Dqn-Based Power Control For Iot Transmission Against Jamming

Internet of Things (IoTs) have to address jammers, with goal to interrupt the communication of the energy constrained IoT devices and sometimes even cause denial-of-service attacks. In this paper, we propose a deep reinforcement learning based power control scheme for IoT devices to improve the transmission efficiency and save energy. This scheme depends on the current IoT transmission status and the jamming strength and applies deep Q-network (DQN) to determine the transmit power without being aware of the IoT topology and the jamming model. This scheme is implemented on the universal software radio peripherals for the anti-jamming communication performance evaluation. Experimental results show that this scheme improves the signal-to-interference-plus-noise of the IoT signals compared with the benchmark Q-learning based power control scheme against jamming.