  Intelligent reflecting surface (IRS) is a potential candidate for massive
multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease
of deployment, energy efficiency and extended coverage. This chapter
investigates the slot-by-slot IRS reflection pattern design and two-timescale
reflection pattern design schemes, respectively. For the slot-by-slot
reflection optimization, we propose exploiting an IRS to improve the
propagation channel rank in mmWave massive MIMO systems without need to
increase the transmit power budget. Then, we analyze the impact of the
distributed IRS on the channel rank. To further reduce the heavy overhead of
channel training, channel state information (CSI) estimation, and feedback in
time-varying MIMO channels, we present a two-timescale reflection optimization
scheme, where the IRS is configured relatively infrequently based on
statistical CSI (S-CSI) and the active beamformers and power allocation are
updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The
achievable average sum-rate (AASR) of the system is maximized without excessive
overhead of cascaded channel estimation. A recursive sampling particle swarm
optimization (PSO) algorithm is developed to optimize the large-timescale IRS
reflection pattern efficiently with reduced samplings of channel samples.
