Blind Beamforming for Multiple Intelligent Reflecting Surfaces

Channel acquisition is a major challenge faced by the conventional beamforming methods when dealing with multiple intelligent reflecting surfaces (IRSs), because the number of unknown channels grows exponentially with the number of IRSs. This work proposes to sidestep channel estimation and to configure the IRSs blindly based on the statistical information which is extracted from a set of random samples of the received signal power. The proposed blind beamforming method has provable performance in terms of the signal-to-noise ratio (SNR) boost. For instance, it yields a quartic SNR boost of
$\Theta(N^{4})$
for a double-IRS system under certain condition, where
$N$
is the number of reflected elements of each IRS. We remark that the above
$\Theta(N^{4})$
result is more sophisticated than the existing ones about the double-IRS system in the literature. Furthermore, we numerically demonstrate the advantage of the proposed blind beamforming method through prototype tests with multiple IRSs.