All right.
So this paper is by Antonio Castro, as you can see over there.
I am not Antonio Castro.
I am Richard Overdorf.
So this is my R&D colleague who wrote this paper, but we are going to talk a little bit
about phase noise and, specifically, some different types of ways to measure phase noise.
So let us get started.
So most of us really have a pretty good understanding, I think in this domain that we are in right
now- phase noise is important on multiple fronts.
There are a couple of examples here.
But really what we are talking about is things for cutting-edge radar and communication systems.
Phase noise obviously limits for things like Doppler effects, and of course, for digitally
modulated communications.
If you have phase noise, it affects the modulated symbols here by moving them in an arcing type
of fashion.
So, again, that degrades our bit error rate performance and gives us some issues there.
So, of course, phase noise is very important.
I am going to talk about it in a couple different formats.
So it is obviously instability in frequency, but we talk about it in kind of two domains.
One is kind of the short domain, which is most common that we talk about for phase noise.
And then the other thing I want to touch on a little bit is long-term stability which
does not manifest itself quite as much as in the phase noise domain.
But specifically we can measure in a couple different methods.
The frequency domain, which is very, very common, you see that single sideband measurements
very frequently, specifically with a logarithmic plot.
And then, of course, also in the time domain for measurements that are close-in and for
short-term stability, we can make those measurements in the time domain using a distributive process.
So you can see that we have different contributors based on where they are in the phase noise
plot.
For the top plot, of course, is the frequency domain; and the bottom plot is in the time
domain where different types of noise are contributing.
Based on those types of noise that are dominant in your system, you may change the way you
want to measure that device.
So as I said, this is a quick agenda.
I should note that the CD has a lot longer presentation with speaker notes that go into
more details on the subject.
This is really just an introduction.
But we talked already a little bit about what phase noise is.
I am going to talk about really three different types of measurement techniques, and then
add kind of another level on the two-channel cross correlation measurement.
And then we are going to talk a little bit about extending phase noise to higher frequencies.
And then we will quickly take a look at some time domain stability analysis with the Allan
variance and domain conversion.
And then, finally, we have some new capability with our atomic frequency references for long-term
stability that we will talk quickly about, and then we will finish up.
So, again, what is phase noise?
So if you look at the two different things I was kind of mentioning already, it is really
the basic concept of stability.
And I kind of already talked a little bit about the short-term versus long-term.
So the long-term stability is frequency variations that really occur over hours, or even days,
where the shorter-term is a subset of that- maybe a few seconds- but really even less
than that is where we really see that manifest itself and make measurements in.
So, again, I am going to talk a lot about the short-term at the beginning, and then
as we go forward, I will talk a little bit more about the long-term.
But, you know, people like NIST, say that it is the ratio of the power density at a
specific offset.
So you will hear us talk usually in the spectrum analysis domain, we like to throw the 10 kHz
offset out there a lot of times, but, of course, depending upon what you are looking at, if
you are doing something like Doppler and you are looking for something that is slowly moving,
you probably want to look for something that is really close-in to the carrier.
If you have Doppler with higher velocities then, of course, it is going to be further
away from the carrier.
So it really depends on what your application is.
But, of course, again, different things contribute based on where you are in the noise curve.
So, what is phase noise?
Again, I think this is pretty basic- most of us probably understand that.
But I do want to point out a couple things.
So, of course, it is changes in phase which are pretty obvious.
But what we also see a lot of times too is, we also have some, sometimes some amplitude
modulation that occurs as well.
And these things can also manifest themselves in that sideband and we really want to – a
lot of times we want to differentiate between phase noise or AM noise.
So I talked about the single sideband approach.
There are a couple different ways we can measure this.
You could take something like a standard spectrum analyzer plot, and take a marker a specific
offset away from the frequency, and we have this band interval density marker where you
can actually get a density from that marker.
But you have to account for things like "What is the noise bandwidth of your resolution
bandwidth?"
And so there are some adjustments that you have to do in the spectrum analyzer to make
sure you are calculating your phase noise correctly.
Again, if you see that the power is in dBc per Hz, of course, so you measure from the
carrier of the offset.
And then, of course, a lot of times, we like to use a log plot, and the typical spectrum
analysis mode does not have the log plot.
So I kind of touched on some of these already, but you tune the analyzer to a specific frequency.
