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.

翻译：机动目标跟踪将成为未来无线网络的重要服务，以支持智能交通等创新应用。然而，利用蜂窝网络跟踪机动目标面临诸多挑战。例如，在感知应用对时延的苛刻要求下，密集网络与高速目标使得感知节点（如基站）的选择及相关功率分配变得极为困难。现有方法虽展现出优异的跟踪性能，但计算复杂度极高。本文提出一种模型驱动的深度学习方法用于感知节点选择，以满足时延要求。为此，我们首先通过联合利用最小化最大化（MM）框架和交替方向乘子法（ADMM）提出一种迭代式感知节点选择方法。随后，我们将该迭代算法展开为深度神经网络（DNN）并证明其收敛性。所提出的模型驱动方法计算复杂度低，因为网络层数少于原始算法所需的迭代次数，且每层仅涉及简单的矩阵-向量加/乘法。最后，我们提出一种基于定点（FP）注水（WF）的高效功率分配方法，并在交替优化框架下解决了联合感知节点选择与功率分配问题。仿真结果表明，所提方法在保持更优性能的同时，计算复杂度显著低于传统基于优化的方法。