User Selection in Near-Field Gigantic MIMO Systems with Modular Arrays

TL;DR

This paper investigates the potential of Modular Arrays in gigantic MIMO systems, demonstrating significant improvements in spectral efficiency and user capacity through optimized user selection and precoding algorithms.

Contribution

It provides a rigorous analysis of inter-user interference in modular gMIMO and introduces two novel algorithms that outperform existing methods in sum-spectral efficiency.

Findings

Over 70% improvement in sum-spectral efficiency with proposed algorithms

Modular Arrays enable serving more users due to better spatial resolution

Analytical characterization of near-field inter-user interference

Abstract

Modular Arrays (MAs) are a promising architecture to enable multi-user communications in next-generation multiple-input multiple-output (MIMO) systems based on extra-large (XL) or gigantic MIMO (gMIMO) deployments, trading off improved spatial resolution with characteristic interference patterns associated with grating lobes. In this work, we analyze whether MAs can outperform conventional collocated deployments, in terms of achievable sum-spectral efficiency (SE) and served users in a multi-user downlink set-up. First, we provide a rigorous analytical characterization of the inter-user interference for modular gMIMO systems operating in the near field. Then, we leverage these results to optimize the user selection and precoding mechanisms, designing two algorithms that largely outperform existing alternatives in the literature, with different algorithmic complexities. Results show that the proposed algorithms yield over 70% improvements in achievable sum-spectral efficiencies compared to the state of the art. We also illustrate how MAs allows us to serve a larger number of users thanks to their improved spatial resolution, compared to the collocated counterpart.