Training Signal Design for Correlated Massive MIMO Channel Estimation

Mojtaba Soltanalian; Mohammad Mahdi Naghsh; Nafiseh Shariati; Petre Stoica; Babak Hassibi

doi:10.1109/twc.2016.2639485

Training Signal Design for Correlated Massive MIMO Channel Estimation

Mojtaba Soltanalian, Mohammad Mahdi Naghsh, Nafiseh Shariati, Petre Stoica, Babak Hassibi

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

In this paper, we propose a new approach to the design of training sequences that can be used for an accurate estimation of multi-input multi-output channels. The proposed method is particularly instrumental in training sequence designs that deal with three key challenges: 1) arbitrary channel and noise statistics that do not follow specific models, 2) limitations on the properties of the transmit signals, including total power, per-antenna power, having a constant-modulus, discrete-phase, or low peak-to-average-power ratio, and 3) signal design for large-scale or massive antenna arrays. Several numerical examples are provided to examine the proposed method.

Original language	English (US)
Pages (from-to)	1135-1143
Number of pages	9
Journal	IEEE Transactions on Wireless Communications
Volume	16
Issue number	2
DOIs	https://doi.org/10.1109/twc.2016.2639485
State	Published - Feb 2017
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2021-04-06
Acknowledgements: This work was supported in part by the European Research Council, in part by the Swedish Research Council, in part by the U.S. National Science Foundation under Grant CNS-0932428, Grant CCF-1018927, Grant CCF-1423663, and Grant CCF-1409204, in part by Qualcomm Inc., in part by the NASA's Jet Propulsion Laboratory through the President and Director's Fund, in part by King Abdulaziz University, and in part by the King Abdullah University of Science and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/twc.2016.2639485

Cite this

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title = "Training Signal Design for Correlated Massive MIMO Channel Estimation",

abstract = "In this paper, we propose a new approach to the design of training sequences that can be used for an accurate estimation of multi-input multi-output channels. The proposed method is particularly instrumental in training sequence designs that deal with three key challenges: 1) arbitrary channel and noise statistics that do not follow specific models, 2) limitations on the properties of the transmit signals, including total power, per-antenna power, having a constant-modulus, discrete-phase, or low peak-to-average-power ratio, and 3) signal design for large-scale or massive antenna arrays. Several numerical examples are provided to examine the proposed method.",

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AU - Hassibi, Babak

N1 - KAUST Repository Item: Exported on 2021-04-06 Acknowledgements: This work was supported in part by the European Research Council, in part by the Swedish Research Council, in part by the U.S. National Science Foundation under Grant CNS-0932428, Grant CCF-1018927, Grant CCF-1423663, and Grant CCF-1409204, in part by Qualcomm Inc., in part by the NASA's Jet Propulsion Laboratory through the President and Director's Fund, in part by King Abdulaziz University, and in part by the King Abdullah University of Science and Technology. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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AB - In this paper, we propose a new approach to the design of training sequences that can be used for an accurate estimation of multi-input multi-output channels. The proposed method is particularly instrumental in training sequence designs that deal with three key challenges: 1) arbitrary channel and noise statistics that do not follow specific models, 2) limitations on the properties of the transmit signals, including total power, per-antenna power, having a constant-modulus, discrete-phase, or low peak-to-average-power ratio, and 3) signal design for large-scale or massive antenna arrays. Several numerical examples are provided to examine the proposed method.

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Training Signal Design for Correlated Massive MIMO Channel Estimation

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