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https://hdl.handle.net/20.500.11851/12723| Title: | Predictive Beamforming With Distributed MIMO and NLOS Identification | Authors: | Akçalı, Hasret Taha Demir, Özlem Tuǧfe Girici, Tolga |
Keywords: | Distributed MIMO Extended Kalman Filter NLOS Identification Predictive Beamforming Sensing-Assisted Beamforming Beamforming Communication Channels (Information Theory) Identification (Control Systems) Millimeter Waves Mimo Radar Mimo Systems Condition Distributed Multiple-Input Multiple-Output Line Of Sight Multiple Inputs Multiple Outputs Non-Line-Of-Sight Identification Nonline Of Sight Predictive Beamforming Road Sides Sensing-Assisted Beamforming Extended Kalman Filters |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | Radar-assisted beamforming in millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) significantly reduces beam tracking overhead, making it particularly beneficial for communication between road-side units (RSUs) and vehicles. Most existing studies assume a single RSU and line-of-sight (LoS) conditions. In this work, we consider multiple RSUs performing mmWave distributed MIMO transmission, where temporary non-line-of-sight (NLoS) conditions arise due to obstructions and blockages. Numerical results demonstrate that combining Bayesian NLoS detection with extended Kalman filtering (EKF) in a distributed MIMO framework ensures nearly uniform coverage and high data rate performance. © 2025 Elsevier B.V., All rights reserved. | Description: | Browsy; CIS Arge; PTT Teknoloji | URI: | https://doi.org/10.1109/SmartNets65254.2025.11106864 https://hdl.handle.net/20.500.11851/12723 |
ISBN: | 9798331511968 |
| Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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