Channel Estimation for Hybrid mmWave MIMO Systems With CFO Uncertainties

Channel estimation at millimeter wave (mmWave) allows designing hybrid precoders and combiners to optimize performance metrics, such as the spectral efficiency. One major source of difficulty when estimating the high-dimensional and large-bandwidth channel, however, is the carrier frequency offset (...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on wireless communications 2019-10, Vol.18 (10), p.4636-4652
Main Authors: Javier Rodriguez-Fernandez, Javier, Gonzalez-Prelcic, Nuria
Format: Article
Language:English
Subjects:
Algorithms
Bandwidths
Carrier frequencies
carrier frequency offset
Channel estimation
Computer simulation
Estimation
Frequency synchronization
hybrid architecture
Hybrid systems
Millimeter wave MIMO
Millimeter waves
MIMO (control systems)
MIMO communication
Optimization
Performance measurement
Radio frequency
Receivers
Signal to noise ratio
Synchronization
Time synchronization
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Channel estimation at millimeter wave (mmWave) allows designing hybrid precoders and combiners to optimize performance metrics, such as the spectral efficiency. One major source of difficulty when estimating the high-dimensional and large-bandwidth channel, however, is the carrier frequency offset (CFO) that impairs the received signal. Most of the prior work assumes perfect time-frequency synchronization when estimating the channel. However, in practical systems, this assumption does not hold, and joint time-frequency synchronization is an important issue to deal with. In this paper, we propose a multi-stage algorithm for joint CFO and channel estimation. Using a training protocol between a transmitter and a receiver, we estimate first the equivalent low-SNR beamformed channel and the CFO, using the maximum-likelihood (ML) criterion. Thereafter, using these estimates, the high-dimensional multiple-input multiple-output (MIMO) channel is estimated leveraging its angular sparsity. The simulation results show that, using our proposed strategy, small estimation errors and near-optimal values of spectral efficiency can be achieved, without incurring in significant overhead and/or computational complexity.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2019.2924004