TY - GEN
T1 - Minimax Design of Constant Modulus MIMO Waveforms
AU - Lin, Zhen
AU - Pu, Wenqiang
AU - Luo, Zhi-Quan
PY - 2019/2/19
Y1 - 2019/2/19
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).
KW - MIMO system
KW - auto-/cross-correlation
KW - mismatched (or matched) filters
KW - primaldual method
KW - unimodular waveforms
UR - http://www.scopus.com/inward/record.url?scp=85062938831&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062938831&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2018.8645521
DO - 10.1109/ACSSC.2018.8645521
M3 - Conference contribution
AN - SCOPUS:85062938831
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1889
EP - 1893
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Y2 - 28 October 2018 through 31 October 2018
ER -