Phases Calibration of RIS Using Backpropagation Algorithm

TL;DR

This paper introduces a novel method for calibrating RIS phases using backpropagation, modeling the estimation as a quasi-neural network and achieving near-optimal accuracy close to theoretical bounds.

Contribution

It proposes a new OTA phase calibration technique for RIS elements using backpropagation, with derived CRBs serving as benchmarks.

Findings

RMSEs of phase estimates are close to CRBs

Proposed algorithm effectively calibrates RIS phases

Simulation confirms high accuracy of the method

Abstract

Reconfigurable intelligent surface (RIS) technology has emerged in recent years as a promising solution to the ever-increasing demand for wireless communication capacity. In practice, however, elements of RIS may suffer from phase deviations, which need to be properly estimated and calibrated. This paper models the problem of over-the-air (OTA) estimation of the RIS elements as a quasi-neural network (QNN) so that the phase estimates can be obtained using the classic backpropagation (BP) algorithm. We also derive the Cram\'{e}r Rao Bounds (CRBs) for the phases of the RIS elements as a benchmark of the proposed approach. The simulation results verify the effectiveness of the proposed algorithm by showing that the root mean square errors (RMSEs) of the phase estimates are close to the CRBs.