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Channel Estimation for Movable Intelligent Surface

Fuente: arXiv
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Bibliographic Details
Main Authors: Alcantara, Daniel C., de Araújo, Josué V., de Araújo, Gilderlan T., de Almeida, André L. F.
Format: Preprint
Published: 2026
Subjects:
Signal Processing
Online Access:
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_version_ 1866913174961258496
author Alcantara, Daniel C.
de Araújo, Josué V.
de Araújo, Gilderlan T.
de Almeida, André L. F.
author_facet Alcantara, Daniel C.
de Araújo, Josué V.
de Araújo, Gilderlan T.
de Almeida, André L. F.
contents This paper proposes a tensor-based channel estimation framework for an uplink MIMO system assisted by a movable intelligent surface. The considered architecture combines a fixed transmissive metasurface with a smaller movable layer, whose discrete positions create an additional structured training dimension. By jointly exploiting fixed-layer phase patterns and movable-layer positions, the received pilots are modeled as a fourth-order PARAFAC tensor. A trilinear alternating least-squares receiver is then derived to estimate the individual channels and the position-dependent response. Importantly, the proposed method does not require prior knowledge of the movable-layer phase response at the receiver, since this unknown factor is estimated from the tensor structure of the received signal. Simulation results show that increasing the training length improves the NMSE of the estimated factors and the reconstructed cascaded channel.
format Preprint
id arxiv_https___arxiv_org_abs_2606_00387
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Channel Estimation for Movable Intelligent Surface
Alcantara, Daniel C.
de Araújo, Josué V.
de Araújo, Gilderlan T.
de Almeida, André L. F.
Signal Processing
This paper proposes a tensor-based channel estimation framework for an uplink MIMO system assisted by a movable intelligent surface. The considered architecture combines a fixed transmissive metasurface with a smaller movable layer, whose discrete positions create an additional structured training dimension. By jointly exploiting fixed-layer phase patterns and movable-layer positions, the received pilots are modeled as a fourth-order PARAFAC tensor. A trilinear alternating least-squares receiver is then derived to estimate the individual channels and the position-dependent response. Importantly, the proposed method does not require prior knowledge of the movable-layer phase response at the receiver, since this unknown factor is estimated from the tensor structure of the received signal. Simulation results show that increasing the training length improves the NMSE of the estimated factors and the reconstructed cascaded channel.
title Channel Estimation for Movable Intelligent Surface
topic Signal Processing
url https://arxiv.org/abs/2606.00387

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