Dynamic user motion prediction using advanced Kalman filtering in 5G mmWave systems
type: CreativeWork
description: Achieving reliable communication in high-data-rate applications and densely populated environments remains a significant challenge for next-generation wireless networks. Millimeterwave (mmWave) technology offers substantial bandwidth and data rate advantages but necessitates precise beam steering to maintain connectivity. The dynamic nature of mobile environments, coupled with diverse user behaviors and trajectories, complicates this task. Traditional beamforming approaches struggle to adapt to such scenarios, often leading to signal degradation, connectivity drops, inefficient resource utilization and more power consumption. This research presents an advanced framework that integrates historical user trajectories through beam sweeping, user tracking via sensor-based information combined with beam sweeping, and Kalman Filter-based prediction to address these challenges effectively. By leveraging key measurements such as Angle of Arrival (AoA), Received Signal Strength (RSS) and Signal-to-Noise-and-Interference Ratio (SINR), the system dynamically tracks and predicts user locations based on data gathered by beam sweeping. This research introduces a sophisticated framework that leverages historical user movement patterns, collected via IMEI-based tracking with beam sweeping over time, to define prototype trajectories. These trajectories are subsequently refined using advanced trajectory identification algorithms. Additionally, multiple sensors installed along the pathway provide approximate user positions as they move along their trajectories. The Kalman Filter, particularly its nonlinear models (Advanced Kalman Filters), significantly enhances prediction accuracy, enabling real-time tracking and prediction of user movements. This capability facilitates future beam steering and adaptive beamforming to optimize signal transmission. The proposed framework addresses several critical challenges. It effectively manages dynamic user behaviors and nonlinear trajectories thro…
abstract: Achieving reliable communication in high-data-rate applications and densely populated environments remains a significant challenge for next-generation wireless networks. Millimeterwave (mmWave) technology offers substantial bandwidth and data rate advantages but necessitates precise beam steering to maintain connectivity. The dynamic nature of mobile environments, coupled with diverse user behaviors and trajectories, complicates this task. Traditional beamforming approaches struggle to adapt to such scenarios, often leading to signal degradation, connectivity drops, inefficient resource utilization and more power consumption. This research presents an advanced framework that integrates historical user trajectories through beam sweeping, user tracking via sensor-based information combined with beam sweeping, and Kalman Filter-based prediction to address these challenges effectively. By leveraging key measurements such as Angle of Arrival (AoA), Received Signal Strength (RSS) and Signal-to-Noise-and-Interference Ratio (SINR), the system dynamically tracks and predicts user locations based on data gathered by beam sweeping. This research introduces a sophisticated framework that leverages historical user movement patterns, collected via IMEI-based tracking with beam sweeping over time, to define prototype trajectories. These trajectories are subsequently refined using advanced trajectory identification algorithms. Additionally, multiple sensors installed along the pathway provide approximate user positions as they move along their trajectories. The Kalman Filter, particularly its nonlinear models (Advanced Kalman Filters), significantly enhances prediction accuracy, enabling real-time tracking and prediction of user movements. This capability facilitates future beam steering and adaptive beamforming to optimize signal transmission. The proposed framework addresses several critical challenges. It effectively manages dynamic user behaviors and nonlinear trajectories thro…
author: Jalali, Erfan
language: en
date published: 2025-04
date modified: 2026-07-24T04:03:50Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/16802

PropertyValue
property ID: DOI
value: 10.82465/5161