• 제목/요약/키워드: Wolf Q Learning Algorithm

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Q Learning MDP Approach to Mitigate Jamming Attack Using Stochastic Game Theory Modelling With WQLA in Cognitive Radio Networks

  • Vimal, S.;Robinson, Y. Harold;Kaliappan, M.;Pasupathi, Subbulakshmi;Suresh, A.
    • Journal of Platform Technology
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    • 제9권1호
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    • pp.3-14
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    • 2021
  • Cognitive Radio network (CR) is a promising paradigm that helps the unlicensed user (Secondary User) to analyse the spectrum and coordinate the spectrum access to support the creation of common control channel (CCC). The cooperation of secondary users and broadcasting between them is done through transmitting messages in CCC. In case, if the control channels may get jammed and it may directly degrade the network's performance and under such scenario jammers will devastate the control channels. Hopping sequences may be one of the predominant approaches and it may be used to fight against this problem to confront jammer. The jamming attack can be alleviated using one of the game modelling approach and in this proposed scheme stochastic games has been analysed with more single users to provide the flexible control channels against intrusive attacks by mentioning the states of each player, strategies ,actions and players reward. The proposed work uses a modern player action and better strategic view on game theoretic modelling is stochastic game theory has been taken in to consideration and applied to prevent the jamming attack in CR network. The selection of decision is based on Q learning approach to mitigate the jamming nodes using the optimal MDP decision process