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.
翻译:本研究考虑在三维米的CRAN结构中联合定位和速度估计UE和散射器。一些现有工程与神经网络(NNS)的本地化取得了令人满意的结果。然而,黑盒NNN本地化方法的性能有限,而且依赖于令人望而却步的培训数据。因此,我们提出一个基于模型的本地化学习网络,将NNP与几何模型结合起来。具体地,我们首先通过使用混合延迟/角测量来开发一个公正的WLS本地化估算器,该测量器仅用一个估测器来确定UE的位置和速度,并能够进一步获得散射器的位置和速度。提议的SDRBS本地化方法的强大学习能力,超越了最先进的培训方法。第二,我们用NNSLS本地化模型来取代拟议的W本地化模型的线性近近,从而提高估测器的性能。 将NNNS的准确性应用到拟议的本地精确度,而拟议的本地化方法的精确性更精确性,这是对当地测算方法的精确性。