RSS Localization in Cell-Free Massive MIMO: Algorithms, Analysis, and Implementation

Received signal strength (RSS) has been extensively studied for localization purposes, and the distributed nature of cell-free massive multiple-input multiple-output (CF-mMIMO) systems offers a new synergistic avenue for achieving high-precision localization. In this work, Open RAN and software-defined radio are used to realize the central and distributed units of a CF-mMIMO system to acquire the RSS measurements. By analyzing the experimental data, it is revealed that the RSS measured from the first-order reflection path can yield a sufficiently high signal-to-noise ratio for localization, enabling localization even without line-of-sight (LoS) paths. Inspired by this finding, a hybrid localization scheme, that can attain the accuracy benchmarked by Cram $\acute {\text {e}}$ r-Rao lower bound, is proposed to estimate the target position using both LoS and first-order non-line-of-sight RSS measurements. Furthermore, a theoretical framework is established to assess the fundamental limits of RSS-based localization in CF-mMIMO systems, offering a principled guideline for system designers to deploy and design localization systems in real-world scenarios.