Towards Explainability and Domain Knowledge-inspired Design of Online Real-Time Learning Techniques in NextG Wireless Systems

Abstract

The air interface of Next-generation (NextG) cellular and wireless networks are expected to incorporate artificial intelligence (AI) and machine learning (ML) in order to meet increasingly stringent performance requirements. Multiple-input multiple-output (MIMO) technology, including massive MIMO and subsequent variants, has played a central role across successive cellular generations, accompanied by continuous design and complexity evolution. AI/ML-based techniques can play a promising role in meeting these stringent performance demands especially with MIMO in NextG systems. However, designing AI/ML-driven approaches for the NextG air interface remains challenging due to the wide range of possible system configurations, the extremely dynamic nature of wireless channels, and the need for adaptability to real-time operational adjustments. Therefore, online and real-time AI/ML approaches can play a key enabling role in realizing this ambitious vision for NextG. To this end, this dissertation first introduces the theoretical underpinnings of online real-time learning architectures based on reservoir computing (RC). The effectiveness of RC in orthogonal frequency division multiplexing (OFDM) and MIMO-OFDM receive processing is established from the ground up with first principles, resulting in enhanced explainability and interpretability of RC-based architectures, thereby turning opaque ``black-box'' models into intuitive ``gray-box'' models. This solid foundation, founded on signal processing and information theory fundamentals, enables the systematic development of procedures to incorporate domain knowledge into the design of RC-based architectures, resulting in significantly improved performance, which is demonstrated in the context of OFDM and MIMO-OFDM receive processing, user beam tracking in massive MIMO systems and near real-time jamming detection and classification in NextG systems. This dissertation emphasizes the crucial role of explainability of AI/ML solutions deployed in NextG wireless systems, and the foundations laid in this dissertation provide a potential roadmap for developing explainable and domain knowledge-guided AI/ML-based techniques in NextG.