Training Signal Design for Correlated Massive MIMO Channel Estimation

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

Training Signal Design for Correlated Massive MIMO Channel Estimation

Type

Article

Authors

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

Date

2017-02

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.

Citation

Soltanalian, M., Naghsh, M. M., Shariati, N., Stoica, P., & Hassibi, B. (2017). Training Signal Design for Correlated Massive MIMO Channel Estimation. IEEE Transactions on Wireless Communications, 16(2), 1135–1143. doi:10.1109/twc.2016.2639485

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.