Model-Based Learning Network for 3-D Localization in mmWave Communications

Abstract

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and 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 Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. 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.

Fast Direct Localization for Millimeter Wave MIMO Systems via Deep ADMM Unfolding

“…Next we focus on (6). By performing similar derivations as ( 8) and ( 9), the minimization in ( 6) is equivalent to…”

“…For the 3-D localization, a closedform estimator was developed in [5]. More recently, several novel localization methods for the indoor/outdoor mmWave MIMO systems were developed in [2], [6]. However, the above two-step localization methods may yield poor performance for This work was supported in part by the National Natural Science Foundation of China under Grant Nos.…”

Performance Analysis for AOA-Based Localization Under Millimeter-Wave Wireless Networks

“…Due to the fast attenuation of mmWave signals, the localization performance under mmWave networks is subject to none-line-of-sight (NLOS) components, and the impact of the NLOS propagation is introduced in [12]. Furthermore, a neural networks-enhanced hybrid localization framework for the mmWave wireless networks is proposed in [13] to achieve high-precision localization purposes.…”

Integrated Sensing and Communication with Multi-Domain Cooperation

“…In Fig. 3 to the studies about the multipath channel sounding [7], the extracted parameter is generally a Gaussian-corrupted observation of true value such as φ1 = ϕ 1 + w, where w is Gaussian noise with zero mean and certain variance. Therefore, φ1 and τ1 form a 2-dimensional Gaussiancorrupted observation of VA position.…”