Self-Interference Mitigation in Full-Duplex Systems: Signal Modeling, Rate Optimization, and Application

Future wireless networks are converging towards a unified communication, sensing, and computing platform with ultra high speed and low latency requirement that calls for optimized co-design of the control and data planes. Full Duplex (FD) communication is proposed as one of the promising wireless candidates to meet such demand due to its inherent capability to enable simultaneous transmission and reception in the same frequency band. However, the fundamental challenge of FD communications is the Self-Interference (SI) signal induced to the Receiver (RX) chains of the FD node by its own Transmitter (TX). This thesis investigates SI mitigation techniques for FD wireless systems employing single-antenna, Multiple-Input Multiple-Output (MIMO), and massive MIMO radios. First, we provide comprehensive signal modeling of the FD systems considering practical hardware impairments, which is utilized to devise adaptive digital SI cancellation algorithms based on truncated singular value decomposition and Deep Neural Network architecture. Then, we present a novel unified FD massive MIMO transceiver architecture comprising Analog and Digital (A/D) TX/RX BeamForming (BF) as well as A/D SI cancellation, which are jointly optimized for various performance objectives and complexity requirements. Finally, the application of FD radios in emerging Integrated Sensing and Communication (ISAC) systems is studied for simultaneous radar target sensing and maximized downlink data transmission. We develop novel sensing algorithms capable of estimating the radar targets' Direction of Arrival (DoA), range, and relative velocity. Representative numerical simulations are presented to validate the proposed FD schemes compared to the state-of-the-art techniques.