Statistical Modeling and Estimation of Censored Pathloss Data

Carl Gustafson, Taimoor Abbas, David Bolin, Fredrik Tufvesson

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Pathloss is typically modeled using a log-distance power law with a large-scale fading term that is log-normal. However, the received signal is affected by the dynamic range and noise floor of the measurement system used to sound the channel, which can cause measurement samples to be truncated or censored. If the information about the censored samples is not included in the estimation method, as in ordinary least squares estimation, it can result in biased estimation of both the pathloss exponent and the large scale fading. This can be solved by applying a Tobit maximum-likelihood estimator, which provides consistent estimates for the pathloss parameters. This letter provides information about the Tobit maximum-likelihood estimator and its asymptotic variance under certain conditions.
Original languageEnglish (US)
Pages (from-to)569-572
Number of pages4
JournalIEEE Wireless Communications Letters
Volume4
Issue number5
DOIs
StatePublished - Oct 1 2015
Externally publishedYes

Fingerprint Dive into the research topics of 'Statistical Modeling and Estimation of Censored Pathloss Data'. Together they form a unique fingerprint.

Cite this