Pathloss Estimation Techniques for Incomplete Channel Measurement Data
Abbas, Taimoor LU ; Gustafson, Carl LU and Tufvesson, Fredrik LU
(2014)
COST IC1004 10th Management Committee and Scientific Meeting
- Abstract
- The pathloss exponent and the variance of the large-scale fading are two parameters that are of great importance when modeling or characterizing wireless propagation channels. The pathloss is typically modeled using a single-slope log-distance power law model, whereas the large-scale fading is modeled using a log-normal distribution. In practice, the received signal is affected by noise and it might also be corrupted by interference from other active transmitters that are transmitting in the same frequency band. Estimating the pathloss exponent and large scale fading without considering the effects of the noise and interference, can lead to erroneous results. In this paper, we show that the path loss and large scale fading estimates can be... (More)
- The pathloss exponent and the variance of the large-scale fading are two parameters that are of great importance when modeling or characterizing wireless propagation channels. The pathloss is typically modeled using a single-slope log-distance power law model, whereas the large-scale fading is modeled using a log-normal distribution. In practice, the received signal is affected by noise and it might also be corrupted by interference from other active transmitters that are transmitting in the same frequency band. Estimating the pathloss exponent and large scale fading without considering the effects of the noise and interference, can lead to erroneous results. In this paper, we show that the path loss and large scale fading estimates can be improved if the effects of samples located below the noise floor are taken into account in the estimation step. When the number of such samples is known, then the pathloss exponent and standard deviation of the large scale fading can be iteratively computed using maximum likelihood estimation from incomplete data via the expectation maximization (EM) algorithm. Alternatively, if the number of samples below the noise floor is unknown, we show that the pathloss and large scale fading parameters can be estimated based on a likelihood expression for a truncated normal distribution. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4442929
- author
- Abbas, Taimoor
LU
; Gustafson, Carl
LU
and Tufvesson, Fredrik
LU
- organization
- publishing date
- 2014
- type
- Contribution to conference
- publication status
- unpublished
- subject
- pages
- 5 pages
- conference name
- COST IC1004 10th Management Committee and Scientific Meeting
- conference location
- Aalborg, Denmark
- conference dates
- 2014-05-26 - 2014-05-28
- language
- English
- LU publication?
- yes
- id
- 4660fca6-e8ef-4b39-ad44-f5c98c0e819e (old id 4442929)
- date added to LUP
- 2016-04-04 13:57:27
- date last changed
- 2019-04-30 10:00:23
@misc{4660fca6-e8ef-4b39-ad44-f5c98c0e819e,
abstract = {{The pathloss exponent and the variance of the large-scale fading are two parameters that are of great importance when modeling or characterizing wireless propagation channels. The pathloss is typically modeled using a single-slope log-distance power law model, whereas the large-scale fading is modeled using a log-normal distribution. In practice, the received signal is affected by noise and it might also be corrupted by interference from other active transmitters that are transmitting in the same frequency band. Estimating the pathloss exponent and large scale fading without considering the effects of the noise and interference, can lead to erroneous results. In this paper, we show that the path loss and large scale fading estimates can be improved if the effects of samples located below the noise floor are taken into account in the estimation step. When the number of such samples is known, then the pathloss exponent and standard deviation of the large scale fading can be iteratively computed using maximum likelihood estimation from incomplete data via the expectation maximization (EM) algorithm. Alternatively, if the number of samples below the noise floor is unknown, we show that the pathloss and large scale fading parameters can be estimated based on a likelihood expression for a truncated normal distribution.}},
author = {{Abbas, Taimoor and Gustafson, Carl and Tufvesson, Fredrik}},
language = {{eng}},
title = {{Pathloss Estimation Techniques for Incomplete Channel Measurement Data}},
url = {{https://lup.lub.lu.se/search/files/6245858/4461934.pdf}},
year = {{2014}},
}