  Intelligent reflecting surface (IRS) has gained tremendous attention recently
as a disruptive technology for beyond 5G networks. In this paper, we consider
the problem of secrecy rate maximization for an IRS-assisted Gaussian
multiple-input multiple-output multi-antenna-eavesdropper (MIMOME) wiretap
channel (WTC). In this context, we aim to jointly optimize the input covariance
matrix and the IRS phase shifts to maximize the achievable secrecy rate of the
considered system. To solve the formulated problem which is non-convex, we
propose an iterative method based on the block successive maximization (BSM),
where each iteration is done in closed form. More specifically, we maximize a
lower bound on the achievable secrecy rate to update the input covariance
matrix for fixed phase shifts, and then maximize the (exact) achievable secrecy
rate to update phase shifts for a given input covariance.We consider the total
free space path loss (FSPL) in this system to emphasize the first-order measure
of the applicability of the IRS in the considered communication system. We
present a convergence proof and the associated complexity analysis of the
proposed algorithm. Numerical results are provided to demonstrate the
superiority of the proposed method compared to a known solution, and also to
show the effect of different parameters of interest on the achievable secrecy
rate of the IRS-assisted MIMOME WTC.
