Designing unimodular sequence with good auto-correlation properties via Block Majorization-Minimization method

Sankuru, Surya Prakash; Babu, Prabhu

doi:10.1016/j.sigpro.2020.107707

Cited by 11 publications

(2 citation statements)

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“…In [30], interior point methods are utilized for generating low-sidelobe phase-coded waveforms i.e., Barker sequences of arbitrary length. Other MM based algorithms with various surrogate objective functions have been developed in [31], [32]. In [31], an algorithm utilizing the Block MM framework for the purpose of ISL metric minimization is proposed to design a sequence of arbitrary length.…”

Section: A Related Workmentioning

confidence: 99%

“…Other MM based algorithms with various surrogate objective functions have been developed in [31], [32]. In [31], an algorithm utilizing the Block MM framework for the purpose of ISL metric minimization is proposed to design a sequence of arbitrary length. In [32], a novel MM algorithm called FISL, is presented which utilizes a majorization function that attains a tighter global upper bound to the original ISL oriented cost function.…”

Section: A Related Workmentioning

confidence: 99%

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A Limited Memory BFGS Based Unimodular Sequence Design Algorithm for Spectrum-Aware Sensing Systems

Savci

2022

IEEE Access

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Unimodular sequences with good correlation and spectral properties are desirable in numerous applications such as active remote sensing and communication systems. Therefore, designing sequences with stopband and correlation sidelobe constraints has gained a lot of attention in the last few decades. In this paper, we propose a fast and efficient iterative algorithm to design unimodular and sparse frequency waveforms with low aperiodic/periodic autocorrelation sidelobes and desired stopband properties. In our approach, the bi-objective optimization problem which minimizes both the integrated sidelobe level (ISL) of the autocorrelation function and the power density in the spectral stopbands is first turned into an unconstrained single objective optimization problem and then is treated as a nonlinear large-scale problem. For the solution of the problem, we develop an algorithm based on Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) Quasi-Newton optimization method. Unlike most gradient based algorithms which employ line searches to deduce the step length, owing to L-BFGS method, unit step length is taken as a general rule to avoid the cost of computation at every iteration with very few exceptions. The calculation of gradient is based on Fast Fourier Transform and Hadamard product operations and thus the algorithm is fast and computationally efficient. Moreover, the algorithm is space efficient and its lowmemory feature makes it possible to generate long sequences. Several numerical examples are presented to validate the efficacy of the proposed method and to show its superiority over other state-of-art algorithms.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%