Advances in Spectrum Utilization for Active and Passive Users

Electromagnetic (EM) spectrum is a limited resource allocated to active radio users performing communication, sensing, navigation, imaging and passive users performing radio astronomical observation and remote sensing among other applications. Accommodating such plethora of existing and emerging applications, from scientific and engineering breakthroughs, in the fractured radio spectrum has become a significant challenge. In this thesis we identify such challenges and propose solutions curated to their requirements towards an overarching goal of improving radio spectrum utilization. Active spectrum usage is burdened by ever-increasing users and bandwidth-hungry emerging applications. This necessitates significant overhaul of current system designs or venturing into the less explored part of spectra with large contiguous bandwidths albeit with its unique challenges and a promise of record-breaking datarates - the sub-Terahertz (sub-THz) and Terahertz (THz) band. In this thesis, domain-aware data-driven methods are developed to improving designs for both parts of spectra by exploiting - 1. Fundamentals of wireless signal propagation, 2. computational capabilities of data-driven models. Such models are capable of outperforming state-of-the-art (SoTA) methods, are adaptive to changing wireless signals and media, and promotes an alternative and efficient system and protocol design for xG communication. The passive users such as radio astronomy are limited by spectrum allocation as with redshifting of spectral lines, wideband observations have become necessary beyond the protected band increasing vulnerability. Additionally, proliferation of terrestrial networks, increasingly generate unwanted radio frequency interference (RFI) in protected bands for passive users. Thus, stringent RFI mitigation techniques for their co-existence to meet their scientific goals has become a necessity. In this thesis, we propose collaborative frameworks, by distributed aggregation of concise apriori information of the interfering signals from its source and utilizing this information to mitigate the RFI encountered by passive users. These methods can adapt to the changing statistics of the RFI and are generalizable to different RFI sources. Several such collaborative methods are designed, implemented, and evaluated in this thesis and practical guidelines for system implementation of the collaborative RFI mitigation are provided.