  In cell-free multiple input multiple output (MIMO) networks, multiple base
stations (BSs) collaborate to achieve high spectral efficiency. Nevertheless,
high penetration loss due to large blockages in harsh propagation environments
is often an issue that severely degrades communication performance. Considering
that intelligent reflecting surface (IRS) is capable of constructing digitally
controllable reflection links in a low-cost manner, we investigate an
IRS-enhanced downlink cell-free MIMO network in this paper. We aim to maximize
the sum rate of all the users by jointly optimizing the transmit beamforming at
the BSs and the reflection coefficients at the IRS. To address the optimization
problem, we propose a fully distributed machine learning algorithm. Different
from the conventional iterative optimization algorithms that require a central
processing at the central processing unit (CPU) and large amount of channel
state information and signaling exchange between the BSs and the CPU, in the
proposed algorithm, each BS can locally design its beamforming vectors.
Meanwhile, the IRS reflection coefficients are determined by one of the BSs.
Simulation results show that the deployment of IRS can significantly boost the
sum user rate and that the proposed algorithm can achieve a high sum user rate
with a low computational complexity.
