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Showing posts with label NPR. Show all posts
Showing posts with label NPR. Show all posts

Saturday, May 1, 2010

Simulation: NPR Test Simulation

So during my time at work today (yes I worked on Saturday), I made an attempt to try and simulate an NPR test for an amplifier in Agilent's EESof/ADS.  Now there is a way to accomplish this using the DSP portion along with the Analog Envelope technique but I haven't found a way to do it directly with the RF/Analog system.  This is quite the disappointment...

Figure 1: NPR Test Stimulus (courtesy Agilent)


So I have been researching into solutions.  Only one has presented itself up to this point: Spectral Balance Simulations.  Unfortunately, ADS doesn't support this directly (at least not to my knowledge).  This is different than the typical Harmonic Balance Simulation most people are used to.  In fact the main difference is that the Spectral Balance simulation stays in frequency domain.  The Harmonic Balance simulation instead does a transient analysis then an FFT to complete the simulation (Of course it's more complex than this).


So how do I get around it?  I really have no idea right now.  The fact is, this is a known issue.  Why hasn't someone developed this into the more popular SPICE simulations?  There are random Matlab tools that can do the trick but not directly in SPICE packages.


Anyways, that's my rant for the day.  I'll go into more detail, hopefully, at a later date.


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Justin Coulston
justin.coulston@gmail.com

Friday, April 30, 2010

RF: Noise to Power Ratio (NPR) Test

Introduction
NPR, or Noise to Power Ratio, is a figure of merit describing an amplifier's linearity.  When I say linearity, I am referring to the amplifier's ability to keep it's gain flat versus output power.  Of course it's deeper than this but this is a good way to understand it.

Now many people know of different types of linearity figures of merit, OIP3 for instance.  Unfortunately, OIP3 is only good for certain instances, single carriers or very few carriers.  But what about OFDM or QAM modulation techniques that have closely spaced signals?

This is where an NPR test is useful.  It takes into account a lot of different carriers during the test and tests how linear the amplifier is.  I'm not going into great detail of the mathematics but I will indicate the basics of how it works

The Basics
The most common way to perform an NPR test is to use 5 main devices in the lab:

  1. AWGN source (Noise Source): This is used to simulate the many frequency tones.
  2. Bandpass Filter: A filter used to bandlimit the noise into a usable bandwidth. Generally, this is the waveform's bandwidth.
  3. Notch Filter: A filter, or method, of taking out a small section of the noise, creating a "notch" in the middle of the waveform.
  4. AUT: This is the amplifier that is being tested, generally a power amplifier.
  5. Spectrum Analyzer: Used to take the measurements. (Could be a Network Analyzer)
So in order to perform the test, you need to hook up the source to the bandpass filter then the notch filter.  Followed by this is the AUT and finally the Spectrum Analyzer.  Generally the notch will be the bandwidth of the signal separation.

Ultimately, the point of this setup is to see how much the notch is filled after the sample noise waveform passes through the amplifier.  This idea is based on intermod theory.  When many tones are very close together they will "intermodulate" with each other and form harmonics at other close frequencies.  This is how the notch "fills up," by the intermodulation harmonics from other tones.  Based on this we can find how linear the amplifier is relative to different noise power levels.

Conclusion
So there is a lot here that wasn't said but I hope you have a basic idea on what the purpose and why NPR works.  An NPR test is basically using intermod theory on a large number of tones to see how much they modulate with each other.  After that, it's simple math using dBc/Hz reference powers of Spectral Density.  If you desire more information or examples, I'll be more than happy to help.  Just email me or comment. Later!

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Justin Coulston
justin.coulston@gmail.com