Joint Channel Parameter Estimation and Scatterers Localization

Jingxiang Hong

doi:10.1109/TWC.2022.3217560

2023-05-01

PRE

Jingxiang Hong

José Rodríguez-Piñeiro *

Yin Xuefeng

Ziming Yu

DOI：10.1109/TWC.2022.3217560

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摘要

En 中文

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 Cramer-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.

Keyword:

Antenna arrays

Antenna measurements

Channel estimation

Monte Carlo methods

Location awareness

Channel models

MIMO communication

Propagation channel estimation

scatterer localization

regularly shaped geometric models

multiple-input multiple-output