Sparse Bayesian Learning with Atom Refinement for mmWave MIMO Channel Estimation
Paper i proceeding, 2023

In this paper, we introduce a novel estimation method for the downlink millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel. The proposed method is able to determine the angles, time delays, and gains of the multi-path components by using the spatially sparse nature of mmWave channels. We first use on-grid sparse Bayesian learning (SBL) to coarsely estimate the channel parameters in the beamspace domain. We then develop a refinement method based on Newton-Raphson and Least Square-based atomic tuning to generate a mismatch-free basis. Finally, we finely reconstruct the channel by SBL using the basis found in the previous step. Simulation results show that the proposed channel estimation method outperforms the traditional ones in terms of mean square error and algorithmic complexity.

mmWave

MIMO

positioning

channel estimation

sparse Bayesian learning

Författare

Ngoc Son Duong

Vietnam National University

Quoc Tuan Nguyen

Vietnam National University

Khac-Hoang Ngo

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Thai Mai Dinh-Thi

Vietnam National University

IEEE Workshop on Statistical Signal Processing Proceedings

Vol. 2023-July 155-159
9781665452458 (ISBN)

22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Hanoi, Vietnam,

Ämneskategorier

Telekommunikation

Kommunikationssystem

Sannolikhetsteori och statistik

Signalbehandling

Datavetenskap (datalogi)

DOI

10.1109/SSP53291.2023.10208044

Mer information

Senast uppdaterat

2023-10-09