Abstract
In this letter, fingerprinting-based cooperative localization for millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (MIMO) systems is investigated. For the purpose of robust localization in line-of-sight (LOS) or non-LOS (NLOS) environments, a dynamic two-dimensional (2D) fingerprint training scheme employing maximum likelihood (ML) estimation and information entropy theory is proposed. This scheme regularly extracts link state matrix and confidence factor-aided AOA vectors from uplink channels. Then, a dynamic AP select scheme along with an anchor node (AN) filtering scheme is used to rapidly lock the range of the user location. Based on this, a weighted mean square error (WMSE)-based AOA fingerprinting scheme is developed to estimate the user location. Numerical results are provided to validate the localization accuracy of the proposed scheme.
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