Zeng X, Franchi N, Weigel R, Pfannenmüller C (2025)
Publication Language: English
Publication Status: Submitted
Publication Type: Unpublished / Preprint
Future Publication Type: Journal article
Publication year: 2025
The development of Intelligent Reflecting Surface (IRS) technology has increased interest in addressing beam misalignment, a critical issue that can severely degrade system performance. Traditional methods typically involve determining the Direction of Arrival (DOA) to adjust the IRS beam direction; however, these approaches often introduce substantial overhead, particularly in dynamic environments. Previous research has proposed a codebook-based user tracking scheme that periodically estimates user direction and utilizes historical data to predict motion trajectories, thereby reducing overhead. Nonetheless, this methodology leaves the system in an uncontrolled state between estimations. To overcome this limitation, we propose a novel direction prediction algorithm based on particle filters that dynamically updates prediction parameters using feedback from received signals. This algorithm was implemented on an FPGA platform to assess its functionality and real-time performance. Experimental results demonstrate that the proposed method not only reduces overhead associated with additional direction estimations but also achieves a higher signal-to-noise ratio (SNR) in downlink communications, thereby enhancing overall system efficiency.
APA:
Zeng, X., Franchi, N., Weigel, R., & Pfannenmüller, C. (2025). Direction Prediction based on Particle Filter for User Tracking with Intelligent Reflecting Surface. (Unpublished, Submitted).
MLA:
Zeng, Xianshi, et al. Direction Prediction based on Particle Filter for User Tracking with Intelligent Reflecting Surface. Unpublished, Submitted. 2025.
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