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The jamming attack is one of the most severe threats in cognitive radio networks, because it can lead to network degradation and even denial of service. However, a cognitive radio can exploit its ability of dynamic spectrum access and its learning capabilities to avoid jammed channels. In this paper, we study how Q-learning can be used to learn the jammer(More)
The emergence of new wireless services and the growing demand for wireless communications are creating a spectrum shortage problem. Moreover, the current technique of static frequency allocation leads to inefficiency utilization of the available spectrum. Cognitive radio (CR) and dynamic spectrum management (DSM) concepts, aim to solve this imbalance(More)
In this paper, we study the power allocation problem for a cognitive radio in the presence of a smart jammer over parallel Gaussian channels. The objective of the jammer is to minimize the total capacity achievable by the cognitive radio. We model the interaction between the two players as a zero-sum game, for which we derive the saddle point closed form(More)
Cognitive jammers are able to deploy advanced strategies that degrade the performance of cognitive radio user communications. In this paper, we study the problem of power allocation in cognitive radio user and jammer games, over parallel Gaussian channels. We model the interaction between a communicator (a transmitter-receiver pair) and a jammer using(More)
We model the power allocation interaction between a cognitive radio and a jammer as a two-player zero-sum game. First, we determine the power allocation strategy for the cognitive radio using a modified version of the Q-learning algorithm against fixed jamming strategies. The learned anti-jamming strategy will be compared to the common waterfilling(More)
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