Interference Management For Next-Generation Dynamic Spectrum Sharing

The exponential growth of wireless devices and bandwidth-intensive applications has intensified the demand for efficient spectrum utilization, exposing the limitations of traditional static spectrum allocation schemes. Next-generation spectrum sharing has emerged as a transformative solution to enhance spectrum efficiency by enabling multiple wireless systems to coexist opportunistically in the same frequency band and geographic area. However, such dynamism introduces significant challenges, particularly in interference management, which is critical to enabling reliable coexistence, ensuring access priority, and building mutual trust across diverse applications. This thesis advances interference management by developing advanced techniques for interference prediction, monitoring, and control in dynamic spectrum sharing environments. Key contributions include the Channel Estimation via Loss Field (CELF) model for accurate and rapid channel loss prediction, the augmented CELF model to enhance explainability and robustness under uncertainty, and a Full-Duplex spectrum Monitoring system (FDMonitor) developed and deployed on experimental testbeds. The CELF work uses channel loss measurements from a deployed network area and a Bayesian linear regression method to estimate a site-specific loss field for the area. Real-world indoor and outdoor datasets validate that CELF is more accurate than common machine learning (ML) benchmarks and is less computationally complex to train. A continued work on CELF explores different base models and spatial multipath effects for the modeling components in CELF. It also verifies analytically and numerically the explainability of CELF, and validates its robust performance in complex environments. The FDMonitor system uses a bidirectional coupler, a two-port receiver, and a new source separation algorithm to simultaneously and adaptively estimate the transmitted signal and the signal incident on the antenna. FDMonitor has been running on POWDER, a large-scale wireless experimental testbed, since 2021, monitoring 19 SDR platforms accessible by outside experimenters. Results show that it achieves a low false alarm rate over 27 months of operation. Together, these solutions—supported by statistical models and extensive experimental validation—offer scalable, efficient, and trustworthy interference management strategies to enhance spectrum efficiency and boost openness in wireless applications like radio dynamic zones and private cellular networks.