Keysight Labs | How to Make Complementary Cumulative Distribution Function (CCDF) Measurements @KeysightLabs | Uploaded May 2018 | Updated October 2024, 2 hours ago.
Use statistics to characterize digitally modulated signals
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Transcript:
When designing components for wireless digital communication systems, it is important to be able to accurately measure their signals which have higher peak-to-average power ratios than their analog counterparts. Since many digitally modulated signals appear noise-like in both the time and frequency domains, characterizing modulated signals can be difficult. Today we’ll discuss how you can quickly characterize the operating signal power of components such as amplifiers, mixers and filters using statistics.

Hi, I’m Ally, and welcome to Keysight’s Rapid Measurement Series for Signal analyzers called Ready, Set Measure.

Modern communication systems use digital modulation instead of analog modulation, and as such have a higher peak-to-average signal power ratio. This means that you have to design your components- amplifiers, filters, mixers- carefully to avoid signal distortion or clipping.

These signals also appear noise-like in both the time and frequency domains. Because of this noise-like appearance, we can use statistics to characterize modulated signals.

A complementary cumulative distribution function or CCDF is helpful for determining design parameters for digital communication systems.

A CCDF curve shows us the probability a signal is at a specific power level. Because signals below the average power level aren’t going to clip or distort, we only care about signals above the average power level. The horizontal axis is the signal’s power relative to the average power, measured in dB. The percentage of time the signal spends at or above each line tells us the probability for that particular power level. The curve is a plot of relative power levels versus probability.

So, we can use a CCDF curve to quickly characterize the peak-to-average power of digital system components.

For example, if you’re a DSP signal designer, you can use a CCDF curve to accurately determine the power characteristics of your signals. You can then relay that information to your RF designers, helping you to avoid costly errors during system integration.
Here, on this modulated signal, is the average power level of the signal, the maximum power, and another point say… +6 dB above average.

Looking at this WCDMA signal, now on screen, it’s very difficult to quantify what’s going on because of inherent randomness and inconsistencies. To be able to extract any useful information from this noise-like signal we need details of the signal’s power levels.

On the horizontal access we have the power relative to the average power. And on the vertical axis percent of time that the signal spends at that power level. The CCDF curve along the vertical axis is displayed logarithmically so we can get better resolution of high power events.

So, for our WCDMA signal here, we can quickly see that the average power level is –[insert number here]dBm. We can also see on the CCDF curve the maximum power level at the ‘x’ intercept-, and the +6 dB point.

This measurement table can also be used to quickly identify key parameters of the measurement such as the average power, some quick reference points, and peak power measurements of the signal.

With the information from the CCDF curve, it can help you:
• Visualize the effects of modulation formats
or the effects of combining multiple system components
• Evaluate spread spectrum systems
• And design or test discreet RF components

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