CAREER: Harnessing Interference with Deep Learning: Algorithms and Large-Scale Experiments

Villanova University, Villanova PA

Investigators

Abstract

The increasing density of cellular networks and the adoption of drones have made interference a significant obstacle to achieving efficient and high-quality communication. Existing solutions for inter-cell interference are impractical due to excessive signaling overhead and synchronization burden. This project leverages deep learning to manage interference from neighboring cells without requiring channel information for interference signals. The proposed algorithms will rely on measured signal and interference power or quality, which are available in modern cellular networks, particularly in 5G. The goal is to improve the spectral efficiency of cellular networks, enable widespread drone adoption, and simplify the design of communication systems. The project also includes outreach efforts to educate underrepresented high school students in Philadelphia. Undergraduate students will have opportunities for interdisciplinary research in wireless communications and artificial intelligence, and a new senior elective course on deep learning for communications will be designed. The findings of this research will be disseminated through high-impact journals, conferences, and workshops, benefiting both academic and industrial communities. This project will pursue a new approach for interference mitigation in two-dimensional and three-dimensional cellular networks. The proposed deep learning-aided algorithms could transform the way real-life cellular networks are designed and operated in two ways: (i) by creating new foundation for interference management that work even when channel information is unknown and the network topology dynamically changes; and (ii) designing end-to-end communication models that can adapt to interference, simplify the current blockby-block design, and yield significantly higher data rates. The proposed algorithms will undergo extensive testing on both small and large scales, and the results will be evaluated on the NSF PAWR platforms that could lead to real-world implementation. This project has the potential to significantly increase the efficiency of current and next-generation cellular networks and enable the widespread use of drones in cellular networks. 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.