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      Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning

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          Abstract

          By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.

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          Cognitive radio sensor networks

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            A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks

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              A Survey of Security Challenges in Cognitive Radio Networks: Solutions and Future Research Directions

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                Author and article information

                Journal
                19 December 2017
                Article
                1712.06793
                aca671d7-6f47-4488-9549-ba0c3296590d

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CR cs.LG

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