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Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid

Autor: Liu, An, Zhou, Yufan, Xu, Wenkang

We investigate the problem of recovering a structured sparse signal from a linear observation model with an uncertain dynamic grid in the sensing matrix. The state-of-the-art expectation maximization based compressed sensing (EM-CS) methods, such as

Externí odkaz: http://arxiv.org/abs/2407.16947

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Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP

Autor: Xu, Wenkang, Liu, An, Zhao, Min-jian

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where near-field propagation and spatial non-stationary effects exist. In this case, eac

Externí odkaz: http://arxiv.org/abs/2405.04027

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Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation

Autor: Xu, Wenkang, Liu, An, Zhou, Bingpeng, Zhao, Minjian

For many practical applications in wireless communications, we need to recover a structured sparse signal from a linear observation model with dynamic grid parameters in the sensing matrix. Conventional expectation maximization (EM)-based compressed

Externí odkaz: http://arxiv.org/abs/2307.09149

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

Autor: Xu, Wenkang, Xiao, Yongbo, Liu, An, Lei, Ming, Zhao, Minjian

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 scatter

Externí odkaz: http://arxiv.org/abs/2302.02587

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