Navneet Garg

Work place: Department of Electrical Engineering IIT Kanpur, 208016, India



Research Interests:


Navneet Garg: Post-graduate student for doctor degree in the department of Electrical Engineering of Indian Institute of Technology (IIT) Kanpur. He has received his M. Tech degree from ABV-Indian Institute of Information Technology & Management (IIITM) Gwalior in Digital communication.

Author Articles
Collaborative Anti-jamming in Cognitive Radio Networks Using Minimax-Q Learning

By Sangeeta Singh Aditya Trivedi Navneet Garg

DOI:, Pub. Date: 8 Sep. 2013

Cognitive radio is an efficient technique for realization of dynamic spectrum access. Since in the cognitive radio network (CRN) environment, the secondary users (SUs) are susceptible to the random jammers, the security issue of the SU’s channel access becomes crucial for the CRN framework. The rapidly varying spectrum dynamics of CRN along with the jammer’s actions leads to challenging scenario. Stochastic zero-sum game and Markov decision process (MDP) are generally used to model the scenario concerned. To learn the channel dynamics and the jammer’s strategy the SUs use reinforcement learning (RL) algorithms, like Minimax-Q learning. In this paper, we have proposed the multi-agent multi-band collaborative anti-jamming among the SUs to combat single jammer using the Minimax-Q learning algorithm. The SUs collaborate via sharing the policies or episodes. Here, we have shown that the sharing of the learned policies or episodes enhances the learning probability of SUs about the jammer’s strategies but reward reduces as the cost of communication increases. Simulation results show improvement in learning probability of SU by using collaborative anti-jamming using Minimax-Q learning over single SU fighting the jammer scenario.

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