Cringe-Worthy Wireless: Industry and Personal History

  Admirable explanations and embarrassing memories

On a recent long-distance drive across western states, we encountered lightning and its usual transient interference across the AM radio band. That distinctive sound crackling from the receiver links two things in my mind: the first spark-gap wireless transmitters and my own unintentional transmissions from a spark gap driving a seven-foot Tesla coil.

Like many RF engineers, I’m fascinated by the history of radio, including the first steps on the path to where we are today. Unfortunately, I didn’t get much of this in school because our lectures only went as far back as the invention of negative feedback in the late 1920s. Practical spark-gap transmitters predated this by several decades.

That early history is enlightening, and I wanted to share an excellent—and underappreciated—explanation of it: a 1991 episode of the British Channel 4 series The Secret Life of Machines. It’s an understatement to call the series quirky and low-budget, but it’s also brilliant and entertaining. In here I do my best to create effective explanations of technical topics, but the hosts of this series have talent that I can only envy.

To see what I mean and get a glimpse of the earliest history of wireless, take a look at the series episode The Secret Life of the Radio Set. This YouTube link is one of several places where you can see the episode and others. You might want to look at the episode before reading the rest of this post. Go ahead. I’ll wait.

Welcome back. In the video, I was particularly struck by the sparks in both the transmitters and receivers. By the time I saw it, I was aware of the growing problems with spectral crowding and interference, and was working with the narrowband TDMA technologies that were being introduced to enable second-generation wireless phones. Videos of the spark-gap transmitters were an effective attention-getter in all kinds of talks about new and more spectrally efficient systems.

Early in my life as a practicing engineer my extracurricular activities included spark gaps and circuits that were the very opposite of spectrally efficient. In my defense, I didn’t come up with the design and, anyway, it was for a good cause. Here are a couple of pictures of the building process of that seven-foot Tesla coil.

Winding a mile of fine insulated wire on a Plexiglas tube to form the final stage of a Tesla coil. The completed winding is shown at right and, yes, that is a plumber’s toilet flange serving as a base anchor at the far end. Also yes, on the left that is your humble author as a younger, darker-haired practicing engineer.

Winding a mile of fine insulated wire on a Plexiglas tube to form the final stage of a Tesla coil. The completed winding is shown at right and, yes, that is a plumber’s toilet flange serving as a base anchor at the far end. Also yes, on the left that is your humble author as a younger, darker-haired practicing engineer.

The completed Tesla coil was inefficient by every measure. It was large and used high-voltage capacitors made from three-foot square panes of glass with heavy aluminum foil glued to each side. It was power hungry, driven by three neon-sign transformers that each produced 15 kV and 200 mA. I didn’t realize it at the time but it was a spectral monster, radiating power over a bandwidth that makes me cringe when I think about it now. It even made all 12 fluorescent tubes in the garage ceiling glow every time we switched it on.

Fortunately, we operated it for only a few seconds at a time, as part of a charity Halloween show. It was the centerpiece of our “Frankenstein laboratory,” sending bolts of lightning as the monster came to life and broke free to terrorize the crowd. Kids would run from the lab in a panic, only to get right back in line for the next show.

As with the radio industry of the last century, I quickly moved on to much more narrowband and civilized electromagnetic radiators. But every time I hear lightning crackle on the AM radio or the clattery, ringing buzz of a spark gap, I think of the true meaning of broadband and hope there is some sort of statute of limitations on my spectrum transgressions.

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Improving Measurement Accuracy with Information You Already Have

  Apply information about the individual instruments on your bench

I suppose design and measurement challenges can be a valuable contribution to job security. After all, if a clever and creative person like you has to struggle to hit the targets and balance the tradeoffs, you can’t be replaced with someone less talented—or by a mere set of algorithms.

However, this general promise of increased job security is scant comfort when you’re dealing with a need to improve yield, reduce the cost of test, increase margins, or otherwise engineer your way out of a jam. From time to time, you need a new tactic or insight that will inspire a novel solution to a problem.

