Collaborative Research: SWIFT: SHIELD: A Software-Hardware Approach for Spectrum Coexistence with Rapid Interferer Learning, Detection, and Mitigation

The last two decades have witnessed enormous increase in wireless data transfer, impacting every aspect our lives and our nation's economy. This has led to a spectrum crunch in frequency bands below 6 GHz that are the most useful for intermediate and long-range wireless communications. Even as new spectrum is allocated for different wireless networks and systems, satisfying the increasing demand of high data rates will lead to coexistence of active users (that generate signals) and passive users (that sense signals for weather sensing, astronomy, and other applications).

Inevitably, interference occurs when these users share the same frequency band or when active users operate in bands close to that used by passive users. Therefore, there is a critical need for an approach to efficiently manage and reduce such interference, which will allow for further improved spectrum usage. This project will address this challenge and focus on a cross-layer software-hardware approach for spectrum coexistence with rapid interferer learning, detection, and mitigation (SHIELD).

On a societal scale, the proposed research can improve spectrum utilization and increase access to in-demand wireless data without adversely impacting existing users, which will have direct economic impact. The broader impacts also include major outreach activities involving high school students and aiming at broadening the participation of women and underrepresented minorities, as well as incorporation of new hardware, software, and network architecture into undergraduate and graduate classes.

Specifically, this interdisciplinary project will bridge the gap between the broad areas of integrated circuits, communications, networking, and machine learning, and will focuses on enabling rapid interferer learning, detection, and mitigation for spectrum coexistence based on the co-design of novel RF hardware and network control architecture. The main activities include: (i) extensive spectrum measurements and data collection for characterizing the spectrum usage and properties of potential interferers, (ii) development of a novel reconfigurable 0.4-4.0 GHz MIMO receiver architecture leveraging a concurrent auxiliary receiver for rapid interference detection and N-path sequence-mixing for nulling specific interferers, and (iii) design of an intelligent control plane, which integrates software-defined networking and machine learning techniques, for efficient spectrum monitoring, management, and resource allocation across spatially distributed receivers.

The developed hardware and software will be evaluated in the lab setting and in real-world environments through their integration in a city-scale wireless testbed.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.