2021
DOI: 10.1109/twc.2021.3067957
|View full text |Cite
|
Sign up to set email alerts
|

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. How… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
21
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
6
2

Relationship

2
6

Authors

Journals

citations
Cited by 18 publications
(21 citation statements)
references
References 33 publications
0
21
0
Order By: Relevance
“…Next we focus on (6). By performing similar derivations as ( 8) and ( 9), the minimization in ( 6) is equivalent to…”
Section: Algorithm Description a Admm Solver For Direct Localizationmentioning
confidence: 99%
See 1 more Smart Citation
“…Next we focus on (6). By performing similar derivations as ( 8) and ( 9), the minimization in ( 6) is equivalent to…”
Section: Algorithm Description a Admm Solver For Direct Localizationmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: B Related Workmentioning
confidence: 99%
“…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.…”
Section: B Multi-user Cooperationmentioning
confidence: 99%