A New Approach to Sequence Construction With Good Correlation by Particle Swarm Optimization

Abstract

In this paper, a novel computationally affordable method to generate long binary sequences featuring desired properties is presented, based on the use of a number of shorter non linear binary sub-sequences. The paper shows the relationship of the Auto- and Cross-Correlation (AC, CC) ofthe generated long binary sequences with the AC and CC ofconstituent sub-sequences. It is also shown that the starting bit position of sub-sequences has an important role on AC and CC of the generated sequences. To generate the optimal long binary sequence from correlation points of view, Particle Swarm Optimization (PSO) algorithm is employed. All the techniques stated in the literature to improve the PSO are implemented and it is clearly shown that the constriction factor and the variable population size turn out to have a great impact on minimizing the fitness function (RMS of AC) representing the target Correlationproperties expected for the resulting long sequence. Possible application scenarios for the long sequences generated by the proposed method are also discussed and evaluated.