  In this paper, we study an IRS-assisted coverage enhancement problem for a
given region, aiming to optimize the passive reflection of the IRS for
improving the average communication performance in the region by accounting for
both deterministic and random channels in the environment. To this end, we
first derive the closed-form expression of the average received signal power in
terms of the deterministic base station (BS)-IRS-user cascaded channels over
all user locations, and propose an IRS-aided coverage enhancement framework to
facilitate the estimation of such deterministic channels for IRS passive
reflection design. Specifically, to avoid the exorbitant overhead of estimating
the cascaded channels at all possible user locations, a location selection
method is first proposed to select only a set of typical user locations for
channel estimation by exploiting the channel spatial correlation in the region.
To estimate the deterministic cascaded channels at the selected user locations,
conventional IRS channel estimation methods require additional pilot signals,
which not only results in high system training overhead but also may not be
compatible with the existing communication protocols. To overcome this issue,
we further propose a single-layer neural network (NN)-enabled IRS channel
estimation method in this paper, based on only the average received signal
power measurements at each selected location corresponding to different IRS
random training reflections, which can be offline implemented in current
wireless systems. Numerical results demonstrate that our proposed scheme can
significantly improve the coverage performance of the target region and
outperform the existing power-measurement-based IRS reflection designs.
