Metraipolis › archive › dc_d6dfd8563ca1

GIST: Location-Dependent Uplink Path Structure Transfer for FDD Downlink Channel Estimation — arXiv:2611.03482

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03482
License (as archived): CC0-1.0
Archived: 2026-11-03T11:55:16.683Z
SHA-256 of the archived text: a6220263bc568179c8252e491878baeeb30fb0654bcc32f24ce9dfc83b8450be
Stable URL of this page: https://metraipolis.com/documento/dc_d6dfd8563ca1

How to cite

GIST: Location-Dependent Uplink Path Structure Transfer for FDD Downlink Channel Estimation — arXiv:2611.03482 — arXiv (en), https://arxiv.org/abs/2611.03482 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d6dfd8563ca1 (Metraipolis archive dc_d6dfd8563ca1, sha256 a6220263bc568179c8252e491878baeeb30fb0654bcc32f24ce9dfc83b8450be)

BibTeX

@misc{dcd6dfd8563ca1,
  title = {GIST: Location-Dependent Uplink Path Structure Transfer for FDD Downlink Channel Estimation — arXiv:2611.03482},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03482},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d6dfd8563ca1; sha256 a6220263bc568179c8252e491878baeeb30fb0654bcc32f24ce9dfc83b8450be},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03482. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A key challenge of downlink (DL) channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems is the pilot overhead, which grows with the numbers of antennas and subcarriers. However, a prior on path delays and directions shared by the uplink (UL) and DL channels can reduce this overhead. A model trained only on UL channels measured at a base station (BS) can predict the path structure (delays, directions and power gains) from the location of a user equipment (UE) and thereby assist DL channel estimation. In this paper, we propose Gaussian-Informed Structure Transfer (GIST), in which each Gaussian primitive models one propagation path, whose delay, directions and power gain are predicted from the UE location relative to the primitive center. The complex gains of the predicted paths are estimated from DL pilots by linear minimum mean square error estimation with this prior. Experiments on a ray-traced and a measured dataset show that GIST outperforms all baselines, reaching a lower DL estimation error with fewer pilot subcarriers. Further experiments demonstrate its robustness to UE location errors, its low storage and computational requirements.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv eess.SP, nuovi lavori del giorno).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2611.03482 — 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 2026-11-03T11:15:41.232Z dalla pagina https://arxiv.org/list/eess.SP/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).