Efficient Sensing Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks

A model-driven deep learning approach is proposed for efficient sensing node selection and power allocation to track maneuvering targets in perceptive mobile networks, achieving better performance with lower computational complexity compared to conventional optimization-based methods.