Use spectrum/signal analysis to limit RF power and spurious noise emissions -

Use spectrum/signal analysis to limit RF power and spurious noise emissions


Frequency-domainmeasurement of RF power is one of the basicmeasurements done by spectrum andvectorsignal analyzers. These systems need to comply with standardlimits on power transmission or spuriousnoiseemissions , and appropriate measurement techniques must beconfigured to avoid errors.

Key frequency controls such as frequency span, center frequency,resolution bandwidth(RBW) andmeasurement time affect measurement results.

Frequency span describes thetotal frequency spectrum captured by the analyzer. Meanwhile, centerfrequency corresponds to the center of a frequency span. It is notedthat center-frequency control, likefrequency span, defines the frequency range on the front panel of theinstrument. On the other hand, FFT signal analyzers have twodistinct modes for acquisition based on the size of the frequency span.

Figure1: A spectrum analyzer plots in a frequency vs. amplitude trace.

Frequency spans up to the RBW specifications of the instrument areacquired by downconverting a single frequency block and digitizing thedownconverted signal. For frequency spans exceeding RBW, spectrumblocks are sequentially converted and digitized.

RBW controls the frequency resolution of the frequency axis. Intraditional analyzers, a narrowband filter is swept across a frequencyspan to create a spectrum display. Filter bandwidth determines thefrequency resolution across the frequency axis, hence the label of thecontrol.

Table1: RBW frequency resolution relates to the bin size of the FFT analyzer.

Meanwhile, FFT-based analyzers have no analog filter and instead useFFT and its associated windowing parameters to determine frequencyresolution or RBW. Current FFT-based analyzers, unlike traditionalspectrum analyzers, can offer a choice of windows to limit spectralleakage and improve resolution of closely spaced tones in the frequencydomain.

Those familiar with FFT analyzers and FFT may question how the RBWfrequency resolution relates to the bin size of the FFT. Table 1 above shows how RBWfrequency resolution parameter (specified at 3dB and 6dB RBWdefinitions) relates to FFT bin size in modern RF signal analyzers.

FFT-based analyzers offer a choice of windows to limit spectralleakage and improve resolution of closely-spaced tones in the frequencydomain. Traditional spectrum analyzers do not offer such capability.

Measurement (or sweep) time of a traditional swept-tuned analyzer isinversely proportional to the square of the RBW because of the analogfilter settling-time. Lowering RBW to improve frequency resolution canexponentially increase sweep time.

In contrast, the FFT signal analyzer performs a longer acquisitionand larger FFT calculation as RBW is lowered. With DSP devices becomingfaster, measurement speed results in higher-frequency resolution ornarrow RBW measurements.

Amplitude settings
Different amplitude controls also affect measurement results. Theseinclude reference level (ref level), attenuator settings and detectionmode.

Ref level sets the maximum input range of the spectrum analyzer. Itcontrols the y-axis similar to the volts/div on an oscilloscope, andmust be set just above the expected maximum power measurement.

The optimum ref level is a balance between minimizing the instrumentdistortion (caused by very low ref level that saturates input signal)and noise floor (caused by very high ref level that reduces instrumentsensitivity and dynamic range).

Sometimes, it is beneficial to set a low ref level, creatinginstrument distortion, for broadband noise measurements. Doing soimproves measurement sensitivity as it recognizes distortion productsand ensures that they are excluded in measurements.

The input range of the instrument is also set by the attenuatorsettings control. This is normally set to automatic so that thesoftware adjusts the attenuation level based on the ref level setting.

Table2: The spectrum analyzer detection modes can impact on powermeasurements

Spectrum analyzers tie the display's y-axis to the ref level orattenuator settings in firmware. For virtual instruments that are notconstrained, the display's y-axis can be decoupled from these controlsif desired. This feature enables visual zoom-in on a portion of thespectrum without affecting amplitude settings of the instrument.

Note that the ref level and the attenuator setting both affect theprogrammable attenuator, thus only one of the controls needs to be set.

Detection mode, another amplitude control, applies for traditionalswept-tuned spectrum analyzers, but not for FFT-based analyzers.Classified as normal, peak, sample or negative peak, detection-modecontrol determines how the spectrum analyzer handles reduction orcompression of spectral information.

It also impacts on integrated power measurement. When spectrum datapoints exceed what the spectrum analyzer can display, the analyzerproceeds to a data reduction strategy. This causes the detection modeto alter power measurement.

Factors for precision
A spectrum analyzer uses a frequency sweep between start and stopfrequencies. An analog ramp generates this frequency sweep while thestart frequency of the span is synthesized from a high accuracytime-base reference. Thus, accuracy in measurement is limited by theanalog ramp signal and the IF filter center frequency.

Without such analog ramps, FFT-based analyzers are not constrainedby these factors and exhibit uniform accuracy in frequency measurementacross a span. Accuracy across the span depends on the time base andthe measurement algorithm, thus frequency accuracy and repeatabilityare obtained more easily.

In a traditional swept-tuned analyzer, the causes of frequency errorinclude reference frequency error, frequency span accuracy (5 percentof the span) and RBW (15 percent of the RBW). In contrast, causes offrequency error in an FFT-based analyzer are reference frequency errorand RBW, which depends on the measurement algorithm and varies from> 50 percent to < 10 percent of RBW.

To compare these errors, the reference frequency error must beignored because a precise frequency reference such as a rubidium sourcecan compensate for it. Frequency span of above 50kHz and RBW settingsexceeding 1kHz compromise measurement on a swept-tuned spectrumanalyzer, unless optimization techniques – such as placing the 100MHztone at the center of the span – are applied.

Using smaller RBW translates to longer measurements because of thesweep time as a typical spectrum analyzer has a sweep time of 150-200ms for the example.

The measurement algorithm limits the accuracy of an FFTbasedanalyzer. For example, modern toolkits for spectral measurement applyinterpolation techniques to measure tone frequency at a higherresolution than that indicated by the RBW, such that in the aboveexample, RBW set at 2kHz would guarantee better accuracy.

FFT-based analyzers use comparatively larger RBW settings toaccurately measure frequencies, even without applying accuracy-optimized measurement techniques.

This means faster and more accurate measurements in the same testtime. Signal analyzers can perform the measurement example in less than20ms, which is six times better than spectrum analyzers.

Unless appropriate instrument settings are applied, a wide variancein measurement results can be expected even from the same instrument.Thus, understanding its operation is essential to properly set up theinstrument for measurement.

Cindy Ong is Marketing Engineerat National Instruments Corp.

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