Reinforcement Learning-Based Wireless Communications Against Jamming and Interference
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Learning-based anti-jamming communication strategy applies reinforcement learning algorithms for mobile users in wireless networks to achieve the optimal transmission policy against jamming and interference, without knowing the network model, radio channel model, and jamming model.
Due to the broadcast nature of radio propagation, wireless networks are vulnerable to jamming attacks, as jammers purposefully inject replayed or faked signals into wireless media to interrupt the ongoing radio transmissions between legitimate users (Xu et al., 2005; Xiao, 2015). With the pervasion of smart and programmable radio devices such as universal software radio peripherals (USRPs) (Rahbari et al., 2016), smart jammers choose to launch multiple types of attacks, such as eavesdropping and spoofing attacks, and select the jamming power, frequency, and time against the ongoing wireless transmissions (Trappe, 2015; Xiao et al., 2018a). Smart jammers can even analyze the...
This work is supported by the National Natural Science Foundation of China under Grant 61671396.
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