You can measure the density, but it really does have some limitations.
I talked about the noise bandwidth in the RBW, so you have to kind of know what the
filter shape is and the shape factor.
Again, you have to know whether or not this is residual FM problems from your local oscillator
inside the signal analyzer.
The other local oscillator stability as far as their noise sideband.
So if you want to measure something that is closer in, you have a problem there.
If the signal analyzer's LO and/or its phase noise performance is worse than the device
under test, then you obviously cannot make a clean measurement with that.
You will be measuring the spectrum analyzer, not your device under test.
We also have things like, we use the peak detector, and also log amplifiers.
This can account for about 3 dB, 2.5 dB of noise.
You can read something like App Note 150.
It describes how this calculation is manifested, but it really has an effect on your end measurement.
And then finally, especially at further offsets, you want to make sure that you are not running
into the noise floor and that is contributing to the measurement as well.
The good news is most signal analyzers today have an internal application, a phase noise
application, that gives you capability to do a lot of different things.
And it actually takes into account a lot of these things that I just mentioned on this
previous slide.
So we have a lot of different things we can do here.
We can monitor the spectrum, of course, we can do spot measurements.
So if you want to see maybe the phase noise is changing versus time; maybe something is
happening in the system where it is changing versus times, that is where you can do something
like a spot measurement and you can see how the phase noise goes across time.
And then, of course, it plots it in log form.
As I mentioned, that is commonly not available inside of the, the swept spectrum analysis
mode.
Another nice thing that applications do, specifically this application from Agilent, we have a patented
AM noise rejection technique where we can actually reduce the AM on the signal so that
you can measure your phase noise without the AM component on it.
This works for offsets less than one MHz.
And, basically, it is a vector process that we use to be able to be able to make that
measurement.
Another thing you might want to be concerned about is if you use an external reference,
that reference can lock to the oscillator, of course.
And if it has something better in the close-in – and I really, say, about 100 Hz– I can
see improvements within 100 Hz.
This slide says 30 Hz.
So if you have a really clean oscillator that you want to use for a 10 MHz reference, that
will improve your phase noise close-in, and improve your measurement results for the close-in
offsets.
This is the signal path to be able to do that – which I am not going to go into detail
with.
Again, looking at the phase detector technique, which is another technique not, not commonly
in a signal analyzer, this basically takes a detector and takes the quadrature of the
two; and based on that, you can see the phase fluctuations in a voltage output out of the
phase detector.
So you can see those fluctuations versus time; and this is a common technique that is used
for, I would say, more advanced VCO and PLLs that are – that have really good phase noise
where a direct method, as seen in the spectrum analyzer domain cannot do a very good job
on.
So we really talk about two sub-segments of this, using a double-balance mixer here as
the detector.
You have a device under test, and then you use a reference oscillator that is locked
with a PLL.
You have a low-pass filter to knock off, especially if you are doing really low frequency measurements.
You really need to lock off the mixing components of the mixer, so you do that with the low-pass
filter.
Then you have a low-noise amplifier, and then you have a baseband analyzer, which in this
case, could be a spectrum analyzer.
So the nice thing is you have one source that is the DUT and then the second source- if
you have a very good reference source- you can make a really strong phase noise measurement.
On the frequency discriminator technique, basically, the frequency discriminator is
a method that is really an adaptation of the phase detector technique, but the reference
source is now eliminated.
Now you only need the device under test, so you do not need a really good oscillator.
It is also, again, very similar to the PLL technique in that it uses a detector – in
this case, a mixer.
But now we use a time delay.
And as long as the time delay is accurately delayed to the other path, the delay line
then converts that fluctuation into a phase fluctuation.
And then once you have that delay line or phase shifter, you adjust it and make sure
it is in quadrature of the mixer similar to the last example.
And then that phase fluctuation just as I said, is converted into a baseband analyzer,
which could be a signal analyzer.
You can read those voltage differences, and now measure your phase noise.
So the frequency discriminator technique is, again, a method that the analog delay line
degrades in sensitivity at really close-in frequencies.
It is really good if the DUT is a noisy source that has really a high level or a low rate
phase noise.
Also longer lines can really improve this sensitivity; but adding insertion loss and
delay can really degrade the signal-to-noise ratio and the measurement sensitivity.
