  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) and the associated power allocation very
challenging. Existing methods demonstrated engaging performance, but with high
computational complexity. In this paper, we propose a model-driven deep
learning (DL)-based approach for SN selection. 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 and prove its convergence. The proposed method achieves lower
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 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.
