Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915219077332992 |
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| author | Xu, Wenkang Liu, An Zhao, Min-jian Caire, Giuseppe Wu, Yik-Chung |
| author_facet | Xu, Wenkang Liu, An Zhao, Min-jian Caire, Giuseppe Wu, Yik-Chung |
| contents | This work considers a spatial non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationary, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and a novel dynamic alternating maximum a posteriori (DA-MAP) framework is developed to solve the problem. The DA-MAP contains four basic modules: channel estimation module, VR detection module, grid update module, and temporal correlated module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computational intensive matrix inverse each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulations show that the proposed method can achieve a significant channel tracking performance while achieving low computational overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking Xu, Wenkang Liu, An Zhao, Min-jian Caire, Giuseppe Wu, Yik-Chung Signal Processing This work considers a spatial non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationary, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and a novel dynamic alternating maximum a posteriori (DA-MAP) framework is developed to solve the problem. The DA-MAP contains four basic modules: channel estimation module, VR detection module, grid update module, and temporal correlated module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computational intensive matrix inverse each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulations show that the proposed method can achieve a significant channel tracking performance while achieving low computational overhead. |
| title | Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.19475 |