Sequential State-Dependent Kalman Filtering for Dynamic Parameter Estimation of a Mobile User in a Large Cellular Network
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.
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BibTeX
@misc{sopranoloto2026sequential,
title = {{Sequential State-Dependent Kalman Filtering for Dynamic Parameter Estimation of a Mobile User in a Large Cellular Network}},
author = {Nahuel Soprano-Loto and Ashutosh Balakrishnan and François Baccelli},
howpublished = {HAL},
year = {2026},
month = oct,
url = {https://hal.science/hal-05612297v2},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Sequential State-Dependent Kalman Filtering for Dynamic Parameter Estimation of a Mobile User in a Large Cellular Network
AU - Nahuel Soprano-Loto
AU - Ashutosh Balakrishnan
AU - François Baccelli
PY - 2026
DA - 2026/10/31
PB - HAL
UR - https://hal.science/hal-05612297v2
AB - 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.
ER -