Majorization Minimization based Memetic Algorithm for Designing Polyphase Sequences with Good Correlation Properties

Abstract

Polyphase sequences with low aperiodic autocorrelation sidelobes are well known to have extensive applications in communication and radar systems. In this paper, we present an effective algorithm named Majorization Minimization based Memetic Algorithm (MA-MM) to design such sequences. Compared with the previously proposed algorithms, this algorithm searches the solution space more efficiently to obtain a global solution with much better autocorrelation properties. It can be used to minimize the integrated sidelobe level (ISL) and peak sidelobe level (PSL), respectively. Numerical experiments are presented to illustrate the performance of the proposed algorithms. It is demonstrated that the proposed algorithms can generate sequences with much better autocorrelation properties compared with some well-known analytical sequences. In addition, the influence of some parameters on the performance of the designed sequences is investigated.