This is ground we have walked before, looking for ways to transcend the “designer’s holy triangle” and previous posts have explained how adding information to the measurement process can be a powerful problem solver. One approach is to take advantage of published measurements of typical performance in test equipment to more accurately estimate measurement uncertainty.

In a comment on the post that described that approach, Joe Gorin explained it clearly: “What good is this accuracy if it is not a warranted specification? How can it be used in my measurement uncertainty computations? This accuracy is of great value even when not warranted. Most of us who deal with uncertainty must conform with ISO standards which call for using the statistical methods of the GUM (Guide to the Expression of Uncertainty in Measurement). The GUM, in an appendix, explains that the measurement uncertainty should be the best possible estimate, not a conservative estimate.”

To arrive at the best possible estimate, another—often overlooked—source of information is available to many of us: calibration reports for individual instruments.

The power level accuracy of an individual microwave signal generator is shown in a report generated during periodic calibration. The guaranteed specification is shown as green dashed lines (high and low limits) while blue dots represent specific measurements and the pink brackets indicate the associated uncertainty.

The power level accuracy of an individual microwave signal generator is shown in a report generated during periodic calibration. The guaranteed specification is shown as green dashed lines (high and low limits) while blue dots represent specific measurements and the pink brackets indicate the associated uncertainty.

It may not surprise you that the measured performance of this signal generator is much better than the guaranteed specifications. After all, generic specifications must apply to every one of that model and account for environmental conditions and other factors that apply to only a minority of use cases. In this example, instrument-specific information can be added to the process of determining total measurement uncertainty, yielding a substantial improvement.

Keysight calibration services test all warranted specifications for all product configurations. The resulting calibration data is available online in graphic and tabular form at Infoline for Keysight-calibrated instruments, a process that’s much easier than tracking down paper certificates inside your organization. This testing regime and data access is not universal in the industry, so if you’re not using Keysight calibration services you’ll need to check with your vendor.

The optimal use of this additional information will depend on your needs and the specifics of your measurement situation. So far I’ve only described the availability of the data, but I’m looking deeper into the practicalities of using it and will share more in my next post on this topic.

In addition, a discussion and excellent set of references are available in a paper discussing methods for pass/fail conformance that complies with industry standards.

I didn’t learn about calibration in school and my exposure to it in practice has been sporadic. However, I’ve been learning more about it in the past few months and have been impressed with the measures taken in factory and field calibration to ensure accuracy and determine its parameters. You should take advantage of all that effort—and the calibrations you pay for—whenever it will help.

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Pulse Analysis: From Many, One*

  Lots of measurements of a stochastic process may provide the deterministic number you seek

For much of my measurement career, many measurement situations have been a search for The One True Number, or at least the closest approximation I could manage. I have complained about measurements that are more stochastic than deterministic and how noise makes my work life difficult in multiple ways, including excess consumption of my remaining days on this mortal coil.

To be fair, I have also had to recognize the occasional usefulness of noise, and generally accept that it’s an inescapable part of our universe. It’s similar to my views on insects: I don’t like most of them, but I’m pretty sure there would be big problems if they weren’t here.

Recently, I’ve been looking at tools and techniques for measuring RF pulses in radar applications, and it seemed that I had entered a kind of alternate measurement domain. In the past, I’ve made many measurements of individual radar pulses, usually with the 89600 VSA software. Using a wide range of RF front ends, this software quantifies anything you might want to know about a pulse: all kinds of frequency, amplitude (average power, power vs. time, on/off ratio), timing, and modulation parameters such as chirp linearity or modulation quality. With the VSA’s time capture and repeated playback capabilities, you can make most of these measurements on a single pulse (from one, many).

No matter how accurate or comprehensive those measurements may be, they are inadequate in one important respect for applications such as radar: They do not account for the consistency of the pulses in question. The VSA software has taken a pulse-by-pulse approach and generally does not indicate repeatability, stochastic characteristics, or trends in the pulse trains or sequences.

