TY - JOUR
T1 - Finding the probability distribution functions of S-parameters and their monte carlo simulation
AU - Agili, Sedig S.
AU - Morales, Aldo W.
AU - Li, Ji
AU - Resso, Michael
N1 - Funding Information:
Manuscript received October 10, 2011; revised April 13, 2012; accepted April 16, 2012. Date of publication July 20, 2012; date of current version October 10, 2012. This paper was supported in part by the Center for Signal Integrity, by Ben Franklin Technology Partners, and by Pennsylvania State University at Harrisburg and Agilent. The Associate Editor coordinating the review process for this paper was Dr. Rik Pintelon.
PY - 2012
Y1 - 2012
N2 - This paper presents the probability distribution function (PDF) of the ratio of two random waves. This result is used to obtain the PDF of S-parameters random errors in magnitude (in decibels) and phase, which are the quantities that most engineers work with. These results are further used on the development of a Monte Carlo simulation method in order to predict the variability of frequency-domain measurements. Experiments are performed to identify and characterize frequency-domain random errors, such as instrument noise, connector repeatability, and calibration variations, in measurement systems. By comparing with real measurement data, it is shown that random-error effects can be accurately estimated by the PDF's obtained and the Monte Carlo technique.
AB - This paper presents the probability distribution function (PDF) of the ratio of two random waves. This result is used to obtain the PDF of S-parameters random errors in magnitude (in decibels) and phase, which are the quantities that most engineers work with. These results are further used on the development of a Monte Carlo simulation method in order to predict the variability of frequency-domain measurements. Experiments are performed to identify and characterize frequency-domain random errors, such as instrument noise, connector repeatability, and calibration variations, in measurement systems. By comparing with real measurement data, it is shown that random-error effects can be accurately estimated by the PDF's obtained and the Monte Carlo technique.
UR - http://www.scopus.com/inward/record.url?scp=84867582365&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867582365&partnerID=8YFLogxK
U2 - 10.1109/TIM.2012.2202165
DO - 10.1109/TIM.2012.2202165
M3 - Article
AN - SCOPUS:84867582365
SN - 0018-9456
VL - 61
SP - 2993
EP - 3002
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
IS - 11
M1 - 6246705
ER -