Ambiguity Function Optimization Based on Joint Communication and Sensing Waveform

The ambiguity function (AF) plays a critical role in joint communication and sensing (JCAS) systems. In this letter, we design a novel JCAS waveform and analyze its AF characteristics. The cut view of the AF, it is observed that the modulation and variation of communication data do not affect the zeros‐delay cut, while the zero‐Doppler cut changes with the modulation data. Since variations in zero‐Doppler cut lead to unnecessary fluctuations and higher sidelobe levels, optimizing zero‐Doppler cut for a more concentrated mainlobe and sidelobe suppression is crucial. Based on this insight, we develop a hybrid optimization algorithm, termed QGPV. It integrates the quantum genetic algorithm (QGA), quantum particle swarm optimization (QPSO), and the variable neighborhood search (VNS). The proposed algorithm QGPV reduces the sidelobe level and improves the overall resolution performance. Simulation results show that the optimized waveform achieves excellent delay and Doppler resolution, making it suitable for JCAS applications.