Beam prediction; beam tracking; Gaussian process; hybrid model and data driven; Kalman filtering; millimeter wave communications; stochastic differential equation; Computational modelling; Data driven; Gaussian Processes; Hybrid datum; Hybrid model; Hybrid model and data driven; Kalman-filtering; Millimeterwave communications; Model-driven; Prediction algorithms; Stochastic differential equations; System Dynamics; Task analysis; Signal Processing; Electrical and Electronic Engineering
Résumé :
[en] Beam prediction and tracking (BPT) are key technology for high-frequency communications. Typical techniques include Kalman filtering and Gaussian process regression (GPR). However, Kalman filter requires explicit models of system dynamics, which are challenging to obtain, especially for complicated environments. In contrast, as a data-driven approach, there is no need to derive the system dynamics model for GPR. However, the computational complexity of GPR is often prohibitive, which makes real-time application challenging. To tackle this issue, we propose a novel hybrid model and data driven approach in this paper, which can exploit simultaneously the advantages from the two techniques while overcoming their drawbacks. In particular, the system dynamics required can be obtained in a data-driven manner. Based on a characterization of the system dynamics, we further investigate the long-term behavior of system evolution and propose two more efficient algorithms - long-term prediction and beam width optimization. We demonstrate two advantages of the proposed BPT approach. First, the computational complexity is low due to the inherent Kalman filter. Second, system performance can be significantly improved thanks to the long-term prediction and beam width optimization.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Zhang, Jianjun ; Nanjing University of Aeronautics and Astronautics, College of Computer Science and Technology, Nanjing, China
Huang, Yongming ; Southeast University, National Mobile Communications Research Laboratory, Nanjing, China
Masouros, Christos ; University College London, Department of Electronic & Electrical Engineering, London, United Kingdom
You, Xiaohu ; Southeast University, National Mobile Communications Research Laboratory, Nanjing, China
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Ottersten
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Hybrid Data-Induced Kalman Filtering Approach and Application in Beam Prediction and Tracking
Date de publication/diffusion :
2024
Titre du périodique :
IEEE Transactions on Signal Processing
ISSN :
1053-587X
Maison d'édition :
Institute of Electrical and Electronics Engineers Inc.
National Natural Science Foundation of China Foundation Research Project of Jiangsu Province Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu Engineering and Physical Sciences Research Council, U.K.
Subventionnement (détails) :
This work was supported in part by the National Natural Science Foundation of China under Grant 62301249 and Grant 62225107, in part by the Foundation Research Project of Jiangsu Province under Grant BK20230878, in part by the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001, and in part by the Engineering and Physical Sciences Research Council, U.K. under Project EP/S028455/1.
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