Abstract
We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.
Highlights
Millimeter wave (30–300 GHz) technologies have been identified as a promising candidate for tackling the data traffic deluge and frequency resource shortage in the fifth generation era [1]
The simulation of channel estimation and parameter recovery considers a full-NLoS propagation; the simulation of environment mapping and user localization assumes that HIU,r,k contains a LoS path, while
Our analysis indicates that the complexity of the bilinear alternating least squares (BALS) and structured CP decomposition (SCPD) is proportional to the number of reflectors NI
Summary
Millimeter wave (mmWave) (30–300 GHz) technologies have been identified as a promising candidate for tackling the data traffic deluge and frequency resource shortage in the fifth generation era [1]. A matrix-calibration scheme that developed a joint calibration and estimation algorithm was developed in [17] Most of these works achieve separated channels from the base station (BS) or user equipment (UE) to the IRS plane, which, induces inherent estimation ambiguities that hinder the exact recovery of multipath parameters. These ambiguities limit the integration of IRSs into environment-dependent applications [11], which can only be removed with strong or unrealistic assumptions, e.g., normalized power, channel reciprocity, quasi-static states, a priori long-term information, etc [14]–[17]. BS (UE) antennas BS (UE) RF chains reflectors in one IRS plane horizontal (vertical) IRS reflectors OFDM subcarriers training subcarriers training frames training time slots per frame BS training streams BS-IRS (IRS-UE) channel paths BS-IRS (IRS-UE) path gain BS-IRS (IRS-UE) path delay horizontal AoA at BS (AoD at UE) vertical AoA at BS (AoD at UE) horizontal AoA (AoD) at IRS vertical AoA (AoD) at IRS grouping pattern: total sub-groups grouping pattern: sub-group size sparse pattern: dense subarray size sparse pattern: sparse subarray size
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