  This paper concentrates on the problem of associating an intelligent
reflecting surface (IRS) to multiple users in a multiple-input single-output
(MISO) downlink wireless communication network. The main objective of the paper
is to maximize the sum-rate of all users by solving the joint optimization
problem of the IRS-user association, IRS reflection, and BS beamforming,
formulated as a non-convex mixed-integer optimization problem. The variable
separation and relaxation are used to transform the problem into three convex
sub-problems, which are alternatively solved through the convex optimization
(CO) method. The major drawback of the proposed CO-based algorithm is high
computational complexity. Thus, we make use of machine learning (ML) to tackle
this problem. To this end, first, we convert the optimization problem into a
regression problem. Then, we solve it with feed-forward neural networks (FNNs),
trained by CO-based generated data. Simulation results show that the proposed
ML-based algorithm has a performance equivalent to the CO-based algorithm, but
with less computation complexity due to its offline training procedure.
