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All Places > Keysight Blogs > Better Measurements: The RF Test Blog > Blog > 2017 > February

  Wrestling information from a hostile universe

I can’t make a serious case that the universe is actively hostile, but when you’re making spurious and related measurements, it can seem that way. In terms of information theory, it makes sense: The combination of limited signal-to-noise ratio and the wide spans required mean that you’re gathering lots of information where the effective data rate is low. Spurious and spectrum emission mask (SEM) measurements will be slower and more difficult than you’d like.

In a situation such as this, the opportunity to increase overall productivity is so big that it pays to improve these measurements however we can. In this post I’ll summarize some recent developments that may help, and point to resources that include timeless best practices.

First, let’s recap the importance of spurious and spectrum emission and the reasons why we persist in the face of adversity. Practical spectrum sharing requires tight control of out-of-band signals, so we measure to find problems and comply with standards. Measurements must be made over a wide span, often 10 times our output frequency. However, resolution bandwidths (RBW) are typically narrow to get the required sensitivity. Because sweep time increases inversely with the square of the RBW, sensitivity can come at a painful cost in measurement time.

The result is that these age-old measurements, basic in comparison to things such as digital demodulation, can consume a big chunk of the total time required for RF testing.

Now on to the good news: advances in signal processing and ADCs can make a big difference in measurement time with no performance penalty. Signal analyzers with digital RBW filters can be set to sweep much faster than analog ones, and the analyzers can precisely compensate for the dynamic effects of the faster sweep. Many years ago, when first used at low frequencies, this oversweep processing provided about a 4x improvement. In the more recent history of RF measurements, the enhanced version is called fast sweep, and the speed increase can be 50x.

Two spectrum measurements with equivalent resolution bandwidths and frequency spans are compared.  The fast sweep capability improves sweep speed by a factor of nearly 50 times.

In equivalent measurements of a 26.5 GHz span, the fast sweep feature in an X-Series signal analyzer reduces sweep time from 35.5 seconds to 717 ms, an improvement of nearly 50 times.

I’m happy to report that DSP and filter technologies continue to march forward, and the improvement from oversweep to fast sweep has been extended for some of the most challenging measurements and narrowest RBW settings. For bandwidths of 4.7 kHz and narrower, a newly enhanced fast sweep for most Keysight X-Series signal analyzers provides a further 8x improvement over the original. This speed increase will help with some of the measurements that hurt productivity the most.

Of course, the enduring challenges of spurious measurements can be met by a range of solutions, not all of them new. Keysight’s proven PowerSuite measurement application includes flexible spurious emission testing and has been a standard feature of all X-Series signal analyzers for many years.

A measurement application in a signal analyzer has a number of benefits for spurious measurements, including pass/fail testing and limit lines, automatic identification of spurs, and generation of a results table.

The PowerSuite measurement application includes automatic spurious emission measurements, such as spectrum emission mask, that include tabular results. Multiple frequency ranges can be configured, each with independent resolution and video bandwidths, detectors, and test limits.

PowerSuite allows you to improve spurious measurements by adding information to the tests, measuring only the required frequencies. Another way to add information and save time is to use the customized spurious and spectrum emissions tests included in standard-specific measurement applications.

A new application note, Accelerating Spurious Emission Measurements using Fast-Sweep Techniques, includes more detailed explanations, techniques, and resources. You’ll find it in the growing collection at our signal analysis fundamentals page.

  Jet engines, furry hoods, and a nod to four years of the Better Measurements blog

This blog gives me occasional freedom to explore technology and phenomena that have only a peripheral relationship to RF design and measurement. Sometimes it feels like a permission slip to cut class and wander off to interesting places with remarkable analogs to our world. I say this by way of warning that it may take me a while to get around to something central to RF engineering this time.

This little side trip begins with high-bypass turbofans and the artistic-looking scallops or chevrons on the outer nacelles and sometimes the turbine cores.

Turbofan jet engine with chevrons or scallops on trailing edge of engine nacelle and engine core, to reduce turbulence and noise.

The NASA-developed chevrons or scallops at the trailing edge of this turbofan engine reduce engine noise by causing a more gradual blending of air streams of different velocities. This reduces shear and the resulting noise-creating turbulence. They look cool, too. (Image from Wikimedia Commons)

In my mental model, shear is the key here. Earlier turbojets had a single outlet with very high velocity, creating extreme shear speeds as the exhaust drove into the ambient air. The large speed differences created lots of turbulence and corresponding noise.

Turbofans reduce noise dramatically by accelerating another cylinder of air surrounding the hot, high-speed turbine core. This cylinder is faster than ambient, but slower than the core output, creating an intermediate-speed air stream and two mixing zones. The shear speeds are now much lower, reducing turbulence and noise.

The chevrons further smooth the blending of air streams, so turbulence and noise are both improved. It’s a complicated technique to engineer, but effective, passive, and simple to implement.

Shear is useful in understanding many other technologies, modern and ancient. When I saw those nacelles I thought of the Inuit and the hoods of their parkas with big furry rims or “ruffs.” In a windy and bitter environment they reduce shear at the edges of the hood, creating a zone of calmer air around the face. A wind tunnel study confirmed Inuit knowledge that the best fur incorporates hairs of varying length and stiffness, anticipating—in microcosm—the engine nacelle chevrons.

The calm air zone reduces wind chill, and it also reduces noise. Years ago I had a parka hood with a simple non-furry nylon rim that would howl at certain air speeds and angles.

