Minimax Design of Constant Modulus MIMO Waveforms

Zhen Lin; Wenqiang Pu; Zhi Quan Luo

doi:10.1109/ACSSC.2018.8645521

Minimax Design of Constant Modulus MIMO Waveforms

Zhen Lin
, Wenqiang Pu
, Zhi Quan Luo

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Waveform optimization is a crucial step in the design of a multiple-input multiple-output (MIMO) system. This paper considers the joint optimization of constant modulus waveforms and mismatched (or matched) receive filters to suppress the auto- and cross-correlations using the minimax (ell-{infty}) design criterion. For practical waveform length and system size, the waveform design problem becomes quite challenging due to the large problem size (more than 10 {5} unimodular complex variables and 10 {6} nonlinear constraints). In addition to the large size, this problem is nonconvex, nonsmooth, and as such, can not be handled effectively by the existing waveform design algorithms or off-the-shelve optimization tools. This paper develops an efficient primal-dual type algorithm with low per-iteration complexity to solve this problem. Numerical comparison shows that the waveforms based on the minimax design outperform those obtained from the existing ell-{2} norm design by 4-5 dBs in terms of peak sidelobe levels (PSL).

Original language	English (US)
Title of host publication	Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1889-1893
Number of pages	5
ISBN (Electronic)	9781538692189
DOIs	https://doi.org/10.1109/ACSSC.2018.8645521
State	Published - Jul 2 2018
Event	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: Oct 28 2018 → Oct 31 2018

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2018-October
ISSN (Electronic)	2576-2303

Conference

Conference	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/Territory	United States
City	Pacific Grove
Period	10/28/18 → 10/31/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

MIMO system
auto-/cross-correlation
mismatched (or matched) filters
primaldual method
unimodular waveforms

Publisher link

10.1109/ACSSC.2018.8645521

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Lin, Z., Pu, W., & Luo, Z. Q. (2018). Minimax Design of Constant Modulus MIMO Waveforms. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 1889-1893). Article 8645521 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645521

Minimax Design of Constant Modulus MIMO Waveforms. / Lin, Zhen; Pu, Wenqiang; Luo, Zhi Quan.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2018. p. 1889-1893 8645521 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, Z, Pu, W & Luo, ZQ 2018, Minimax Design of Constant Modulus MIMO Waveforms. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645521, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 1889-1893, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 10/28/18. https://doi.org/10.1109/ACSSC.2018.8645521

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abstract = "Waveform optimization is a crucial step in the design of a multiple-input multiple-output (MIMO) system. This paper considers the joint optimization of constant modulus waveforms and mismatched (or matched) receive filters to suppress the auto- and cross-correlations using the minimax (ell-\{infty\}) design criterion. For practical waveform length and system size, the waveform design problem becomes quite challenging due to the large problem size (more than 10 \{5\} unimodular complex variables and 10 \{6\} nonlinear constraints). In addition to the large size, this problem is nonconvex, nonsmooth, and as such, can not be handled effectively by the existing waveform design algorithms or off-the-shelve optimization tools. This paper develops an efficient primal-dual type algorithm with low per-iteration complexity to solve this problem. Numerical comparison shows that the waveforms based on the minimax design outperform those obtained from the existing ell-\{2\} norm design by 4-5 dBs in terms of peak sidelobe levels (PSL).",

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N2 - Waveform optimization is a crucial step in the design of a multiple-input multiple-output (MIMO) system. This paper considers the joint optimization of constant modulus waveforms and mismatched (or matched) receive filters to suppress the auto- and cross-correlations using the minimax (ell-{infty}) design criterion. For practical waveform length and system size, the waveform design problem becomes quite challenging due to the large problem size (more than 10 {5} unimodular complex variables and 10 {6} nonlinear constraints). In addition to the large size, this problem is nonconvex, nonsmooth, and as such, can not be handled effectively by the existing waveform design algorithms or off-the-shelve optimization tools. This paper develops an efficient primal-dual type algorithm with low per-iteration complexity to solve this problem. Numerical comparison shows that the waveforms based on the minimax design outperform those obtained from the existing ell-{2} norm design by 4-5 dBs in terms of peak sidelobe levels (PSL).

AB - Waveform optimization is a crucial step in the design of a multiple-input multiple-output (MIMO) system. This paper considers the joint optimization of constant modulus waveforms and mismatched (or matched) receive filters to suppress the auto- and cross-correlations using the minimax (ell-{infty}) design criterion. For practical waveform length and system size, the waveform design problem becomes quite challenging due to the large problem size (more than 10 {5} unimodular complex variables and 10 {6} nonlinear constraints). In addition to the large size, this problem is nonconvex, nonsmooth, and as such, can not be handled effectively by the existing waveform design algorithms or off-the-shelve optimization tools. This paper develops an efficient primal-dual type algorithm with low per-iteration complexity to solve this problem. Numerical comparison shows that the waveforms based on the minimax design outperform those obtained from the existing ell-{2} norm design by 4-5 dBs in terms of peak sidelobe levels (PSL).

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Minimax Design of Constant Modulus MIMO Waveforms

Abstract

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