Censored Multipath Component Cross-Polarization Ratio Modeling
(2017) In IEEE Wireless Communications Letters 6(1). p.82-85- Abstract
In wireless channel measurements, the relatively weak cross-polarized multipath components (MPCs) are typically severely affected by the measurement noise level. As shown in this letter, the typical cross-polarization ratio (XPR) model parameter estimation, which ignores the existence of censored samples, may lead to significant errors. We demonstrate how to achieve accurate parameter estimates with a maximum likelihood estimator that properly takes into account both the measured XPRs and the censored samples. Also, a new XPR model is presented in which the average XPR is modeled as a function of the MPC excess loss. The new model is shown to be insensitive to the channel measurement noise level. A practical example with measured data... (More)
In wireless channel measurements, the relatively weak cross-polarized multipath components (MPCs) are typically severely affected by the measurement noise level. As shown in this letter, the typical cross-polarization ratio (XPR) model parameter estimation, which ignores the existence of censored samples, may lead to significant errors. We demonstrate how to achieve accurate parameter estimates with a maximum likelihood estimator that properly takes into account both the measured XPRs and the censored samples. Also, a new XPR model is presented in which the average XPR is modeled as a function of the MPC excess loss. The new model is shown to be insensitive to the channel measurement noise level. A practical example with measured data in an indoor environment at 60 GHz demonstrates the utility of the approach.
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- author
- Karttunen, Aki ; Gustafson, Carl LU ; Molisch, Andreas F. LU ; Jarvelainen, Jan and Haneda, Katsuyuki
- organization
- publishing date
- 2017-02-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- censored data, channel characterization and modeling, Cross-polarization ratio (XPR), maximum likelihood estimation, millimeter-wave propagation measurements, radio propagation
- in
- IEEE Wireless Communications Letters
- volume
- 6
- issue
- 1
- article number
- 7762106
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85013413515
- wos:000395868700021
- ISSN
- 2162-2337
- DOI
- 10.1109/LWC.2016.2633355
- language
- English
- LU publication?
- yes
- id
- fdff31c3-5e59-4470-8d7c-f984a60493e6
- date added to LUP
- 2017-03-08 11:49:59
- date last changed
- 2025-01-07 09:16:36
@article{fdff31c3-5e59-4470-8d7c-f984a60493e6, abstract = {{<p>In wireless channel measurements, the relatively weak cross-polarized multipath components (MPCs) are typically severely affected by the measurement noise level. As shown in this letter, the typical cross-polarization ratio (XPR) model parameter estimation, which ignores the existence of censored samples, may lead to significant errors. We demonstrate how to achieve accurate parameter estimates with a maximum likelihood estimator that properly takes into account both the measured XPRs and the censored samples. Also, a new XPR model is presented in which the average XPR is modeled as a function of the MPC excess loss. The new model is shown to be insensitive to the channel measurement noise level. A practical example with measured data in an indoor environment at 60 GHz demonstrates the utility of the approach.</p>}}, author = {{Karttunen, Aki and Gustafson, Carl and Molisch, Andreas F. and Jarvelainen, Jan and Haneda, Katsuyuki}}, issn = {{2162-2337}}, keywords = {{censored data; channel characterization and modeling; Cross-polarization ratio (XPR); maximum likelihood estimation; millimeter-wave propagation measurements; radio propagation}}, language = {{eng}}, month = {{02}}, number = {{1}}, pages = {{82--85}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Wireless Communications Letters}}, title = {{Censored Multipath Component Cross-Polarization Ratio Modeling}}, url = {{http://dx.doi.org/10.1109/LWC.2016.2633355}}, doi = {{10.1109/LWC.2016.2633355}}, volume = {{6}}, year = {{2017}}, }