Understanding some aspects of radar performance requires a kind of meta-analysis, quantifying the trends or repeatability limits of various parameters of the signals in question. The recent addition of option BHQ to the 89600 VSA software adds this large-scale statistical view in the form of a measurement application for pulse analysis. One typical measurement, aggregating the behavior of a multitude of pulses, is the histogram.

This histogram of best-fit FM results summarizes the behavior of thousands of pulses, automatically identifying and quantifying outliers.

This histogram of best-fit FM results summarizes the behavior of thousands of pulses, automatically identifying and quantifying outliers.

Radar is a prime example of a system in which repeatability is of critical importance, and where trend behavior can be invaluable in design optimization.

The inevitable question, however, is which parameter to analyze for trends or other statistical measures. This is where the experience, insight and intuition of the radar engineer come into play. As is true in wireless, this is another example of measurement software, powerful DSP and large multi-trace displays working together to leverage the talents of the design engineer.

The radar measurement application automatically identifies and measures large numbers of pulses. Multi-trace displays with both graphical and tabular data take advantage of an engineer’s pattern recognition to spot anomalous behavior or identify connections and causes.

The radar measurement application automatically identifies and measures large numbers of pulses. Multi-trace displays with both graphical and tabular data take advantage of an engineer’s pattern recognition to spot anomalous behavior or identify connections and causes.

Clever software and processing power is no substitute for engineering skill, but it helps distill the magnitude and complexity of pulse trains filled with complex signals. While it may not yield a single value as The One True Number, it can mitigate the risks of measuring too few pulses or analyzing too few parameters together.

If you’re interested in this sort of data reduction and analysis, please visit www.keysight.com/find/radar.

* ”From many, one” is a common translation of “E pluribus unum” from the Great Seal of the United States

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Dynamic Range and a Different Kind of Analyzer

  Signals and noise in the optical realm

It looks like I’m not the only one who finds myself wrestling with noise quite a bit, and recent developments in digital photography spurred me to briefly depart from my usual focus (pun intended) on the RF world .

I’m not departing very much, though, because digital photography can be seen as a two-dimensional type of signal analysis. Not surprisingly, many of the electrical engineers I know have at least a hobbyist interest in photography, and for quite a few it’s more than that. Our engineering knowledge helps a lot in understanding the technical aspects of making a good photograph, and I’d like to explain one recent development here.

The megapixel race in digital imaging is abating, perhaps because sensor resolution now exceeds the performance of some lenses and autofocus systems. I see this as a positive development, shifting attention to other important factors such as sensitivity or low-light performance. Sensitivity is as critical as resolution in those all-too-common situations when light is scarce and camera shake or subject movement render long exposures impractical.

Camera sensitivity goes back to the days of film, and the parameter called ISO quantifies it. In film, this sensitivity is related to grain size, but in digital imaging it’s more closely related to gain applied to the signal coming from the sensor. In an interesting correspondence, high ISO settings in a digital camera will produce noisier images that echo the coarser grain of high-ISO film.

This dance of gain and noise is awfully familiar to all of us, and I wonder if we should be suggesting to the digital imaging folks some sort of measure based on noise figure.

Today’s best digital cameras offer impressive sensitivity, driving new emphasis on a parameter near and dear to all of us: dynamic range. In the last several years, dramatic improvements in dynamic range have made cameras that are almost ISO-invariant, and this provides a big benefit for photographers.

Here’s my crude attempt at a graphical representation of the situation.

This digital image “tone flow” diagram shows how a scene with wide dynamic range may be clipped and compressed in the process of capture and conversion to JPEG format. If you rotate this diagram 90 degrees to the left, it corresponds well with the amplitude levels of an RF signal measurement.

