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DTSTART;TZID=America/New_York:20231207T100000
DTEND;TZID=America/New_York:20231207T110000
DTSTAMP:20260512T144511
CREATED:20231204T185514Z
LAST-MODIFIED:20231204T185514Z
UID:40692-1701943200-1701946800@coe.northeastern.edu
SUMMARY:Mauro Belgiovine PhD Proposal Review
DESCRIPTION:Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation\, Beam Refinement\, and Protocol Classification for Next Generation Networks \nCommittee Members:\nProf. Kaushik Chowdhury (advisor)\nProf. Stratis Ioannidis\nDr. Chris Dick \nAbstract:\nhis 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. \nThe 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. \nThis 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.
URL:https://coe.northeastern.edu/event/mauro-belgiovine-phd-proposal-review/
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DTSTART;TZID=America/New_York:20231207T140000
DTEND;TZID=America/New_York:20231207T150000
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CREATED:20231204T190346Z
LAST-MODIFIED:20231204T190346Z
UID:40688-1701957600-1701961200@coe.northeastern.edu
SUMMARY:Yisi Liu MS Thesis Defense
DESCRIPTION:Title: Experimental research on the Nonlinear Magnetoelectric Effect of the VLF ME Antennas \nCommittee Members:\nProf. Nian Sun (Advisor)\nProf. Yongmin Liu\nProf. Xufeng Zhang \nAbstract:\nMagnetoelectric (ME) coupling effects in ferromagnetic and piezoelectric composites involve the control of electric polarization (P) by applying a magnetic field (H) (direct ME effect)\, or the manipulation of magnetization (M) through an electric field (E) (converse ME effect) . These effects are facilitated by the mechanical deformation in the ferroic phases resulting from the combination of magnetostriction and piezoelectricity. In single-phase materials\, the breakthrough in achieving large ME coefficients has further advanced the development of ME materials and devices. Consequently\, numerous multifunctional ME devices\, such as mechanical antennas\, magnetic sensors\, tunable inductors\, and filters\, have been developed. This thesis has provides a summary and categorization of these devices based on their physical mechanism and type of ME effects. The inclusion of mechanical ME antennas based on piezoelectric/magnetostrictive heterostructures with acoustic actuation reflects the significant interest in this topic. Notably\, a maximum communication distance of 120 m for a very low frequency (VLF) communication system has been achieved using a pair of mechanical ME antennas. Subsequently\, we will focus on introducing and reviewing the materials and devices related to the ME effect\, as well as the application of ME mechanical antennas in very low frequency (VLF) communication systems. \nIn addition to that\, we developed a transmitter with a Metglas/PZT/Metglas structure antenna. Our study focuses on investigating the transmission effects of this antenna when employing direct antenna modulation techniques to enhance data transmission. Through our research\, we have introduced a novel modulation method by modulating the antenna. We observed that this modulation method produces a more stable and stronger signal. \n 
URL:https://coe.northeastern.edu/event/yisi-liu-ms-thesis-defense/
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