The University of Southampton
University of Southampton Institutional Repository

Sparse, group-sparse and online Bayesian learning aided channel estimation for doubly-selective mmWave hybrid MIMO OFDM systems

Sparse, group-sparse and online Bayesian learning aided channel estimation for doubly-selective mmWave hybrid MIMO OFDM systems
Sparse, group-sparse and online Bayesian learning aided channel estimation for doubly-selective mmWave hybrid MIMO OFDM systems
Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier’s quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequencydomain (FD) correlation of the channel’s frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.
0090-6778
5843 - 5858
Srivastava, Suraj
d1cf72bf-db1d-4e5c-86a8-d4badc5a5b94
Patro, Ch Suraj Kumar
adc763ab-8111-40ee-b13d-aa2da34fbcb6
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Srivastava, Suraj
d1cf72bf-db1d-4e5c-86a8-d4badc5a5b94
Patro, Ch Suraj Kumar
adc763ab-8111-40ee-b13d-aa2da34fbcb6
Jagannatham, Aditya K.
ea2f628b-0f2a-48a3-a293-122c809757aa
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Srivastava, Suraj, Patro, Ch Suraj Kumar, Jagannatham, Aditya K. and Hanzo, Lajos (2021) Sparse, group-sparse and online Bayesian learning aided channel estimation for doubly-selective mmWave hybrid MIMO OFDM systems. IEEE Transactions on Communications, 69 (9), 5843 - 5858. (doi:10.1109/TCOMM.2021.3085344).

Record type: Article

Abstract

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier’s quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequencydomain (FD) correlation of the channel’s frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

Text
mmWave_MIMO_OFDM - Accepted Manuscript
Download (3MB)

More information

Accepted/In Press date: 22 May 2021
e-pub ahead of print date: 2 June 2021

Identifiers

Local EPrints ID: 449495
URI: http://eprints.soton.ac.uk/id/eprint/449495
ISSN: 0090-6778
PURE UUID: b0b13ceb-00ae-42f2-9cdd-9d44c16b2325
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 03 Jun 2021 16:31
Last modified: 18 Mar 2024 05:13

Export record

Altmetrics

Contributors

Author: Suraj Srivastava
Author: Ch Suraj Kumar Patro
Author: Aditya K. Jagannatham
Author: Lajos Hanzo ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×