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Tensor Decomposition-Based Wireless Sensing for MIMO-OFDM ISAC via Flexible Spatial-Temporal-Spectral Optimization — arXiv:2608.15792

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.15792
License (as archived): CC0-1.0
Archived: 2026-10-19T02:13:58.550Z
SHA-256 of the archived text: df83f886f95777c919c97ced2ad1c32262401b638b12791dcc9aae5b82fef455
Stable URL of this page: https://metraipolis.com/documento/dc_764f00a8179b

How to cite

Tensor Decomposition-Based Wireless Sensing for MIMO-OFDM ISAC via Flexible Spatial-Temporal-Spectral Optimization — arXiv:2608.15792 — arXiv (en), https://arxiv.org/abs/2608.15792 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_764f00a8179b (Metraipolis archive dc_764f00a8179b, sha256 df83f886f95777c919c97ced2ad1c32262401b638b12791dcc9aae5b82fef455)

BibTeX

@misc{dc764f00a8179b,
  title = {Tensor Decomposition-Based Wireless Sensing for MIMO-OFDM ISAC via Flexible Spatial-Temporal-Spectral Optimization — arXiv:2608.15792},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.15792},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_764f00a8179b; sha256 df83f886f95777c919c97ced2ad1c32262401b638b12791dcc9aae5b82fef455},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.15792. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Integrated sensing and communication (ISAC) is regarded as a key enabling technique in future 6th-generation (6G) mobile communication systems. However, existing multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC designs generally rely on the fixed-position antennas and fixed allocation of time-frequency resources, thereby limiting the degrees of freedom of wireless sensing along the spatial-temporal-spectral dimensions. In this paper, we propose a novel wireless sensing framework for MIMO-OFDM ISAC systems with flexible spatial-temporal-spectral optimization and propose a tensor decomposition-based approach to estimate target parameters, including azimuth/elevation angles, ranges, and velocities. Specifically, we first establish a monostatic wireless sensing model for MIMO-OFDM ISAC systems, where the positions of antenna elements, the allocation of OFDM symbols and subcarriers can be flexibly configured. Then, we formulate the problem of estimating target parameters as a tensor decomposition problem admitting to the canonical polyadic format, which enables the parallel target parameters estimation process from corresponding factor matrices along the spatial, temporal, and spectral dimensions, respectively. Based on the decomposed factor matrices, we derive the Cramer-Rao Bound (CRB) for the unknown target parameters and reveal that the estimation accuracy of azimuth/elevation angles, velocities and ranges is fundamentally determined by the array geometry, the distribution of OFDM symbols and subcarriers. Building on this insight, we obtain an optimized solution for the positions of antenna elements, and optimal solutions for the subcarrier allocation and OFDM symbol allocation to minimize the CRB, as well as the mean square error of target parameters estimation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-18 in eess.SP).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.15792 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T01:25:14.316Z dalla pagina https://arxiv.org/catchup/eess.SP/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).