  This study considers the joint location and velocity estimation of UE and
scatterers in a three-dimensional mmWave CRAN architecture. Several existing
works have achieved satisfactory results with neural networks (NNs) for
localization. However, the black box NN localization method has limited
performance and relies on a prohibitive amount of training data. Thus, we
propose a model-based learning network for localization by combining NNs with
geometric models. Specifically, we first develop an unbiased WLS estimator by
utilizing hybrid delay/angular measurements, which determine the location and
velocity of the UE in only one estimator, and can obtain the location and
velocity of scatterers further. The proposed estimator can achieve the CRLB and
outperforms state-of-the-art methods. Second, we establish a NN-assisted
localization method (NN-WLS) by replacing the linear approximations in the
proposed WLS localization model with NNs to learn higher-order error
components, thereby enhancing the performance of the estimator. The solution
possesses the powerful learning ability of the NN and the robustness of the
proposed geometric model. Moreover, the ensemble learning is applied to improve
the localization accuracy further. Comprehensive simulations show that the
proposed NN-WLS is superior to the benchmark methods in terms of localization
accuracy, robustness, and required time resources.
