ERI: An Adaptive Incremental Deep Learning Architecture for Real-Time Inference of RF Signals in Dynamic Spectrum Sharing Environments

University Of Massachusetts, Dartmouth, North Dartmouth MA

Investigators

Abstract

5G and beyond, the next generation of wireless communication technology, will provide higher capacity, faster speeds, and world-wide connectivity, transforming the way we live, work, learn and entertain. The development of 5G alone impacts our economy and workforce by contributing $1.4 to $1.7 trillion to US gross domestic product over the next decade and create 4.6 million 5G-related jobs through 2034. The dramatic growth and ever-increasing functionality and performance of wireless devices has crowed the electromagnetic spectrum. Dynamic spectrum sharing in 5G and beyond can meet the demands of scarce spectrum, unprecedented traffic, and better quality of service. For example, unlicensed and license-aided bands, such as 2.4 - 5 GHz industrial, scientific and medical bands, 6 GHz radio frequency bands, 60 GHz millimeter wave bands, are being shared for commercial and scientific use. However, dynamically sharing spectrum poses additional challenges to share autonomously, reliably, and securely among civilian, government, and defense. Therefore, learning surrounding wireless signals is essential to support wireless user coexistence over shared spectrum. This project focuses on developing an adaptive incremental deep learning architecture to infer radio frequency signals in dynamic spectrum sharing environments in real time. The proposed research will provide recommendations to Institute of Electrical and Electronics Engineers (IEEE) Standard P1900.8 on criteria for evaluating the performance of machine learned spectrum awareness models. The integration of research and education activities and outreach activities will broaden participation of underrepresented groups in science, technology, engineering, and mathematics fields and benefit local community collaborations through service learning-based curriculum development. The goal of this project is to develop an incremental deep learning architecture for adaptively and efficiently detecting, classifying, and demodulating radio frequency signals in dynamic spectrum sharing environments in real time with online learning capabilities. The proposed incremental deep-learning architecture will (1) adaptively learn a wide range of wireless communication scenarios, starting from a small dataset with limited known signals or scenarios and then incrementally learning new signals or scenarios as well as updating the deep learning network in an online manner without interruption to re-train the whole network and a man-in-the-middle to label the signal; (2) advance self-learning the spectrum including spectrum sensing, signal classification and radio frequency parameter characterization in real-time; and (3) improve deep learning-based signal demodulation with the adaptive incremental learning, which can replace conventional block-based demodulation processes and preserve the same performance with high flexibility. 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.