Joint Channel Parameter Estimation and Scatterers Localization

In this paper, the novel extended space-alternating generalized expectation-maximization (SAGE) algorithm, providing joint propagation channel multi-path component (MPC) estimation and scatterer localization underlying spherical-wavefront multipath model, is proposed. Two geometry-based models are a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 22; no. 5; pp. 3324 - 3340
Main Authors: Hong, Jingxiang, Rodriguez-Pineiro, Jose, Yin, Xuefeng, Yu, Ziming
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Antenna arrays
Antenna measurements
Bouncing
Channel estimation
Channel models
Geometry
high-resolution parameter estimation algorithms
Indoor environments
Localization
Location awareness
Lower bounds
Mathematical models
MIMO communication
Monte Carlo methods
multiple-input multiple-output
Parameter estimation
Propagation channel estimation
regularly shaped geometric models
scatterer localization
Wave fronts
Wave propagation
ISSN:1536-1276, 1558-2248
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, the novel extended space-alternating generalized expectation-maximization (SAGE) algorithm, providing joint propagation channel multi-path component (MPC) estimation and scatterer localization underlying spherical-wavefront multipath model, is proposed. Two geometry-based models are aided for estimating the first and last hop scatterers under different bouncing orders. The performance of the proposed algorithm, as called geometry-aided SAGE (GA-SAGE), is illustrated by means of the Cramér-Rao lower bound derived for parameter estimates and the root-mean-square-estimation-errors (RMSEEs) obtained through and Monte-Carlo simulations, which shows the applicability both near- and far-field estimation. Finally, the GA-SAGE is applied to processing the experimental data obtained from measurements in an indoor office environment by using a single-input multiple-output (SIMO) configuration. The obtained results show that the method proposed outperforms the traditional SAGE algorithm in terms of MPCs estimation accuracy, convergence rate, and the extra capability of localizing scatterers involved in different bouncing order propagation paths. It is considered useful in environment sensing alike applications and makes the GA-SAGE an efficient and effective tool for the development of geometry-based stochastic channel models capable of reproducing channel realizations of the so-called spatial consistency or spatial non-stationarity, which are the basis for the design of transmission technologies using extremely-large antenna array (ELAA) for 5G and beyond.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3217560