  Intelligent reflecting surface (IRS) is a promising technology for enhancing
wireless communication systems, which adaptively configures massive passive
reflecting elements to control wireless channel in a desirable way. Due to
hardware characteristics and deploying environments, the IRS may be subject to
reflecting element blockages and failures, and hence developing diagnostic
techniques is of great significance to system monitoring and maintenance. In
this paper, we develop diagnostic techniques for IRS systems to locate faulty
reflecting elements and retrieve failure parameters. Three cases of the channel
state information (CSI) availability are considered. In the first case where
full CSI is available, a compressed sensing based diagnostic technique is
proposed, which significantly reduces the required number of measurements. In
the second case where only partial CSI is available, we jointly exploit the
sparsity of the millimeter-wave channel and the failure, and adopt compressed
sparse and low-rank matrix recovery algorithm to decouple channel and failure.
In the third case where no CSI is available, a novel atomic norm is introduced
as the sparsity-inducing norm of the cascaded channel, and the diagnosis
problem is formulated as a joint sparse recovery problem. Finally, proposed
diagnostic techniques are validated through numerical simulations.
