  The main requirements for 5G and beyond connectivity include a uniform high
quality of service, which can be attained in crowded scenarios by extra-large
MIMO (XL-MIMO) systems. Another requirement is to support increasing connected
users in (over)crowded machine-type communication (mMTC). In such scenarios,
pilot assignment (PA) becomes paramount to reduce pilot contamination and
consequently improve spectral efficiency (SE). We propose a novel quasi-optimal
low-complexity iterative pilot assignment strategy for XL-MIMO systems based on
a genetic algorithm (GA). The proposed GA-based PA procedure turns the quality
of service more uniform, taking into account the normalized mean-square error
(NMSE) of channel estimation from each candidate of the population. The
simulations reveal that the proposed iterative procedure minimizes the channel
estimation NMSE averaged over the UEs. The second procedure is the subarray
(SA) selection. In XL-MIMO systems, commonly, a UE is close to a SA antenna
subset such that a sufficient data rate can be achieved if only a specific SA
serves that UE. Thus, a SA selection procedure is investigated to make the
system scalable by defining the maximum number of UEs each SA can help. Hence,
the SA clusters are formatted based on the PA decision. Furthermore, we
introduce an appropriate channel model for XL-MIMO, which considers a
deterministic LoS component with a distance-dependent probability of existence
combined with a stochastic spatially correlated Rayleigh NLoS fading component.
The developed simulations and analyses rely on this suitable channel model
under realistic assumptions of pilot contamination and correlated channels.
