Abstract: Integrated Sensing and Communications Systems (ISAC) perform radio frequency sensing and transfer wireless data jointly. They operate in a shared and congested spectrum. ISAC systems will be an integral part of emerging 6G wireless systems. We are considering ISAC systems that cooperate or are co-designed for mutual benefits. Co-designed systems may share HW and antenna resources as well as awareness about the state of the radio spectrum. The ISAC systems have a number of operational parameters that can be adjusted either by using structured optimization or machine learning. We focus on multicarrier waveforms used by most current and emerging wireless communication systems. Similarly, multicarrier waveforms have been employed for radar purposes. We will present waveform optimization, machine learning, interference management and signal processing methods for co-designed ISAC systems that share spectrum awareness. Both structured optimization and reinforcement learning methods are developed to exploit the rich structural knowledge of man-made communication and sensing systems while learning from data. Optimization and learning impose constraints that ensure minimum desired performance levels for other sub-systems. We have also developed OFDM radar signal processing algorithms for ISAC that can take advantage of nonidealities such as carrier offsets and phase noise, commonly considered an impairment in wireless communications. We demonstrate the achieved performance gains in different sensing and communication tasks and interference management through extensive simulation and analytical results.
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