{
  "abstract": "We estimate the position, the velocity and the shadowing gains of a mobile user with the base stations (BSs) of a large cellular network modelled by a Poisson point process. At each slot, the strongest BS senses the user and sends its measurements to a controller, which runs a sequential extended Kalman filter (EKF), whose measurement noise covariance depends on the state through the echo signal-to-noise ratio (SNR). As a network metric, we use the Cesàro average of the error covariance estimated by the filter. In a two-dimensional setting, the state-dependent covariance lowers the position error by 23% with respect to a classical EKF, and keeps the estimated variance consistent with the actual error. Next, we compare two measurement reports for the estimation of the shadowing gain: (i) the echo SNR only, and (ii) the echo SNR with the delay, which is a sensing-aided communication (SAC) framework. With SAC, the shadowing gain estimation error falls from 1.9 to 0.25dB, equivalent to that of a filter that knows the true user position. The SAC framework also saves up to 2.4dB of transmit power for a downlink scheduled ahead under slow shadowing, with respect to the last measured gain. As the user moves across the network, the error peaks of the two reports alternate: without the delay, they occur close to the BSs; with it, near the cell boundaries. The engineering insights obtained through the proposed framework are expected to be useful for designing initial access protocols with joint sensing and communication.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Nahuel Soprano-Loto"
    },
    {
      "name": "Ashutosh Balakrishnan"
    },
    {
      "name": "François Baccelli"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/752qp77dtrdma.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "752qp77dtrdma",
  "issns": [],
  "landing_url": "https://hal.science/hal-05612297v2",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.stat"
    ],
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      "hal",
      "1.info.info-mo"
    ],
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    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05612297/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/752qp77dtrdma.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:20:44Z",
      "native_id": "hal-05612297",
      "source": "hal",
      "url": "https://hal.science/hal-05612297v2"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "hal:0.stat",
      "subfield": "statistics"
    }
  ],
  "title": "Sequential State-Dependent Kalman Filtering for Dynamic Parameter Estimation of a Mobile User in a Large Cellular Network",
  "title_text": "Sequential State-Dependent Kalman Filtering for Dynamic Parameter Estimation of a Mobile User in a Large Cellular Network",
  "type": "preprint",
  "updated_at": "2026-11-02T12:20:44Z",
  "url": "https://research.draconitic.com/paper/752qp77dtrdma",
  "venue": "HAL",
  "versions": []
}