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arXiv · 2302.02587

Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field

Wenkang Xu·Yongbo Xiao·An Liu·Ming Lei·Minjian Zhao
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Abstract

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.

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Wenkang Xu, Yongbo Xiao, An Liu, Ming Lei, Minjian Zhao. 2023-02-06. Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field. https://arxiv.org/abs/2302.02587

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