This digital image “tone flow” diagram shows how a scene with wide dynamic range may be clipped and compressed in the process of capture and conversion to JPEG format. If you rotate this diagram 90 degrees to the left, it corresponds well with the amplitude levels of an RF signal measurement.

For RF engineers, this is familiar territory. Wider dynamic range in a measurement tool is always a good thing, and sometimes there is no substitute.

Taking advantage of this ISO-invariance is simple, though perhaps not intuitive. Instead of exposing normally for a challenging scene, the metering is set to capture desired highlights as a raw sensor output—not JPEG—file format. This may leave parts of the scene apparently underexposed, but the raw format preserves the full dynamic range of the sensor, and this allows all the tones to be brought into the desired relationship for the end result. In an ISO-invariant camera deep shadows may be brought up several stops or more without significant noise problems.

The result is more easily demonstrated than described, and an article at dpreview.com discusses the theory with examples. The folks at DPReview even consulted with Professor Eric Fossum, the inventor of the modern CMOS camera sensors that make this possible.

In a related article they also discuss the sources of noise in digital imaging, and once again there are parallels to our common vexations. I’m sure Boltzmann is in there somewhere.

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Faster-Sweeping Signal Analyzers: An Invisible Technology that Just Works

  With a benefit or two that should not remain invisible

Though we don’t always think of them in quite this way, signal measurements such as low-level spurious involve the collection of a great deal of information, and thus can be frustratingly slow. I’ve described how the laws of physics sometimes help us, but this bit of good fortune confers only a modest benefit.

Some years ago, the advent of digital RBW filters in signal analyzers brought gains in speed and performance. The improved shape factor and consistent bandwidth yielded better accuracy, and the predictable dynamic response allowed sweep speeds to be increased by a factor of two to four. The effects of a faster sweep were correctable in real time as long as the speed wasn’t increased too much.

The idea of correcting for even faster sweep speeds was promising, and the benefits have gotten more attractive as spurious, harmonics and other performance specifications get ever tighter. To meet these requirements, the principal technique for reducing noise level in a spectrum or signal analyzer is to reduce RBW, with noise floor dropping 10 dB for each 10x reduction in RBW.

Unfortunately, sweep time lengthens with the square of the RBW reduction. A 100x increase in measurement time for a 10 dB improvement in signal-to-noise is a painful tradeoff.

As has occurred in the past, clever algorithms and faster DSP have combined to improve measurements and relieve the tedium for the RF engineer:

These two measurements cover the same frequency span with the same resolution bandwidth. Option FS1 in the Keysight X-Series signal analyzers (bottom) improves measurement speed by about 50 times.

These two measurements cover the same frequency span with the same resolution bandwidth. Option FS1 in the Keysight X-Series signal analyzers (bottom) improves measurement rate by about 50 times.

Fast ASIC processing in the signal analyzer corrects for the frequency, amplitude and bandwidth effects of sweeping the RBW filters at speeds up to about 50 times faster than the traditional minimal-error speed. This improvement applies to swept—not FFT—measurements and is most beneficial when RBW is approximately 10 kHz or greater.

While the speed benefits are obvious, another may be nearly invisible:  narrower RBWs also [update: see note below]  improve repeatability.

This graph compares the repeatability (vertical axis) of fast sweep and traditional sweep. The lower level and shallower slope of the blue line show both improved repeatability and less dependence on sweep time.

This graph compares the repeatability (vertical axis) of fast sweep and traditional sweep. The lower level and shallower slope of the blue line show both improved repeatability and less dependence on sweep time.

The magnitude of the speed improvement depends on measurement specifics and analyzer configuration, but they’re achieved automatically and with no tradeoff in specifications. If slow measurements are increasing your ambient level of tedium, find more information about this technique in our fast sweep application note.

