Channel Estimation for Intelligent Reflecting Surface Assisted Backscatter Communication

TL;DR

This paper introduces an efficient channel estimation scheme for IRS-assisted backscatter communication systems, addressing the challenge of acquiring CSI due to passive reflection over forward and backward links, and demonstrating improved performance through simulations.

Contribution

It proposes a novel channel estimation method tailored for IRS-assisted backscatter communication, optimizing IRS reflection matrices to minimize estimation error.

Findings

The proposed scheme outperforms baseline methods in simulations.

Optimized IRS training reflection matrices improve channel estimation accuracy.

Simulation results confirm the effectiveness of the proposed approach.

Abstract

Intelligent reflecting surface (IRS) is a promising technology to improve the performance of backscatter communication systems by smartly reconfiguring the multi-reflection channel. To fully exploit the passive beamforming gain of IRS in backscatter communication, channel state information (CSI) is indispensable but more practically challenging to acquire than conventional IRS-assisted systems, since IRS passively reflects signals over both the forward and backward (backscattering) links between the reader and tag. To address this issue, we propose in this letter a new and efficient channel estimation scheme for the IRS-assisted backscatter communication system. To minimize the mean-square error (MSE) of channel estimation, we formulate and solve an optimization problem by designing the IRS training reflection matrix for channel estimation under the constraints of unit-modulus elements and full rank. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to other baseline schemes.