ADVANCED SIGNALING FOR INTEGRATED SENSING AND COMMUNICATION IN WIRELESS NETWORKS

Wireless systems have advanced to support ever-growing demands for high data rates in every generation. While the data rate will remain a key metric, next-generation wireless systems will be more versatile, incorporating additional functionalities such as computing and sensing. Particularly, the integration of sensing and communication has attracted interest due to its potential to enable new applications such as precision agriculture, extended reality, and robotics. At the core of this transition is multiple-input multiple-output (MIMO) technology, which is critical to both sensing and communications. Moreover, the exploration of the upper mid-band and mmWave, in conjunction with an increasing number of antennas, promises higher throughput and accurate sensing, but also poses new technological challenges. In this dissertation, we address these challenges by exploring advanced MIMO signaling techniques for integrated sensing and communication (ISAC) over wireless networks. First, we study the integration of sensor and channel data for predicting beamforming in air-to-ground massive MIMO communication. We show that sensor measurements obtained from on-board sensors like the inertial measurement unit (IMU) and Global Positioning System (GPS), combined with channel state information (CSI), enhance the UAV’s motion and beam predictions. Next, we explore the design of constant modulus waveforms for radar-centric ISAC systems, to address the peak-to-average power ratio (PAPR) constraint. To improve the space-time resolution of the designed waveform, we propose the joint optimization of the ambiguity function and beampattern, which is crucial for many radar applications like positioning and imaging. Then, we propose a low-complexity ISAC waveform design strategy, called the spatial-division ISAC waveform. The proposed SD-ISAC method separates the communication and sensing waveform design tasks, even in the joint transmission regime, offering advantages such as low complexity and high compatibility with traditional algorithms. Lastly, we propose a joint polarimetric and communication framework, which extends the ISAC paradigm into the polarization domain. Specifically, we study the use of polarization-reconfigurable antennas to adapt to polarization mismatch in both sensing and communication channels.