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

Core Concepts
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.
The content presents a model-driven deep learning (DL) approach for sensing node (SN) selection and power allocation to track maneuvering targets in perceptive mobile networks (PMNs). Key highlights: An iterative SN selection method is proposed by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). The iterative MM-ADMM algorithm is unfolded into a deep neural network, named deep alternating network (DAN), to reduce computational complexity. The convergence of DAN is analyzed and proven. An efficient power allocation method based on fixed-point water-filling is proposed and combined with the SN selection under an alternative optimization framework. Simulation results show the proposed method achieves better performance than conventional optimization-based methods with much lower computational complexity.
The content does not provide specific numerical data or metrics. It focuses on the algorithmic development and theoretical analysis.
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Deeper Inquiries

What are the potential applications of the proposed perceptive mobile network beyond intelligent transportation systems

The proposed perceptive mobile network can have various applications beyond intelligent transportation systems. One potential application is in environmental monitoring, where the network can be utilized to track and analyze air quality, pollution levels, and other environmental factors in real-time. Additionally, in healthcare, the network can be used for remote patient monitoring, tracking vital signs, and providing timely medical assistance. Furthermore, in industrial settings, the network can assist in asset tracking, inventory management, and predictive maintenance, optimizing operations and reducing downtime.

How can the model-driven DL approach be extended to handle uncertainties in the target motion model and signal propagation

To handle uncertainties in the target motion model and signal propagation, the model-driven DL approach can be extended by incorporating probabilistic models and Bayesian techniques. By integrating uncertainty quantification methods such as Monte Carlo simulations or Gaussian processes, the model can account for variations in the target's motion behavior and the signal's propagation characteristics. Additionally, ensemble learning techniques can be employed to capture the variability in the data and provide more robust predictions in the presence of uncertainties.

Can the proposed techniques be generalized to track multiple types of targets (e.g., cooperative and non-cooperative) simultaneously in the perceptive mobile network

The proposed techniques can be generalized to track multiple types of targets simultaneously in the perceptive mobile network by extending the SN selection and power allocation algorithms to accommodate different target characteristics. By incorporating feature extraction methods and target classification models, the network can identify and track various types of targets, including cooperative and non-cooperative entities. Additionally, by leveraging multi-task learning and transfer learning approaches, the network can adapt to different target behaviors and optimize the tracking performance for diverse target types.