Maneuvering target tracking will be an important service of future wireless
networks to assist innovative applications such as intelligent transportation.
However, tracking maneuvering targets by cellular networks faces many
challenges. For example, the dense network and high-speed targets make the
selection of the sensing nodes (SNs), e.g., base stations, and the associated
power allocation very difficult, given the stringent latency requirement of
sensing applications. Existing methods have demonstrated engaging tracking
performance, but with very high computational complexity. In this paper, we
propose a model-driven deep learning approach for SN selection to meet the
latency requirement. To this end, we first propose an iterative SN selection
method by jointly exploiting the majorization-minimization (MM) framework and
the alternating direction method of multipliers (ADMM). Then, we unfold the
iterative algorithm as a deep neural network (DNN) and prove its convergence.
The proposed model-driven method has a low computational complexity, because
the number of layers is less than the number of iterations required by the
original algorithm, and each layer only involves simple matrix-vector
additions/multiplications. Finally, we propose an efficient power allocation
method based on fixed point (FP) water filling (WF) and solve the joint SN
selection and power allocation problem under the alternative optimization
framework. Simulation results show that the proposed method achieves better
performance than the conventional optimization-based methods with much lower
computational complexity