Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912204854394880 |
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| author | Ma, Ming Dai, Jisheng Jiang, Xue-Qin |
| author_facet | Ma, Ming Dai, Jisheng Jiang, Xue-Qin |
| contents | Accurate channel estimation in orthogonal time frequency space (OTFS) systems with massive multiple-input multiple-output (MIMO) configurations is challenging due to high-dimensional sparse representation (SR). Existing methods often face performance degradation and/or high computational complexity. To address these issues and exploit intricate channel sparsity structure, this letter first leverages a novel hybrid burst-sparsity prior to capture the burst/common sparse structure in the angle/delay domain, and then utilizes an independent variational Bayesian inference (VBI) factorization technique to efficiently solve the high-dimensional SR problem. Additionally, an angle/Doppler refinement approach is incorporated into the proposed method to automatically mitigate off-grid mismatches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation Ma, Ming Dai, Jisheng Jiang, Xue-Qin Signal Processing Accurate channel estimation in orthogonal time frequency space (OTFS) systems with massive multiple-input multiple-output (MIMO) configurations is challenging due to high-dimensional sparse representation (SR). Existing methods often face performance degradation and/or high computational complexity. To address these issues and exploit intricate channel sparsity structure, this letter first leverages a novel hybrid burst-sparsity prior to capture the burst/common sparse structure in the angle/delay domain, and then utilizes an independent variational Bayesian inference (VBI) factorization technique to efficiently solve the high-dimensional SR problem. Additionally, an angle/Doppler refinement approach is incorporated into the proposed method to automatically mitigate off-grid mismatches. |
| title | Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.12239 |