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Noise figure is the increase in noise power of a device from the input to the output that is greater that the signal gain. In effect, it is the amount of decrease of the signal-to-noise ratio. Like gain, noise figure can be expressed either as a ratio or in decibels.

Click here to view an example of a cascaded system.

Cascade noise figure calculation is carried out by dealing with gain and noise figure as a ratio rather than decibels, and then converting back to decibels at the end. As the following equation shows, cascaded noise figure is effected most profoundly by the noise figure of components closest to the input of the system as long as some positive gain exists in the cascade. If only loss exists in the cascade, then the cascaded noise figure equals the magnitude of the total loss. The following equation is used to calculate cascaded noise figure as a ratio based on ratio values for gain and noise figure (do not use decibel values).

Here is a  great article on Noise Figure Uncertainty in the October 1999 "Microwave & RF" magazine.

Note that when calculating cascaded noise figure, quantities must be expressed as ratios, NOT as decibels. By convention, lower case variables represent ratios, while upper case variables represent decibels (dB).                      Conversions:   nf = 10NF/10  ↔   NF (dB) = 10 * log10 (nf)

In Compact Form: nfN = , where N = number of stages

Cascaded Noise Figure In Expanded Form:

Click here to see Agilent's App Note 1303 on using a spectrum analyzer to measure noise figure.

Conversion Between Noise Figure and Noise Temperature Noise Temperature (K) = 290 * Noise Figure (dB) = 10 * log10 These equations assume a room temperature reference (290 K). If applicable, use whatever your reference temperature is in place of the 290.

Cascaded Noise Temperature Calculation

 
The following table lists a few conversions. See the Noise Power Calculator online calculator for calculating other values.

NF(dB) TN (°K)   NF(dB) TN (°K) 0.1 7 2.1 180 0.2 14 2.2 191 0.3 21 2.3 202 0.4 28 2.4 214 0.5 35 2.5 226 0.6 43 2.6 238 0.7 51 2.7 250 0.8 59 2.8 263 0.9 67 2.9 275 1.0 75 3.0 289 1.1 84 3.1 302 1.2 92 3.2 316 1.3 101 3.3 330 1.4 110 3.4 344 1.5 120 3.5 359 1.6 129 3.6 374 1.7 139 3.7 390 1.8 149 3.8 406 1.9 159 3.9 422 2.0 170 4.0 438

Check this out - someone referenced this page on Wikipedia!

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