On the use of channel models and channel estimation techniques for massive MIMO systems

Massive multiple-input multiple-output (MIMO) is a key technology driving the 5G evolution. It relies on the use a large number of antennas at the base-station to improve network performance. The performance of massive MIMO systems is often limited by imperfect channel estimation because of pilot contamination. Recently, several channel estimation techniques have been proposed to minimize the performance degradation. However, the assessment of these techniques in the literature has often been conducted considering standard channel models, like the independent Rayleigh fading model and Clarke's multipath model, which do not consider spatial correlation. In this work, we investigate different channel models used and proposed for massive MIMO transmission and, through numerical studies, highlight their effect on the performance of the aforementioned channel estimation techniques. Based on this we recommend the use of channel models that capture the spatial correlation between antennas and different user channels.