arXiv subjects

Pu Xie

Publications and source records attributed to Pu Xie.

2 recordsLinked to original sources

arXiv2024

Physical-Layer Security for 6G: Safe Jamming against Malicious Sensing

The integration of sensing, communications, array signal processing, etc. into 6G mobile networks has ushered in an era of heightened situational awareness. However, this progress brings forth significant concerns regarding privacy and security, particularly due to the proliferation of devices equipped with radar-like sensing capability, including malicious ones. In response, this paper proposes a novel actor-critic (AC) method-based frequency selection scheme for noise jamming, in order to effectively counter malicious multifunction frequency agility sensing. In the meanwhile, to mitigate potential interference (caused by sidelobes of the jamming beam) with uplink transmissions conducted by legitimate but non-cooperative users, a robust action correction mechanism, which is capable of learning and predicting the spectrum utilization state, is proposed to find feasible but near-optimal frequency configuration for jamming. Numerical results demonstrate that benefiting from the robust action correction mechanism, the proposed AC-based safe jamming can not only make the malicious sensing device continuously get stuck in the searching mode but also guarantee minimal disruption to the legitimate non-cooperative users.

cs.IT↗

arXiv2023

Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there are no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

eess.SP↗