Time-Varying Parametric Channel Estimation With CP Decomposition Tensor Processing
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917531177975808 |
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| author | Pinto, Enrique T. R. de Almeida, André L. F. Juntti, Markku |
| author_facet | Pinto, Enrique T. R. de Almeida, André L. F. Juntti, Markku |
| contents | Integrated sensing and communications (ISAC) is a key use case for sixth-generation (6G) wireless systems, where parametric channel estimation (PCE) plays a central role in enabling sensing, localization, and channel equalization in high-mobility scenarios. However, PCE is typically more computationally demanding than conventional channel estimation, which motivates the development of lower-complexity solutions. In this letter, we propose a fast PCE algorithm for time-varying and frequency-selective (TVFS) channels based on canonical polyadic (CP) decomposition and tensor processing, combined with ESPRIT-based initialization, component refinement, and exact line-search alternating coordinate descent. Two variants are presented: one for fully digital and another for hybrid receiver architectures. Numerical results show that the proposed method clearly outperforms a related CP-based baseline while achieving estimation performance close to a multiple-start SAGE benchmark at a substantially lower computational cost, with about one order of magnitude shorter execution time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_25593 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Time-Varying Parametric Channel Estimation With CP Decomposition Tensor Processing Pinto, Enrique T. R. de Almeida, André L. F. Juntti, Markku Signal Processing Integrated sensing and communications (ISAC) is a key use case for sixth-generation (6G) wireless systems, where parametric channel estimation (PCE) plays a central role in enabling sensing, localization, and channel equalization in high-mobility scenarios. However, PCE is typically more computationally demanding than conventional channel estimation, which motivates the development of lower-complexity solutions. In this letter, we propose a fast PCE algorithm for time-varying and frequency-selective (TVFS) channels based on canonical polyadic (CP) decomposition and tensor processing, combined with ESPRIT-based initialization, component refinement, and exact line-search alternating coordinate descent. Two variants are presented: one for fully digital and another for hybrid receiver architectures. Numerical results show that the proposed method clearly outperforms a related CP-based baseline while achieving estimation performance close to a multiple-start SAGE benchmark at a substantially lower computational cost, with about one order of magnitude shorter execution time. |
| title | Time-Varying Parametric Channel Estimation With CP Decomposition Tensor Processing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.25593 |