 2012, IEEE Journal on Selected Areas in Communications.

[19]  Man Hon Cheung,et al.  Spectrum investment with uncertainty based on prospect theory , 2014, 2014 IEEE International Conference on Communications (ICC).

[20]  K. J. Ray Liu,et al.  An anti-jamming stochastic game for cognitive radio networks , 2011, IEEE Journal on Selected Areas in Communications.

[21]  Liang Xiao,et al.  Anti-Jamming Transmission Stackelberg Game With Observation Errors , 2015, IEEE Communications Letters.

[22]  Lin Chen,et al.  Fight jamming with jamming - A game theoretic analysis of jamming attack in wireless networks and defense strategy , 2011, Comput. Networks.

[23]  Tamer Basar,et al.  A dynamic transmitter-jammer game with asymmetric information , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[24]  Rajgopal Kannan,et al.  CSI Usage over Parallel Fading Channels under Jamming Attacks: A Game Theory Study , 2012, IEEE Transactions on Communications.

[25]  Quanyan Zhu,et al.  A Stochastic Game Model for Jamming in Multi-Channel Cognitive Radio Systems , 2010, 2010 IEEE International Conference on Communications.

[26]  Anthony Ephremides,et al.  Jamming games in wireless networks with incomplete information , 2011, IEEE Communications Magazine.

[27]  Eylem Ekici,et al.  Multiple access game with a cognitive jammer , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[28]  Narayan B. Mandayam,et al.  Prospects in a wireless random access game , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[29]  Narayan B. Mandayam,et al.  Impact of end-user decisions on pricing in wireless networks , 2014, CISS.

[30]  Takashi Okuda,et al.  A Design Method of Local Community Network Service Systems with Ad-Hoc Network Technology , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[31]  Narayan B. Mandayam,et al.  Impact of end-user decisions on pricing in wireless networks under a multiple-user-single-provider setting , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[32]  Moshe Ben-Akiva,et al.  Adaptive route choices in risky traffic networks: A prospect theory approach , 2010 .

[33]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .