A Learning-Inspired Strategy to Design Binary Sequences with Good Correlation Properties: SISO and MIMO Radar Systems

In this article, the design of binary sequences exhibiting low values of aperiodic/periodic correlation functions, in terms of integrated sidelobe level (ISL), is pursued via a learning-inspired method. Specifically, the synthesis of either a single or a burst of codes is addressed, with reference to both single-input single-output (SISO) and multiple-input multiple-output (MIMO) radar systems. Two optimization machines, referred to as two- and single-layer binary sequence correlation networks, able to learn actions to design binary sequences with small ISL/complementary ISL for SISO and MIMO systems are proposed. These two networks differ in terms of the capability to synthesize low-correlation-zone sequences and computational cost. Numerical experiments show that proposed techniques can outperform state-of-the-art algorithms for the design of binary sequences and complementary sets of sequences in terms of ISL and, interestingly, of peak sidelobe level.