Application of MIMO Neural Networks in Wireless Signal Monitoring

The integration of IoT technology in wireless signal monitoring holds substantial potential for both military and civilian applications, including signal reconnaissance, anti-jamming, and device identification. Traditional modulation recognition methods, based on likelihood and feature extraction, face limitations in computational complexity and reliance on expert knowledge. In contrast, ML and DL models like Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) offer superior performance without manual feature extraction. We propose a MIMO neural network model for automatic modulation classification and direction of arrival (DOA) estimation. The system, detailed through functional block diagrams, includes components such as antenna arrays, analog preprocessing units, radio signal receivers, digital signal processors, MIMO neural networks, data processors, and display units. This model simultaneously performs modulation classification and DOA estimation, providing an efficient and cost-effective solution for real-time signal monitoring.