This is really good for free-running sources.
So if you have something that is unlocked, that moves a lot, such as a cavity oscillator,
this is a good, a good technique for that.
So we actually use a heterodyne digital discriminator.
This is what we use in our N5052 which is a signal source analyzer.
Basically, this is where the DUT is down-converted by a mixer, so we do it up to 26 GHz.
The IF signal was amplified and, and then we used an A to D converter in the digital
signal processing, after the digital signal processing, to split the signal.
And then we do the comparison in the digital domain.
So this is a picture of it.
Basically, it can measure unstable sources and oscillators.
So if you want to measure a characteristic of how that oscillator – or maybe it is
a voltage controlled oscillator that moves versus time.
You can make a lot of those measurements with this product.
The dynamic range is really limited by the low-noise amplifier and, and the ADC performance
of the signal source analyzer.
It is pretty fast and accurate, and it is really improved by course correlation – and
we will talk a little bit about what that means.
That is not fast in many cases, depending upon how much correlation you want to do.
So the cross-correlation technique is a way to really improve your phase noise by doing
a lot of number of correlation.
It is essentially averaging.
But if you look at this equation here, the lower the noise here that contributes to your,
your measured noise.
So you want to make sure that this is minimized.
But you take a signal – or system under test, or a device under test- you split that
signal into two different channels that are coherent, and then you do a whole bunch of
correlations in DSP, do a whole bunch of correlations.
And based on that you can get reduction in noise level.
Conceivably, this could go forever, right?
I mean you could get down to KTB if you really wanted to by using the correlation technique.
So we do cross-correlation, as I mentioned, to reduce the measurement noise.
Again, it is a two-channel phase noise for a PLL system, or a two –channel heterodyne
digital discriminator.
It is really, again, well-suited for running oscillators.
Measurement speed, of course as I mentioned, suffers when you want to do a lot of correlations
to get the lower noise level.
But it is something that that is available for you.
So I am not going to go through this block diagram.
We have some notes on exactly how this works inside of the DVD.
Another thing we have is a phase noise measurement system; and this phase noise measurement system
actually has a lot of capability in it.
It uses both the discriminator technique with a PLL, or it can use a cross-correlation technique.
So the system is somewhat complex, but it allows for a lot of flexibility between the
overall system and performance.
This shows the phase noise capability of that 5500 system.
It also does pulsed phase noise, which is something else I should mention.
And then for extending up to 110 GHz, basically these next few slides, which I am going to
go through very quickly explain how we downconvert.
Again, it is very important for things like radar.
There are a lot of applications that this is important for.
Again, the more you have for a harmonic mixer, the more you mix up, the more you have noise
at the end goal.
So you have to make sure that that cascades through the system.
This basically shows the uncertainty of a measurement versus the offset from the carrier,
and how much drift you have in the system.
So the more drift you have, obviously, the more uncertainty you are going to have in
your measurement.
And, basically, you are not able to make close-in measurements.
This shows the downconversion process of the, 5502 that I mentioned has a downconverter
in front of it, and then we do the signal processing like I mentioned earlier.
We also have options for 110 GHz, where we use external mixers and then here is a 77
GHz measurement example that you can refer to.
Again, here is the time domain.
Basically, two different types.
The Allan variance.
We also have the modified Allan; most common, but it has really not very good confidence.
And then we have the white noise and flicker.
So we have a new application that does this measurement and I am going to show you here
quickly.
Basically, we have a new signal stability analyzer using a frequency counter that does
that.
You can see that here.
That is available on MATLAB central, basically using MATLAB and one of these frequency counters.
So we also have this in the digital domain as well.
You can do the same thing and correlate the time domain to the frequency domain measurement.
We go through more detail on this in the slide set.
Here is the slide; here is the data, and more of the conversion.
Last but not least, interfacing with a new frequency reference, which I mentioned at
the beginning- basically what we do is we train the inside of the bot.
This is the FPGA inside a signal analyzer.
This is for long-term stability.
So basically what we do is we detect a one-pulse-per-second based on a CZM standard, so it is very concrete
as far as its repeatability.
We compare that and then we adjust our time base over time.
So here is the summary, very quickly.
We are done.
I am sorry that I went over.
There are more slides here than I was expecting.
So thank you very much.