  Recently, intelligent reflecting surface (IRS) assisted mmWave networks are
emerging, which bear the potential to address the blockage issue of the
millimeter wave (mmWave) communication in a more cost-effective way. In
particular, IRS is built by passive and programmable electromagnetic elements
that can manipulate the mmWave propagation channel into a more favorable
condition that is free of blockage via judicious joint BS-IRS transmission
design. However, the coexistence of IRSs and mmWave BSs complicates the network
architecture, and thus poses great challenges for efficient beam management
(BM) that is one critical prerequisite for high performance mmWave networks. In
this paper, we systematically evaluate the key issues and challenges of BM for
IRS-assisted mmWave networks to bring insights into the future network design.
Specifically, we carefully classify and discuss the extensibility and
limitations of the existing BM of conventional mmWave towards the IRS-assisted
new paradigm. Moreover, we propose a novel machine learning empowered BM
framework for IRS-assisted networks with representative showcases, which
processes environmental and mobility awareness to achieve highly efficient BM
with significantly reduced system overhead. Finally, some interesting future
directions are also suggested to inspire further researches.
