Mauro Belgiovine PhD Proposal Review

Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation, Beam Refinement, and Protocol Classification for Next Generation Networks

Committee Members:
Prof. Kaushik Chowdhury (advisor)
Prof. Stratis Ioannidis
Dr. Chris Dick

Abstract:
his thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets, with a focus on enhancing both local and cellular wireless networks through the deployment of highly customized deep learning solutions that target specific bottlenecks affecting traditional signal processing based communication.

The research delves into three key areas that address critical challenges in the current wireless landscape. The first focal point of the investigation involves channel estimation using deep learning techniques to denoise pilots and expedite the accurate estimation of Channel State Information (CSI). By leveraging deep learning methodologies, the proposed solution aims to enhance the reliability and computation for MIMO and massive MIMO channel estimation, thereby contributing to improved communication efficiency and reduced errors. The second major topic encompasses the application of reinforcement learning for 5G New Radio (NR) millimeter-wave (mmWave) beam refinement. The study aims to develop a Deep Reinforcement Learning algorithm capable of adjusting beamsteering angles, starting from a coarse beam scanning procedure and further refining them for higher transmission efficiency. This innovation is expected to substantially decrease traffic overhead while simultaneously enhancing beam steering precision, thus optimizing the performance of mmWave communication. The third and final area of focus introduces a transformer-based WiFi multi-protocol classifier, strategically deployed on a DeepWave Air-T edge device, which is equipped with Module on Chip (MoC) low power CPU-GPU and programmable Software Defined Radio (SDR). This classifier outperforms existing modulation classification models and legacy methods under lower SNR conditions, leveraging TensorRT’s model compression capabilities to efficiently process extended sequences of raw IQ samples, ensuring high performance at a low computational cost. The proposed solution addresses the growing demand for efficient and adaptable wireless communication systems, paving the way for advancements in edge-based processing and intelligent protocol classification.

This work seeks to contribute significantly to the ongoing AI revolution in wireless systems by addressing crucial issues in channel estimation, beam refinement, and protocol classification. The outcomes of this research hold the potential to redefine the landscape of wireless communication, offering enhanced performance, reduced overhead, and increased adaptability in both local and cellular networks.