HBT's RF noise parameter determination by means of an efficient method based on noise analysis of linear amplifier networks

1997 ◽  
Vol 45 (5) ◽  
pp. 690-694 ◽  
Author(s):  
P. Rouquette ◽  
D. Gasquet ◽  
T. Holden ◽  
J. Moult
Keyword(s):  
Efficient Method ◽  
Noise Analysis ◽  
Parameter Determination ◽  
Noise Parameter ◽  
Linear Amplifier ◽  
Rf Noise

A new method for phemt noise-parameter determination based on 50-Ω noise measurement system

2003 ◽  
Vol 51 (10) ◽  
pp. 2079-2089 ◽  
Author(s):  
Jianjun Gao ◽  
Choi Look Law ◽  
Hong Wang ◽  
S. Aditya ◽  
G. Boeck
Keyword(s):  
Measurement System ◽  
Noise Measurement ◽  
New Method ◽  
Parameter Determination ◽  
Noise Parameter

Novel Noise Parameter Determination for On-Wafer Microwave Noise Measurements

2008 ◽  
Vol 57 (11) ◽  
pp. 2462-2471 ◽  
Author(s):  
Chih-Hung Chen ◽  
Ying-Lien Wang ◽  
M.H. Bakr ◽  
Zheng Zeng
Keyword(s):  
Parameter Determination ◽  
Noise Parameter ◽  
Noise Measurements ◽  
Microwave Noise

scholarly journals Phase diffusion and the small-noise approximation in linear amplifiers: Limitations and beyond

Quantum ◽  
2019 ◽  
Vol 3 ◽  
pp. 200 ◽  
Author(s):  
Andy Chia ◽  
Michal Hajdušek ◽  
Rosario Fazio ◽  
Leong-Chuan Kwek ◽  
Vlatko Vedral
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Analysis ◽  
Photon Number ◽  
Optical Field ◽  
Signal To Noise ◽  
Small Noise ◽  
Phase Diffusion ◽  
Small Noise Approximation ◽  
Linear Amplifier ◽  
Phase Uncertainty

The phase of an optical field inside a linear amplifier is widely known to diffuse with a diffusion coefficient that is inversely proportional to the photon number. The same process occurs in lasers which limits its intrinsic linewidth and makes the phase uncertainty difficult to calculate. The most commonly used simplification is to assume a narrow photon-number distribution for the optical field (which we call the small-noise approximation). For coherent light, this condition is determined by the average photon number. The small-noise approximation relies on (i) the input to have a good signal-to-noise ratio, and (ii) that such a signal-to-noise ratio can be maintained throughout the amplification process. Here we ask: For a coherent input, how many photons must be present in the input to a quantum linear amplifier for the phase noise at the output to be amenable to a small-noise analysis? We address these questions by showing how the phase uncertainty can be obtained without recourse to the small-noise approximation. It is shown that for an ideal linear amplifier (i.e. an amplifier most favourable to the small-noise approximation), the small-noise approximation breaks down with only a few photons on average. Interestingly, when the input strength is increased to tens of photons, the small-noise approximation can be seen to perform much better and the process of phase diffusion permits a small-noise analysis. This demarcates the limit of the small-noise assumption in linear amplifiers as such an assumption is less true for a nonideal amplifier.

A new programmable load for noise parameter determination

1991 ◽  
Vol 39 (2) ◽  
pp. 216-223 ◽  
Author(s):  
B.M. Albinsson ◽  
H. Guo ◽  
M. Schoon ◽  
H.-O. Vickes
Keyword(s):  
Parameter Determination ◽  
Noise Parameter

scholarly journals Adaptive Noise Parameter Determination Based on a Particle Filter Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Hyun-Tae Cho ◽  
Sungho Mun
Keyword(s):  
Particle Filter ◽  
Prediction Models ◽  
Traffic Noise ◽  
Noise Model ◽  
Parameter Determination ◽  
Model Parameters ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Noise Parameter ◽  
Adaptive Noise

Due to the growing number of vehicles using the national road networks that link major urban centers, traffic noise is becoming a major issue in relation to the transportation system. Thus, it is important to determine noise model parameters to predict road traffic noise levels as part of an environmental assessment, according to traffic volume and pavement surface type. To determine the parameters of a noise prediction model, statistical pass-by and close proximity tests are required. This paper provides a parameter determination procedure for noise prediction models through an adaptive particle filter (PF) algorithm, based on using a weigh-in-motion system, which obtains vehicle velocities and types, as well as step-up microphones, which measure the combined noises emitted by various vehicle types. Finally, an evaluation of the adaptive noise parameter determination algorithm was carried out to assess the agreement between predictions and measurements.

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