DocumentCode :
1479143
Title :
An anti-jamming stochastic game for cognitive radio networks
Author :
Wang, Beibei ; Wu, Yongle ; Liu, K. J Ray ; Clancy, T. Charles
Author_Institution :
Corp. R&D, Qualcomm Inc., San Diego, CA, USA
Volume :
29
Issue :
4
fYear :
2011
fDate :
4/1/2011 12:00:00 AM
Firstpage :
877
Lastpage :
889
Abstract :
Various spectrum management schemes have been proposed in recent years to improve the spectrum utilization in cognitive radio networks. However, few of them have considered the existence of cognitive attackers who can adapt their attacking strategy to the time-varying spectrum environment and the secondary users´ strategy. In this paper, we investigate the security mechanism when secondary users are facing the jamming attack, and propose a stochastic game framework for anti-jamming defense. At each stage of the game, secondary users observe the spectrum availability, the channel quality, and the attackers´ strategy from the status of jammed channels. According to this observation, they will decide how many channels they should reserve for transmitting control and data messages and how to switch between the different channels. Using the minimax-Q learning, secondary users can gradually learn the optimal policy, which maximizes the expected sum of discounted payoffs defined as the spectrum-efficient throughput. The proposed stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which only maximizes each stage´s payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.
Keywords :
cognitive radio; jamming; minimax techniques; radio spectrum management; stochastic games; anti-jamming stochastic game; cognitive radio networks; jammed channels; minimax-Q learning; myopic learning; spectrum management; time-varying spectrum; Cognitive radio; Games; Jamming; Stochastic processes; Switches; Throughput; Security mechanism; cognitive radio networks; game theory; reinforcement learning; spectrum management;
fLanguage :
English
Journal_Title :
Selected Areas in Communications, IEEE Journal on
Publisher :
ieee
ISSN :
0733-8716
Type :
jour
DOI :
10.1109/JSAC.2011.110418
Filename :
5738229
Link To Document :
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