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2017
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The Four Ws of Spurious Emissions

Posted by benz Apr 30, 2017

  Refining your design and creating happy customers

 

Note from Ben Zarlingo: I invite you to read this guest post by Nick Ben, a Keysight engineer. He discusses the what, why, when and where of spurious emissions before delving into the importance of identifying them in your device’s transmitting signal and thereby improving your product design.

 

If you’re reading this, you’re probably an engineer. That means you may be looking for ways to improve your present and future designs. If yours includes a transmitter, one key to success is checking for spurious emissions that can interfere with signals outside your device’s designated bandwidth. Characterizing spurious behavior can save money and help you create happy and loyal customers.

But wait, you say: WHAT, WHY, WHEN and WHERE can I save money and create happy customers by measuring spurious emissions? I’m glad you asked. Let’s take a look.

What: A Quick Reminder

Ben Z has covered this: Spurs are unwanted stray frequency content that can appear both outside and within the device under test’s (DUT’s) operating bandwidth. Think of a spur as the oddball signal you weren’t expecting to be emanating from your device—but there it is. Just like you wouldn’t want your family’s phones to overlap with one another in the same band, they shouldn’t interfere with that drone hovering outside your window (made you look). If you refer to the traces below, the left side presents a device’s transmitted signal without a spur and the right side reveals an unwanted spurious signal, usually indicating a design flaw.  

 

Two GSM spectrum measurements are compared.  The one on the right contains a spurious signal.

In these side-by-side views of a 1 MHz span at 935 MHz, the presence of an unwanted spur is visible in the trace on the right. Further investigation should identify the cause.

Why and When

In densely populated regions of the frequency spectrum, excessive spurs and harmonics are more likely to be both troublesome and noticed. To prevent interference with devices operating on nearby bands, we need to measure our own spurious emissions.

Where (& How)

Spur measurements are usually performed post-R&D, during the design validation and manufacturing phases. Using a spectrum or signal analyzer, these measurements are presented on a frequency vs. amplitude plot to reveal any undesirable signals. Spur characterization is done over a frequency span of interest using a narrow resolution bandwidth (RBW) filter and auto-coupled sweep-time rules. The sweep normally begins with the fundamental and reaches all the way up to the tenth harmonic.

While current spur-search methods are good for the design validation phase, they aren’t great because measurements are too slow for pass/fail testing of thousands of devices on the production line. These tests are often based on published standards (perhaps from the FCC) that may be described in terms of a spectrum emission mask (SEM). Fortunately, SEM capability is available in Keysight X-Series signal analyzers.

To tackle the issue of slow sweep times and enable faster testing, today’s signal analyzers use digital technologies, especially DSP, to improve measurement speed and performance (see one of Ben’s earlier posts). Ultimately, you can achieve faster sweep times—as much as 49x faster than older analyzers—when chasing low-level signals at wide bandwidths.

Wrapping Up

If you’d like to learn more, a recent application note titled Accelerating Spurious Emission Measurements using Fast-Sweep Techniques includes detailed explanations, techniques, and resources. You’ll find it in the growing collection on our signal analysis fundamentals page.

I hope my first installment of The Four Ws of X provided some information you can use. Please post any comments—positive, constructive, or otherwise—and let me know what you think. If it was useful, please give it a like and, of course, feel free to share.

  Electrical engineers lead the way

Years ago, a manager of mine seemed to constantly speak in analogies. It was his way of exploring the technical and business (and personal!) issues we faced, and after a while I noticed how often he’d begin a sentence with “It’s like...”

His parallels came in a boundless variety, and he was really creative in tying them to our current topic, whatever it was. He was an excellent manager, with a sense of humor and irony, and his analogies generally improved group discussions.

There were exceptions, of course, and misapplying or overextending qualitative analogies is one way these powerful tools can let us down.

The same is true of quantitative analogies, but many have the vital benefit of being testable in ways that qualitative analogies aren’t. Once validated, they can be used to drive real engineering, especially when we can take advantage of established theory and mathematics in new areas.

The best example I’ve found of an electrical engineering (EE) concept with broad applicability in other areas is impedance. It’s particularly powerful as a quantitative analogy for physical or mechanical phenomena, plus the numerical foundations and sophisticated analytical tools of EE are all available.

Some well-established measurements even use the specific words impedance and its reciprocal—admittance. One example is the tympanogram, which plots admittance versus positive and negative air pressure in the human ear.

Two "tympanograms" are impedance measurements of the human hearing system. These diagrams show admittance (inverse of impedance) to better reveal the response of the system at different pressures, to help diagnose problems.