Note: Improved measurement speed and repeatability are alternative benefits in this case, contrary to the implication of my original wording. You can use the same measurement time and get improved repeatability, or you can improve measurement time without improving repeatability. I apologize for the confusion.
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Posted in Aero/Def, EMI, History, Measurement techniques, Measurement theory, Microwave, Millimeter, Signal analysis, Wireless

Measurement Statistics: Comparing Standard Deviation and Mean Deviation

  A nagging little question finally gets my attention

In a recent post on measurement accuracy and the use of supplemental measurement data, the measured accuracy in the figure was given in terms of the mean and standard deviations. Error bounds or statistics are often provided in terms of standard deviation, but why that measure? Why not the mean or average deviation, something that is conceptually similar and measures approximately the same thing?

I’ve wondered about standard and average deviation since my college days, but my curiosity was never quite strong enough to compel me to find the differences, and I don’t recall my books or my teachers ever explaining the practicalities of the choice. Because I’m working on a post on variance reduction in measurements, this blog is the spur I need to learn a little more about how statistics meets the needs of real-world measurements.

First, a quick summary: Standard deviation and mean absolute—or mean average—deviation are both ways to express the spread of sampled data. If you average the absolute value of sample deviations from the mean, you get the mean or average deviation. If you instead square the deviations, the average of the squares is the variance, and the square root of the variance is the standard deviation.

For the normal or Gaussian distributions that we see so often, expressing sample spread in terms of standard deviations neatly represents how often certain deviations from the mean can be expected to occur.

This plot of a normal or Gaussian distribution is labeled with bands that are one standard deviation in width. The percentage of samples expected to fall within that band is shown numerically. (Image from Wikimedia Commons)

This plot of a normal or Gaussian distribution is labeled with bands that are one standard deviation in width. The percentage of samples expected to fall within that band is shown numerically. (Image from Wikimedia Commons)

Totaling up the percentages in each standard deviation band provides some convenient rules of thumb for expected sample spread:

  • About one in three samples will fall outside one standard deviation
  • About one in twenty samples will fall outside two standard deviations
  • About one in 300 samples will fall outside three standard deviations

Compared to mean deviation, the squaring operation makes standard deviation more sensitive to samples with larger deviation. This sensitivity to outliers is often appropriate in engineering, due to their rarity and potentially larger effects.

Standard deviation is also friendlier to mathematical operations because squares and roots are generally easier to handle than absolute values in operations such as differentiation and integration.

Engineering use of standard deviation and Gaussian distribution is not limited to one dimension. For example, in new calculations of mismatch error the complementary elements of the reflection coefficient both have Gaussian distributions. Standard deviation measures—such as the 95% or two standard deviation limit—provide a practical representation of the expected error distribution.

I’ve written previously about how different views of data can each be useful, depending on your focus. Standard and mean deviation measures are no exception, and it turns out there’s a pretty lively debate in some quarters. Some contend, for example, that mean deviation is a better basis on which to make conclusions if the samples include any significant amount of error.

I have no particular affection for statistics, but I have lots of respect for the insight it can provide and its power in making better and more efficient measurements in our noisy world.

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A Signal Analyzer Connector Puzzler

  Is something wrong with this picture?

Many of the things that intrigue me do not have the same effect on an average person. However, you are also not an average person—or you wouldn’t be reading this blog. Thus, I hope you’ll find the following image and explanation as interesting and useful as I did. Take a close look at this Keysight X-Series signal analyzer and the bits I’ve highlighted:

The frequency range of this MXA signal analyzer extends to 26.5 GHz but it is equipped with a Type N input connector. Because N connectors are normally rated to 11 or 18 GHz, do we have a problem?

The frequency range of this MXA signal analyzer extends to 26.5 GHz but it is equipped with a Type N input connector. Because N connectors are normally rated to 11 or 18 GHz, do we have a problem?

One up-front confession: I looked at this combination of frequency range and input connector for years before it struck me as strange. I vaguely remembered that N connectors were meant for lower frequencies and finally took the time to look it up.

The explanation is only a little complicated, including some clever engineering to optimize tradeoffs, and it’s worth understanding. As always with microwaves and connections, it’s a matter of materials, precision and geometry.

