  This paper discusses how to optimize the phase shifts of intelligent
reflecting surface (IRS) to combat channel fading without any channel state
information (CSI), namely blind beamforming. Differing from most previous works
based on a two-stage paradigm of first estimating channels and then optimizing
phase shifts, our approach is completely data-driven, only requiring a dataset
of the received signal power at the user terminal. Thus, our method does not
incur extra overhead costs for channel estimation, and does not entail
collaboration from service provider, either. The main idea is to choose phase
shifts at random and use the corresponding conditional sample mean of the
received signal power to extract the main features of the wireless environment.
This blind beamforming approach guarantees an $N^2$ boost of signal-to-noise
ratio (SNR), where $N$ is the number of reflective elements (REs) of IRS,
regardless of whether the direct channel is line-of-sight (LoS) or not.
Moreover, blind beamforming is extended to a double-IRS system with provable
performance. Finally, prototype tests show that the proposed blind beamforming
method can be readily incorporated into the existing communication systems in
the real world; simulation tests further show that it works for a variety of
fading channel models.
