Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916377277759488 |
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| author | Chen, Panqi Cheng, Lei |
| author_facet | Chen, Panqi Cheng, Lei |
| contents | This letter introduces a structured high-rank tensor approach for estimating sub-6G uplink channels in multi-user multiple-input and multiple-output (MU-MIMO) systems. To tackle the difficulty of channel estimation in sub-6G bands with hundreds of sub-paths, our approach fully exploits the physical structure of channel and establishes the link between sub-6G channel model and a high-rank four-dimensional (4D) tensor Canonical Polyadic Decomposition (CPD) with three factor matrices being Vandermonde-constrained. Accordingly, a stronger uniqueness property is derived in this work. This model supports an efficient one-pass algorithm for estimating sub-path parameters, which ensures plug-in compatibility with the widely-used baseline. Our method performs much better than the state-of-the-art tensor-based techniques on the simulations adhering to the 3GPP 5G protocols. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00723 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach Chen, Panqi Cheng, Lei Signal Processing This letter introduces a structured high-rank tensor approach for estimating sub-6G uplink channels in multi-user multiple-input and multiple-output (MU-MIMO) systems. To tackle the difficulty of channel estimation in sub-6G bands with hundreds of sub-paths, our approach fully exploits the physical structure of channel and establishes the link between sub-6G channel model and a high-rank four-dimensional (4D) tensor Canonical Polyadic Decomposition (CPD) with three factor matrices being Vandermonde-constrained. Accordingly, a stronger uniqueness property is derived in this work. This model supports an efficient one-pass algorithm for estimating sub-path parameters, which ensures plug-in compatibility with the widely-used baseline. Our method performs much better than the state-of-the-art tensor-based techniques on the simulations adhering to the 3GPP 5G protocols. |
| title | Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.00723 |