These tympanograms characterize the impedance of human hearing elements, primarily the eardrum and the middle ear cavity behind it, including the bones that conduct sound. The plot at the left shows a typical maximum at zero pressure, while the uniformly low admittance of the one on the right may indicate a middle ear cavity filled with fluid. (Image from Wikimedia Commons)

Interestingly, those who make immittance* measurements of ears speak of them as describing energy transmission, just like an RF engineer might.

Any discussion of energy transmission naturally leads to impedance matching and transformers of one kind or another. That’s where the analogies become the most interesting to me. Once you see the equivalence outside of EE, you start noticing it everywhere: the transmission in a car, the arm on a catapult, the exponential horn on a midrange speaker. One manufacturer of unconventional room fans has even trademarked the term “air multiplier” to describe the conversion of a small volume of high-speed air to a much larger volume at lower speeds.

All of these things can be quantitatively described with the power of the impedance analogy, leading to effective optimization. It’s typically a matter of maximizing energy transfer, though other tradeoffs are illuminated as well.

Maybe my former manager’s affection for analogies rubbed off on me all those years ago. I certainly have a lot of respect for them, and their ability to deliver real engineering insight in so many fields. We EEs can take some degree of pride in leading the way here, even if we’re the only ones who know the whole story.

*A term coined by our old friend H. W. Bode in 1945.

  The difference can be anything from 0 dB to infinity

Most RF engineers are aware that signal measurements are always a combination of the signal in question and any contributions from the measuring device. We usually think in terms of power, and power spectrum has been our most common measurement for decades. Thus, the average power reading at a particular frequency is the result of combining the signal you’re trying to measure plus any extra “stuff” the analyzer chips in.

In many cases we can accept the displayed numbers and safely ignore the contribution of the analyzer because its performance is much better than that of the signal we’re measuring. However, the situation becomes more challenging when the signal in question is so small that it’s comparable to the analyzer’s own contribution. We usually run into this when we’re measuring harmonics, spurious signals, and intermodulation (or its digital-modulation cousin, adjacent channel power ratio, ACPR).

I’ve discussed this situation before, particularly in spurious measurements in and around the analyzer’s noise floor.

Graphic explanation of addition of CW signal and analyzer noise floor. Diagram of apparent and actual signals, actual and displayed signal-to-noise ratio (SNR).

Expanded view of measurement of a CW signal near an analyzer’s noise floor. The analyzer’s own noise affects measurements of both the signal level and signal/noise.

It’s apparent that addition happens between the power of the signal and that of the analyzer’s internal noise. For example, when the actual signal power and the analyzer noise floor are the same, the measured result will be high by 3 dB.

However, it’s essential to understand that the added power is in the form of noise, which is not coherent with the signal—or anything else. The general incoherence of noise is a valuable assumption in many areas of measurement and signal processing.

We can get tripped up when we unknowingly violate this assumption. Consider the addition of these CW examples in the time domain, where the problem is easier to visualize:

Graphic explanation of addition of two equal-amplitude CW signals, showing effect of relative phase. In-phase addition produces a total power 6 dB greater than an individual signal, while out-of-phase addition results in zero net power.

The addition of coherent signals can produce a wide range of results, depending on relative amplitude and phase. In this equal-amplitude example, the result can be a signal with twice the voltage and therefore 6 dB more power (top) or a signal with no power at all (bottom).

I’ve previously discussed the log scaling of power spectrum measurements and the occasionally surprising results. The practical implications for coherent signals are illustrated by the two special cases above: two equal signals with either the same or opposite phase.

When the signals have the same phase, they add to produce one with four times the power, or an amplitude 6 dB higher. With opposite phase the signals cancel each other, effectively hiding the signal of interest and showing only the measurement noise floor. Actual measurements will fall somewhere between these extremes, depending on the phase relationships.

This coherent addition or subtraction isn’t typically a concern with spurious signals; however, it may arise with harmonic and intermodulation or ACPR measurements in which the analyzer’s own distortion products might be coherent with the signal under test. Some distortion products might add to produce a power measurement that is incorrectly high; others could cancel, causing you to miss a genuine distortion product.

I suppose there are two lessons for RF engineers. One: some measurements may need a little more performance margin to get the accuracy you expect for very small signals. The other: be careful about the assumptions for signal addition and the average power of the result.

In a future post I’ll describe what we learned about how this applies to optimizing ACPR measurements. Until then, you can find more information on distortion measurements at our signal analysis fundamentals page.