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In this paper, the design of binary sequences exhibiting low values ofaperiodic/periodic correlation functions, in terms of Integrated Sidelobe Level(ISL), is pursued via a learning-inspired method. Specifcally, the synthesis ofeither a single or a burst of codes is addressed, with reference to bothSingle-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)radar systems. Two optimization machines, referred to as two-layer andsingle-layer Binary Sequence Correlation Network (BiSCorN), able to learnactions to design binary sequences with small ISL/Complementary ISL (CISL) forSISO and MIMO systems are proposed. These two networks differ in terms of thecapability to synthesize Low-Correlation-Zone (LCZ) sequences and computationalcost. Numerical experiments show that proposed techniques can outperformstate-of-the-art algorithms for the design of binary sequences andComplementary Sets of Sequences (CSS) in terms of ISL and, interestingly, ofPeak Sidelobe Level (PSL).

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