Near-Field Localization for Reconfigurable Intelligent Surface Aided XL-MIMO Systems Harnessing the NLoS Components

This paper studies the near-field localization problem under dynamic scenarios, which harnesses the non-line-of-sight (NLoS) components, in a reconfigurable intelligent surface (RIS)-aided system equipped with extremely large-scale multi-input multi-output (XL-MIMO). To reduce the complexity of the position estimation, the subarray far-field model is employed to approximate the near-field channel. A factor graph within a Bayesian framework is constructed to detail the probability transition relationship among the relevant variables. Based on the message passing in this factor graph, a near-field localization algorithm is developed to estimate the marginal probability distributions of the UE’s and scatterers’ positions in each time slot. The misspecified Cramér-Rao Lower Bound (MCRLB) is derived to evaluate the performance of the algorithm under the subarray far-field model. To explore the localization potential of the system, a closed-form solution for a low-complexity directional beamforming design and a robust beamforming design based on the gradient descent method (GDM) are further proposed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark schemes, and validate the performance gain of harnessing the NLoS components.