First, the short summary: The N connectors used in Keysight’s 26 GHz instruments are specially designed and constructed, and their characteristics are accounted for in the instrument specifications. If you’re working above 18 GHz and using appropriate adapters such as those in the 11878 Adapter Kit, you can measure with confidence. Just connect the N-to-3.5mm adapter at the instrument front panel and use 3.5 mm or SMA hardware from there.

Why use the N connector on a 26 GHz instrument in the first place? Why not an instrument-grade 3.5 mm connector that will readily connect to common SMA connectors as well? The main reason is the strength and durability of the N connector when dealing with the bumps, twists and frequent reconnections that test equipment must endure—and still ensure excellent performance. Precision N connectors offer a combination of robustness and consistent performance that is unique in the RF/microwave world. They’re also easy to align and are generally tightened by hand.

However, there is that small matter of limited frequency range. Standard N connectors are rated to 11 GHz and precision ones to 18 GHz. Above 18 GHz, conductor size and geometry can allow amplitude and phase errors due to the moding phenomenon I described in a previous post.

Moding is a resonance phenomenon from the larger dimensions of the N connector, and the solution involves a change in the construction of the instrument’s precision N connector. This special connector has a combination of a slotless inner shield, a support bead of a special material, and higher precision construction. As a result, resonances can be eliminated or reduced to such a small magnitude that the N connector is the overall best choice for test equipment over this frequency range.

There you have it, the practical advantages of N connectors over the full 26.5 GHz frequency range, without a performance penalty.

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Signal Generators and Confidence in the Very Small

   Precisely small is more of a challenge than precisely big

Recently, I’ve been looking at sensitivity measurements and getting acquainted with the difficulty of doing things correctly at very low signal levels. It’s an interesting challenge and I thought it would be useful to share a couple of surprising lessons about specifications and real-world performance.

From the outset, I’ll concede that data sheets and detailed specifications can be boring. Wading through all that information is a tedious task, but it’s the key to performance you can count on, and specs are a reason to buy test equipment in the first place. Also, extensive specifications are better than the alternative.

Sensitivity measurements show the role and benefits of a good data sheet in helping you perform challenging tests. Say, for example, you’ve got a sensitivity target of 1 µV and you need a signal just that size because the desired tolerance is ±1 dB. In a 50Ω system, that single microvolt is −107 dBm, and 1 dB differences amount to only about 10 nV.

The hard specs for a Keysight MXG X-Series microwave signal generator are ±1.6 dB and extend to −90 dBm, so there are issues with the performance required in this situation. However, it’s worth keeping in mind that the specs cover a wide range of operating conditions, well beyond what you’ll encounter in this case.

Once again this is a good time to consider adding information to the measurement process as a way to get more from it without changing the test equipment. A relevant item from the signal generator data sheet illustrates my point.

The actual performance of a set of MXG microwave signal generators is shown over 20 GHz, and the statistical distribution is provided as well. Though the measurement conditions are not as wide as for hard specs, these figures are a better indication of performance in most situations.

The actual performance of a set of MXG microwave signal generators is shown over 20 GHz, and the statistical distribution is provided as well. Though the measurement conditions are not as wide as for hard specs, these figures are a better indication of performance in most situations.

The performance suggested by this graph is very impressive—much better than the hard specs over a very wide frequency range—and it applies to the kind of low output level we need for our sensitivity measurement. Accuracy is almost always better than ±0.1 dB, dramatically better than the hard spec.

The graph also includes statistical information that relates to the task at hand. Performance bounds are given for ±one standard deviation, and this provides a 68% confidence level if the distribution is normal (Gaussian). If I understand the math, a tolerance of ±0.2 dB would then correspond to two standard deviations and better than 95% confidence.

The time spent wading through a data sheet is amply rewarded, and the right confidence can then be attached to the performance of a tricky measurement. The confidence you need in your own measurements may be different, but the principle is the same and the process of adding information will improve your results.

