Anu Jagannath PhD Dissertation Defense

Announcing:
PhD Dissertation Defense

Name:
Anu Jagannath

Title:
Deep Learning at the Edge for Future G Networks: RF Signal Intelligence for Comprehensive Spectrum Awareness

Date:
4/4/2024

Time:
1:00:00 PM

Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Kaushik Chowdhury
Prof. Yanzhi Wang

Abstract:
Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness at the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification or radio frequency fingerprinting – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this dissertation, deep learning is applied to the various signal recognition problems from  a multi-task perspective with an emphasis on edge deployment. To address edge deployment, various techniques are applied to solve the signal recognition problem under consideration (modulation, wireless protocol, emitter fingerprint recognition) to design scalable and computationally efficient framework. While designing the edge deployable architectures, the generalization capability of the architectures are evaluated under various circumstances to quantify their performance under real-world settings such as emissions from actual emitters (commercial emissions wherever applicable), training with a different propagation scenario and testing under a never-before-seen setting.

The study was sectioned into different stages where multi-task learning is first applied to solving wireless standard and modulation recognition, followed by applying deep compression for CBRS radar waveform classification, next radio frequency fingerprinting for commercial WiFi and Bluetooth emissions were studied utilizing novel multi-task attentional architectures, and finally the multi-task learning together with deep compression was employed to deploy the architectures in a real-time streaming radio testbed for real-time inferencing of wireless standard and modulation recognition. The feasibility of employing deep compression techniques are carefully evaluated in a real-world deployment setting to quantify the performance from a computational and inference capacity perspective.