  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.
