Power Measurement Enabled Channel Autocorrelation Matrix Estimation for IRS-Assisted Wireless Communication

TL;DR

This paper introduces a novel IRS-assisted wireless channel estimation method using received power measurements, avoiding protocol modifications, and employs low-rank matrix recovery techniques to estimate the channel autocorrelation matrix.

Contribution

It proposes a new power-based channel estimation scheme for IRS systems that does not require protocol changes and develops algorithms for low-rank matrix recovery under noise.

Findings

The proposed algorithms accurately estimate the channel autocorrelation matrix.

The methods are robust against noise and quantization errors.

Simulation results confirm the effectiveness of the estimation and reflection design.

Abstract

By reconfiguring wireless channels via passive signal reflection, intelligent reflecting surface (IRS) can bring significant performance enhancement for wireless communication systems. However, such performance improvement generally relies on the knowledge of channel state information (CSI) for IRS-involved links. Prior works on IRS CSI acquisition mainly estimate IRS-cascaded channels based on the extra pilot signals received at the users/base station (BS) with time-varying IRS reflections, which, however, needs to modify the existing channel training/estimation protocols of wireless systems. To address this issue, we propose in this paper a new channel estimation scheme for IRS-assisted communication systems based on the received signal power measured at the user terminal, which is practically attainable without the need of changing the current protocol. Due to the lack of signal phase information in measured power, the autocorrelation matrix of the BS-IRS-user cascaded channel is estimated by solving an equivalent rank-minimization problem. To this end, a low-rank-approaching (LRA) algorithm is proposed by employing the fractional programming and alternating optimization techniques. To reduce computational complexity, an approximate LRA (ALRA) algorithm is also developed. Furthermore, these two algorithms are extended to be robust against the receiver noise and quantization error in power measurement. Simulation results are provided to verify the effectiveness of the proposed channel estimation algorithms as well as the IRS passive reflection design based on the estimated channel autocorrelation matrix.