Improving spectral efficiency via pilot assignment and subarray selection under realistic XL-MIMO channels

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