A Learning-Aided Intermittent Cooperative Jamming Scheme for Nonslotted Wireless Transmission in an IoT System

The boom of the Internet of Things (IoT) has exposed many security issues in recent years. Cooperative jamming, including the continuous jamming strategy (CJS) and intermittent jamming strategy (IJS), is an effective approach toward secure wireless communication in the physical layer. CJS used to be a primary physical-layer security technology that sends cooperative jamming signals at the expense of energy consumption. Different from CJS, IJS is more energy efficient. The feasibility of IJS has been proved in a slotted scenario, which motivates us to design IJS in a nonslotted scenario. In this article, we discuss the feasibility of IJS for a nonslotted transmission IoT system and formulate an optimization problem based on a sense-harvest-jam policy. This problem is to find the optimal matching precision between durations of artificial noise and legitimate signals. To solve this problem, we exploit a backpropagation-neural-network model to analyze jamming duration proportion and derive the optimal proportion for the binary phase-shift keying modulation. Finally, we design a matching precision optimization algorithm to achieve the optimal nonslotted secure transmission. Simulation results on jamming efficiency demonstrate that the proposed IJS has preferable secure performance than the CJS under energy constraints.