Two-phase optimization of binary sequences with low peak sidelobe level value

Abstract

The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths L=2m−1, for 14≤m≤20. From the obtained results it is evident that the two-phase optimization improved the efficiency of the algorithm significantly, the solver speed is increased significantly by using graphics processing units, new-best known solutions were achieved, and the achieved peak sidelobe level values were significantly less than L.