The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machine learning (ML) to LOS/NLOS classification and this paper is part of this trend, but not all the ...
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Machine Learning-Based Human Detection Using Active Non-Line-of-Sight Laser Sensing. [PDF]
Çelebi S, Türkoğlu İ.
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Adaptive Kalman Filter-Based UWB Location Tracking with Optimized DS-TWR in Workshop Non-Line-of-Sight Environments. [PDF]
Wu J, Xiong Y, Li W, Xia W.
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AI-Driven Real-Time Phase Optimization for Energy Harvesting-Enabled Dual-IRS Cooperative NOMA Under Non-Line-of-Sight Conditions. [PDF]
Al-Ghafri Y +3 more
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Improving QoS for streaming data transmission over 6G networks using reconfigurable intelligent surfaces (RIS). [PDF]
Ibrahim HM +3 more
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Robust ISAC based framework for location estimation and target detection in 6G networks. [PDF]
Soni L +3 more
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3D Urban Outdoor WiFi 7 Network Planning and Analysis Using Ray-Tracing and Machine Learning: Transformer-Based Surrogate Modeling for High-Resolution Digital Twin. [PDF]
Trînc EC +5 more
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An Improved Adaptive Kalman Filter Positioning Method Based on OTFS. [PDF]
Xia S, Liu A, Liang X.
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Application of a temporal convolutional network algorithm fused with channel attention module for UWB indoor positioning. [PDF]
He L +5 more
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Multipath Credibility Selection for Robust UWB Angle-of-Arrival Estimation in Narrow Underground Corridors. [PDF]
Li J +6 more
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