So far, we’ve taken advantage of information that is generic to the instrument model involved. Even more specific information may be available to you, and I’ll discuss that in a future post.

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RF Intuition: A Strength or a Vulnerability?

  Intuition is powerful but if you don’t frame questions well it can mislead you

Contrary to the popular stereotype, good engineers are creative and intuitive. Indeed, these characteristics are essential tools for successful engineering.

I have great respect for the power of intuitive approaches to problems, and I see at least two big benefits. First, intuition can gather diffuse or apparently unrelated facts that enable exceptionally powerful analysis. Second, it often provides an effective shortcut for answers to complex questions, saving time and adding efficiency.

Of course, intuition is not infallible, and I’m always intrigued by its failure. It makes sense to pay attention to these situations because they provide lessons about using intuitive thinking without being misled by it. Two of my favorite examples are the Monty Hall Problem and why mirrors appear to reverse left and right but not up and down.

I’d argue that a common factor in most intuition failures is not so much the reasoning process itself but the initial framing of the question. If you start with a misapprehension of some part of the problem or question, even a perfect chain of reasoning will fail you.

As a useful RF example, let’s look at an intuition failure in “sub-kTB” signal measurements. Among RF engineers, kTB is shorthand for -174 dBm/Hz*, which is the power delivered by a 50Ω thermal source into a 50Ω load at room temperature. It should therefore be the best possible noise level—or, more accurately, noise density or PSD—you could obtain in a signal analyzer that has a perfect 0 dB noise figure.

Not surprisingly, many engineers also see this as the lowest possible signal level one could measure, a kind of noise floor or barrier that one could not see beyond or measure beneath. As a matter of fact, even this level should not be achievable because signal analyzers contribute some noise of their own.

This intuitive expectation of an impenetrable noise floor is logical but flawed, as demonstrated by the measurement example below that uses Keysight’s Noise Floor Extension (NFE) feature in a signal analyzer. Here, a multi-tone signal with very low amplitude is measured near the signal analyzer’s noise floor.

The noise marker shows that the effective noise floor of the measurement (blue) is actually below kTB after NFE removes most of the analyzer’s noise. The inset figure shows how a signal produces a detectable bump in the analyzer’s pre-NFE noise floor (yellow), even though it’s about 5 dB below that noise floor.

The noise marker shows that the effective noise floor of the measurement (blue) is actually below kTB after NFE removes most of the analyzer’s noise. The inset figure shows how a signal produces a detectable bump in the analyzer’s pre-NFE noise floor (yellow), even though it’s about 5 dB below that noise floor.

I’ve previously described NFE, and for this discussion I’ll summarize by saying that it allows some analyzers to accurately estimate their own noise contribution and then automatically subtract most of it from the measurement. The result is a substantial improvement in effective noise floor and the ability to separate very small signals from noise.

While it is indeed correct that kTB is a noise floor that cannot be improved, or even matched in an analyzer, the error in intuition is in associating this in a 1:1 fashion with an ultimate measurement limit. As discussed previously, signal and noise power levels—even very small ones—can be reliably added or subtracted to refine raw measurement results.

kTB and related noise in analyzers are phenomena whose values, when averaged, are predictable when the measurement conditions and configuration are known. Consequently, subtracting analyzer noise power can be seen as adding information to the measurement process, in turn allowing more information to be taken from the measurement result.

OK, so measuring below kTB is perhaps more of a parlor trick than a practical need. However, an intuitive understanding of its possibility illuminates some important aspects of making better RF measurements of those tiny signals that so frequently challenge us.

* You may instead see the figure -177 dBm/Hz for kTB. This refers to a slightly different noise level measurement than that of a spectrum or signal analyzer, as explained at the link.

