SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO
Huizhi Wang, Yong Zeng

TL;DR
This paper investigates the SNR scaling laws for radio sensing using extremely large-scale MIMO arrays, accounting for near-field effects and spherical wavefronts, revealing more practical scaling behaviors than traditional models.
Contribution
It develops a generic model for XL-MIMO radar that considers spherical wavefronts and derives new closed-form SNR expressions, highlighting practical scaling laws.
Findings
SNR scales differently under realistic models compared to UPW assumptions.
XL-phased-array radar SNR increases with diminishing returns with M and N.
XL-MIMO radar SNR first increases then decreases with M.
Abstract
Mobile communication networks were designed to mainly support ubiquitous wireless communications, yet they are expected to also achieve radio sensing capabilities in the near future. Most prior studies on radar sensing focus on distant targets, which usually rely on far-field assumption with uniform plane wave (UPW) models. However, with ever-increasing antenna size, together with the growing need to also sense nearby targets, the far-field assumption may become invalid. This paper studies radar sensing with extremely large-scale (XL) antenna arrays, where a generic model that takes into account both spherical wavefront and amplitude variations across array elements is developed. Furthermore, new closed-form expressions of the sensing signal-to-noise ratios (SNRs) are derived for both XL-MIMO radar and XL-phased-array radar modes. Our results reveal that different from the conventional…
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Taxonomy
TopicsRadar Systems and Signal Processing · Microwave Imaging and Scattering Analysis · Sparse and Compressive Sensing Techniques
