Signal Processing of Internet of Vehicles Based on Intelligent Interference

Abstract With the rapid development of in-vehicle electronic technology and artificial intelligence, Internet of Vehicles (IoV) technology, as an effective integration of the two, greatly reduces the probability of road traffic accidents. However, the current IoV system is not perfect for the control process of abnormal vehicles. Therefore, in order to strengthen the management and control of abnormal vehicles in the IoV, it is extremely necessary to propose a method for interfering with IoV signals. Among the current popular intelligent interference methods, most of them rely on the prior knowledge of the signal to deduce the best interference waveform. However, These methods rely too much on prior knowledge to be practically applicable. Therefore, in view of the shortcomings of the current communication interference technology, this study proposes an interference waveform generation technology based on convolutional autoencoders. The convolutional autoencoder was used to change the features on the fully connected layer to generate an interference waveform that is very similar to the received signal waveform, and the interference waveform is sent to the receiver to realize the control of the IoV signal. The simulation results show that the interference waveform generation technology proposed in this study can make the bit error rate (BER) reach 38.4\% within the signal-to-interference ratio (SIR) from -10dB to -15dB.