Sequence Set Design With Good Correlation Properties via Majorization-Minimization

TL;DR

This paper introduces efficient algorithms based on majorization-minimization for designing sequence sets with optimal correlation properties, suitable for advanced sensing and communication systems, and demonstrates their effectiveness through numerical examples.

Contribution

It develops novel FFT-accelerated algorithms for designing complementary and correlation-optimized sequence sets using majorization-minimization techniques.

Findings

Algorithms are computationally efficient due to FFT implementation.

Designed sequence sets exhibit superior correlation properties.

Numerical examples confirm the effectiveness of the proposed methods.

Abstract

Sets of sequences with good correlation properties are desired in many active sensing and communication systems, e.g., multiple-input-multiple-output (MIMO) radar systems and code-division multiple-access (CDMA) cellular systems. In this paper, we consider the problems of designing complementary sets of sequences (CSS) and also sequence sets with both good auto- and cross-correlation properties. Algorithms based on the general majorization-minimization method are developed to tackle the optimization problems arising from the sequence set design problems. All the proposed algorithms can be implemented by means of the fast Fourier transform (FFT) and thus are computationally efficient and capable of designing sets of very long sequences. A number of numerical examples are provided to demonstrate the performance of the proposed algorithms.