TY - GEN
T1 - Efficent Large-Scale Multi-Unimodular Waveform Design with Good Correlation Properties via Direct Phase Optimizations
AU - Zhao, Xiaohan
AU - Li, Yongzhe
AU - Tao, Ran
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose an efficient algorithm for designing large-scale multi-unimodular waveforms with low correlations. Different from existing approaches that commonly involve repetitive projections of complex values into their constant-modulus approximations, we conduct optimizations directly on the phase values of waveform elements. Specifically, we optimize the weighted integrated sidelobe level of waveforms, and formulate such design into an unconstrained optimization problem with respect to phase values of waveform elements. Then, we derive the gradient of the newly formulated objective function, through which we subsequently elaborate its majorant with the support of a properly designed Lipschitz-constant related quantity. Our major contributions also lie in obtaining a closed-form update of phase values that boils down to a gradient-descent regime, and calculating the update with fast implementations. Simulation results verify the superiority of our algorithm over existing state-of-the-art methods.
AB - In this paper, we propose an efficient algorithm for designing large-scale multi-unimodular waveforms with low correlations. Different from existing approaches that commonly involve repetitive projections of complex values into their constant-modulus approximations, we conduct optimizations directly on the phase values of waveform elements. Specifically, we optimize the weighted integrated sidelobe level of waveforms, and formulate such design into an unconstrained optimization problem with respect to phase values of waveform elements. Then, we derive the gradient of the newly formulated objective function, through which we subsequently elaborate its majorant with the support of a properly designed Lipschitz-constant related quantity. Our major contributions also lie in obtaining a closed-form update of phase values that boils down to a gradient-descent regime, and calculating the update with fast implementations. Simulation results verify the superiority of our algorithm over existing state-of-the-art methods.
KW - Direct phase optimization
KW - gradient
KW - majorization-minimization
KW - unimodular waveform design
UR - http://www.scopus.com/inward/record.url?scp=85177587069&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10095743
DO - 10.1109/ICASSP49357.2023.10095743
M3 - Conference contribution
AN - SCOPUS:85177587069
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -