Flexible Density-based Multipath Component Clustering Utilizing Ground Truth Pose
(2023) IEEE 98th Vehicular Technology Conference (VTC2023-Fall)- Abstract
- Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE... (More)
- Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE base station by a passenger vehicle driving in an urban environment with a massive antenna array on the roof are used to evaluate the repeatability and intuitiveness of the proposed clustering algorithm. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c9118fb1-3921-4400-ac0a-d124c824451f
- author
- Whiton, Russ LU ; Chen, Junshi LU and Tufvesson, Fredrik LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2023 98th Vehicular Technology Conference (VTC2023-Fall)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
- conference location
- Hong Kong, China
- conference dates
- 2023-10-10 - 2023-10-13
- language
- English
- LU publication?
- yes
- id
- c9118fb1-3921-4400-ac0a-d124c824451f
- date added to LUP
- 2023-09-08 13:22:14
- date last changed
- 2023-09-15 10:56:00
@inproceedings{c9118fb1-3921-4400-ac0a-d124c824451f, abstract = {{Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE base station by a passenger vehicle driving in an urban environment with a massive antenna array on the roof are used to evaluate the repeatability and intuitiveness of the proposed clustering algorithm.}}, author = {{Whiton, Russ and Chen, Junshi and Tufvesson, Fredrik}}, booktitle = {{2023 98th Vehicular Technology Conference (VTC2023-Fall)}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Flexible Density-based Multipath Component Clustering Utilizing Ground Truth Pose}}, url = {{https://lup.lub.lu.se/search/files/157774844/Clustering_Paper_2023_09_08_Final_Submission_Version.pdf}}, year = {{2023}}, }