Training Signal Design for Correlated Massive MIMO Channel Estimation

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

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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 languageEnglish (US)
Pages (from-to)1135-1143
Number of pages9
JournalIEEE Transactions on Wireless Communications
Issue number2
StatePublished - Feb 2017
Externally publishedYes

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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