Another, more modern example is the large, furry microphone windscreen called (I am not making this up) a “dead cat.” At the cost of some size, weight, and delicacy (imagine the effect of rain) this is perhaps the most effective way to reduce wind noise.

The opposite approach to shear and noise is equally instructive. “Air knife” techniques have been used for years to remove fluids from surfaces, and you can now find them in hand dryers in public restrooms. They inevitably make a heck of a racket because the concentrated jet of air and resulting shear are also what makes them effective in knocking water from your hands. Personally, I don’t like the tradeoffs in this case.

In RF applications, we generally avoid the voltage and current equivalents of shear and undesirable signal power. When we can’t adequately reduce the power, we shape it to make it less undesirable, or we push it around to a place where it will cause less trouble. For example, PLLs in some frequency synthesizers can be set to optimize phase noise at narrow versus wide offsets.

Switch-mode power supplies are another example of undesirable power, typically because of the high dv/dt and di/dt of their pulsed operation. It isn’t usually the total power that causes them to fail EMC tests, but the power concentrated at specific frequencies. From a regulatory point of view, an effective solution can be to modulate or dither the switching frequency to spread the power out.

One final example is the tactic of pushing some noise out of band. Details are described in an article on delta-sigma modulation for data converters. Oversampling and noise shaping shift much of the noise to frequencies where it can be removed with filtering.

I’m sure that’s enough wandering for now, but before I finish this post I wanted to note that we’ve passed the four-year anniversary of the first post here. I’d like to thank all of you who tolerate my ramblings, and encourage you to use the comments to add information, suggest topics, or ask questions. Thanks for reading!

  Taking advantage of knowledge your signal analyzer doesn’t have

To respect the value of your time and the limits of your patience, I try to keep these posts relatively short and tightly focused. Inevitably, some topics demand more space, and this follow up to December’s post Exchanging Information You’ve Got for Time You Need is one of those. Back then, I promised additional suggestions for adding information to the measurement process to optimize the balance of performance and speed for your needs.

Previously, I used the example of a distortion measurement, setting attenuation to minimize analyzer distortion. This post touches on the other common scenario of improving analyzer noise floor, including the choice of input attenuation. A lower noise floor is important for finding and accurately measuring small signals, and for measuring the noise of a DUT.

One of the first items to add is the tolerable amount of analyzer noise contribution and the amplitude error it can cause. If you’re measuring the noise of your DUT, the table below from my post on low SNR measurements summarizes the effects.

Noise ratio or signal/noise ratio or SNR and measurement error from analyzer noise floor or DANL

Examples of amplitude measurement error values—always positive—resulting from measurements made near the noise floor. Analyzer noise in the selected resolution bandwidth adds to the input signal.

Only you can decide how much error from analyzer noise is acceptable in the measurement; however, a 10 dB noise ratio with 0.41 dB error is not a bad place to start. It’s worth noting that a noise ratio of about 20 dB is required if the error is to be generally negligible.

Sadly, the input attenuation setting for best analyzer noise floor is not the same as that for best distortion performance. The amount of analyzer distortion you can tolerate is another useful factor. Reducing attenuation will improve analyzer noise floor and SNR, but at some point the cost in analyzer distortion performance may outweigh the benefit. And remember that video averaging provides a “free” noise floor benefit of 2.5 dB for measurements of CW signals.

Comparison of signal measurement and analyzer noise floor for different values of input attenuation and use of video averaging to improve effective noise floor

Reducing input attenuation by 12 dB improves noise floor by a similar amount, as shown in the yellow and blue traces. Using a narrow video bandwidth (purple trace) for averaging reduces the measured noise floor but does not affect the measurement of the CW signal.

You can consult your analyzer’s specifications to find its warranted noise floor and adjust for resolution bandwidth, attenuation, etc. That approach may be essential if you’re using the measurements to guarantee performance of your own, but your specific needs are another crucial data point. If you simply want the best performance for a given configuration, you can experiment with attenuation settings versus distortion performance to find the best balance.

Many analyzer specs also include “typical” values for some parameters, and these can be extremely helpful additions. Of course, only you can decide whether the typicals apply, and whether it’s proper for you to rely on them.

If you use Keysight calibration services, they may be another source of information. Measurement results are available online for individual instruments and can include the measurement tolerances involved.

Signal analyzers themselves can be a source of information for improving measurements, and the Noise Floor Extension feature in some Keysight signal analyzers is a useful example. Each analyzer contains a model of its own noise power for all instrument states, and can automatically subtract this power to substantially improve its effective spectrum noise floor.

For microwave measurements, many signal analyzers use preselector filters to remove undesirable mixing products created in the analyzer’s downconversion process. However, these filters have some insertion loss, which increases the analyzer’s effective noise floor. A valuable nugget that you alone have is whether the mixing products or other signals will be a problem in your setup. If not, you can bypass the preselector and further improve the noise floor.

Finally, one often-overlooked tidbit is whether the signal in question is a consistently repeating burst or pulse. For these signals, time averaging can be a powerful tool. This averaging is typically used with vector signal analysis, averaging multiple bursts in the time domain before performing signal analysis. It can improve noise floor dramatically and quickly, and the result can be used for all kinds of signal analysis and demodulation.

Sorry for going on so long. There are other potential sources of information you can add, but these are some of the most useful I’ve found. If you know of others, please add a comment to enlighten the rest of us.