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Adding Measurement Sensitivity by Subtracting Noise Power

Blog Post created by benz on Sep 15, 2016

Originally posted May 30, 2014

 

If noise power is part of your problem, less is more

An earlier post on measuring signals near noise described how noise power in a signal measurement adds to the measured signal power and thus creates an error component. The error can be significant, even for signals well above noise, due to the inherent accuracy of modern signal analyzers.

Fortunately, this additive error process can be reversed in many measurements, providing an improvement in both measurement accuracy and effective sensitivity. This performance improvement is especially important when small signals must be measured along with larger ones. That is, when sensitivity can’t be improved by reducing attenuation or adding preamplification.

The key to these improvements is knowledge of the amount of added noise power, and in most cases this corresponds to the noise floor of the signal analyzer. To correct the typical power spectrum measurement, the average noise floor power in the analyzer’s RBW filter is subtracted from each point of a power spectrum measurement. An example of that process is shown in the figure below.

Two spectrum measurements of a low power seven-tone signal. Amplitude decreases 3 dB for each tone and the scale is 3 dB/div. The blue trace shows the benefit of subtracting most of the signal analyzer noise using Agilent’s noise floor extension (NFE) technique.

Two spectrum measurements of a low power seven-tone signal. Amplitude decreases 3 dB for each tone and the scale is 3 dB/div. The blue trace shows the benefit of subtracting most of the signal analyzer noise using Agilent’s noise floor extension (NFE) technique.

Subtracting the analyzer’s noise power contribution is simple trace math on a power (not dB) scale, but precisely determining that power is not so simple.

The direct approach is to disconnect the signal under test, perform a highly averaged noise floor measurement, reconnect the signal and measure it with the noise subtracted. This approach is accurate and effective but can be very slow. In addition, the noise floor measurement must be re-done if the measurement configuration is changed in any way or if measurement conditions change, especially temperature.

A more sophisticated technique involves accurately modeling the signal analyzer noise floor under all measurement configurations and operating conditions then using that information to correct signal measurements on the fly. This technique is not quite as effective as individual noise floor measurements but it is much faster and more convenient. In addition, it requires no user interaction, imposes no speed penalty, and in the Agilent PXA signal analyzer it can provide up to 12 dB of improved sensitivity, as shown above.

This noise floor extension (NFE) technique has been available as a standard feature in the PXA signal analyzer for several years, and is now available as an option for the MXA signal analyzer. In the MXA this option is a license key upgrade, available for all existing MXA models and implemented through an automated self-calibration that takes 30 minutes or less.

Over the full frequency range of the MXA, the NFE option produces improvements such as those shown here.

The noise floor of an MXA signal analyzer is shown from 10 MHz to 26 GHz, both with and without the benefit of NFE noise power subtraction. Effective sensitivity is improved over a wide frequency range by approximately 9 dB with no reduction in measurement speed and no need for separate noise floor characterization measurements.

The noise floor of an MXA signal analyzer is shown from 10 MHz to 26 GHz, both with and without the benefit of NFE noise power subtraction. Effective sensitivity is improved over a wide frequency range by approximately 9 dB with no reduction in measurement speed and no need for separate noise floor characterization measurements.

I suppose this is another example of adding information to improve measurement performance. In this case the information is the analyzer noise power, and the resulting improvement is in both sensitivity and accuracy for small signals.

The discussion so far has focused mainly on sensitivity and its consequences. It’s worth noting that this sensitivity enhancement can be traded away for other benefits such as measurement speed. For example, NFE may allow a wider RBW to be used for a given measurement, resulting in significantly faster sweep rates.

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