  One main challenge for implementing intelligent reflecting surface (IRS)
aided communications lies in the difficulty to obtain the channel knowledge for
the base station (BS)-IRS-user cascaded links, which is needed to design
high-performance IRS reflection in practice. Traditional methods for estimating
IRS cascaded channels are usually based on the additional pilot signals
received at the BS/users, which increase the system training overhead and also
may not be compatible with the current communication protocols. To tackle this
challenge, we propose in this paper a new single-layer neural network
(NN)-enabled IRS channel estimation method based on only the knowledge of
users' individual received signal power measurements corresponding to different
IRS random training reflections, which are easily accessible in current
wireless systems. To evaluate the effectiveness of the proposed channel
estimation method, we design the IRS reflection for data transmission based on
the estimated cascaded channels in an IRS-aided multiuser communication system.
Numerical results show that the proposed IRS channel estimation and reflection
design can significantly improve the minimum received signal-to-noise ratio
(SNR) among all users, as compared to existing power measurement based designs.
