Accurate Wideband Localization in Massive MIMO Systems With Low-Resolution ADCs

This article investigates the localization of a mobile station (MS) in a quantized massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, in which the base station (BS) uses low-resolution analog-to-digital converters (ADCs) to quantize the received signals. Specifically, a novel two-stage localization scheme is proposed. In the first stage, we try to finely estimate the channel parameters by resorting to the generalized turbo (GTurbo) and the sparse Bayesian learning (SBL) methodologies. In the second stage, we estimate the MS position using the obtained estimates of the channel parameters, where the Gauss-Newton algorithm is used to minimize the involved nonlinear least square objective function. Simulation results show that the proposed scheme outperforms existing counterparts, and can approach the localization Cramér-Rao bound (CRB) in the medium-to-high signal-to-noise ratio (SNR) regime.