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Envelope Tracking and Riding the Gain

  You can “turn it up to eleven” as long as you don’t leave it there

When I first heard the term “envelope tracking” I thought of the classic investigative/surveillance technique called “mail cover” in which law enforcement gets the postal service to compile information from the outside of envelopes. The practice was in the news a while back due to its use with digital communications.

Learning a little more, I quickly realized that it has nothing to do with the mail but, like the mail, has precedent that reaches back many years. “Riding the gain” or “gain riding” is a manual process that has been used for decades in audio recording and other applications where excessive dynamic range is a problem. Its use predates vinyl records, though I first encountered it in my previous life as a radio announcer, broadcasting live events.

When I was riding the gain, it was a manual process of twisting a knob, trying to reduce input dynamic range to something a small-town AM transmitter could handle. I was part of a crude feedback system, prone to delay and overshoot, as I’m sure my listeners would attest.

These days, envelope tracking is another example of how digital processing is used to solve analog problems. In this case it’s the conflict between amplifier efficiency and the wide variations in the RF envelope of digital modulation. If the power supply of an RF amplifier can be dynamically adjusted according to the power needed by modulation, it can—at every instant—be operating at its most efficient point.

In envelope tracking an RF power amplifier is constantly adjusted to track the envelope of the modulated input signal. The amplifier operates at higher efficiency and lower temperature, using less battery power and potentially creating less adjacent-channel interference.

In envelope tracking an RF power amplifier is constantly adjusted to track the envelope of the modulated input signal. The amplifier operates at higher efficiency and lower temperature, using less battery power and potentially creating less adjacent-channel interference.

Power efficiency has always been a major driver in mobile communications and its importance continues to grow. Batteries are limited by the size and weight of the handsets users are willing to carry and, yet again, Moore’s Law points the way to improvement. Available DSP now has the high speed and low power consumption to calculate RF envelope power on the fly. The envelope value is fed to a power supply with sufficient bandwidth or response time to adjust its drive of the RF power amplifier accordingly.

An envelope tracking power amplifier (ETPA) is dynamically controlled for optimum efficiency by tracking the required RF envelope power. The tracking is based on envelope calculations from the I/Q signal, modified by a shaping table.

An envelope tracking power amplifier (ETPA) is dynamically controlled for optimum efficiency by tracking the required RF envelope power. The tracking is based on envelope calculations from the I/Q signal, modified by a shaping table.

This all seems fairly straightforward but, of course, is anything but. The calculation and response times are very short, and a high degree of time alignment is required. Power supplies must be extremely responsive and still very efficient. All of the DSP must itself be power efficient, to avoid compromising the fundamental power benefit.

Envelope tracking is a downstream solution to power amplifier efficiency, joining previous upstream techniques such as crest factor reduction and macro-scale approaches such as digital predistortion. To a great extent, all rely on sophisticated algorithms implemented in fast DSP.

That’s where Keysight’s design and test tools come in. You can find a collection of application notes and other information at www.keysight.com/find/ET.

With envelope tracking you can now turn your amplifiers up to eleven when you need to, and still have a battery that lasts all day.

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Posted in Signal analysis, Signal generation, Wireless
About

Agilent Technologies Electronic Measurement Group is now Keysight Technologies http://www.keysight.com.

My name is Ben Zarlingo and I’m an applications specialist for Keysight Technologies. I’ve been an electrical engineer working in test & measurement for several decades now, mostly in signal analysis. For the past 20 years I’ve been involved primarily in wireless and other RF testing.

RF engineers know that making good measurements is a challenge, and I hope this blog will contribute something to our common efforts to find the best solutions. I work at the interface between Agilent’s R&D engineers and those who make real-world measurements, so I encounter lots of the issues that RF engineers face. Fortunately I also encounter lots of information, equipment, and measurement techniques that improve accuracy, measurement speed, dynamic range, sensitivity, repeatability, etc.

In this blog I’ll share what I know and learn, and I invite you to do the same in the comments. Together we’ll find ways to make better RF measurements no matter what “better” means to you.

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