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A New Anti-Jamming Strategy Based on Deep Reinforcement Learning for MANET

Abstract: Mobile Ad-hoc Network (MANET) is a self-configuring network that is widely used but vulnerable to the malicious jammers in practice. In this paper, we consider a jamming channel problem in MANET where a jammer intermittently interrupts the communication channels and the transmitter needs to determine which time slot to send data in order to avoid the interruption. Learning from the historical experience, a Deep Q-Network (DQN) based approach is proposed to generate transmission decisions at the transmitter. In addition, a variant of DQN, termed adaptive DQN, is introduced to cope with the change of jamming conditions. The simulation results demonstrate that the proposed scheme can learn an optimal policy to guide the transmitter to avoid jamming more quickly and efficiently than a Q-learning baseline. Moreover, the effectiveness and robustness of the adaptive DQN is also numerically verified.

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A New Anti-Jamming Strategy Based on Deep Reinforcement Learning for MANET  

A New Anti-Jamming Strategy Based on Deep Reinforcement Learning for MANET IEEE PROJECTS 2019-2020 TITLE LIST Call Us: +91-7806844441,814419...

A New Anti-Jamming Strategy Based on Deep Reinforcement Learning for MANET  

A New Anti-Jamming Strategy Based on Deep Reinforcement Learning for MANET IEEE PROJECTS 2019-2020 TITLE LIST Call Us: +91-7806844441,814419...

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