Since the 1980's, one of the most profound areas of change in spectrum analysis has been the application of digital technology to replace portions of the instrument that had previously been implemented as analog circuits. With the availability of high-performance analog-to-digital converters, the latest spectrum analyzers digitize incoming signals much earlier in the signal path compared to spectrum analyzer designs of just a few years ago. The change has been most dramatic in the IF section of the spectrum analyzer. Digital IFs 1 have had a great impact on spectrum analyzer performance, with significant improvements in speed, accuracy, and the ability to measure complex signals through the use of advanced DSP techniques.
Digital filters
A partial implementation of digital IF circuitry is implemented in the Agilent ESA-E Series spectrum analyzers. While the 1 kHz and wider RBWs are implemented with traditional analog LC and crystal filters, the narrowest bandwidths (1 Hz to 300 Hz) are realized using digital techniques. As shown in Figure 3-1, the linear analog signal is mixed down to an 8.5 kHz IF and passed through a bandpass filter only 1 kHz wide. This IF signal is amplified,
then sampled at an 11.3 kHz rate and digitized
Figure 3-1. Digital implementation of 1, 3, 10, 30, 100, and 300 Hz resolution filters in ESA-E Series
Once in digital form, the signal is put through a fast Fourier transform algorithm. To transform the appropriate signal, the analyzer must be fixed-tuned (not sweeping). That is, the transform must be done on a time-domain signal. Thus the ESA-E Series analyzers step in 900 Hz increments, instead of sweeping continuously, when we select one of the digital resolution bandwidths. This stepped tuning can be seen on the display, which is updated in 900 Hz increments as the digital processing is completed.
As we shall see in a moment, other spectrum analyzers, such as the PSA Series, use an all-digital IF, implementing all resolution bandwidth filters digitally.
A key benefit of the digital processing done in these analyzers is a bandwidth selectivity of about 4: 1. This selectivity is available on the narrowest filters, the ones we would be choosing to separate the most closely spaced signals.
1. Strictly speaking, once a signal has been digitized, it is no longer at an intermediate frequency, or IF. At that point, the signal is represented by digital data values. However, we use the term digital IF to describe the digital processing that replaces the analog IF processing found in traditional spectrum analyzers.In Chapter 2, we did a filter skirt selectivity calculation for two signals spaced 4 kHz apart, using a 3 kHz analog filter. Let's repeat that calculation using digital filters. A good model of the selectivity of digital filters is a near-Gaussian model:
At an offset of 4 kHz, the 3 kHz digital filter is down 24.1 dB compared to the analog filter which was only down 14.8 dB. Because of its superior selectivity, the digital filter can resolve more closely spaced signals.
The all-digital IF
The Agilent PSA Series spectrum analyzers have, for the first time, combined several digital techniques to achieve the all-digital IF. The all-digital IF brings a wealth of advantages to the user. The combination of FFT analysis for narrow spans and swept analysis for wider spans optimizes sweeps for the fastest possible measurements. Architecturally, the ADC is moved closer to the input port, a move made possible by improvements to the A-to-D converters and other digital hardware. Let's begin by taking a look at the block diagram of the all-digital IF in the PSA spectrum analyzer, as shown in Figure 3-2.
Figure 3-2. Block diagram of the all-digital IF in the Agilent PSA Series
In this case, all 160 resolution bandwidths are digitally implemented. However, there is some analog circuitry prior to the ADC, starting with several stages of down conversion, followed by a pair of single-pole prefilters (one an LC filter, the other crystal-based). A prefilter helps prevent succeeding stages from contributing third-order distortion in the same way a prefilter
would in an analog IF. In addition, it enables dynamic range extension via autoranging. The output of the single-pole prefilter is routed to the autorange detector and the anti-alias filter.
As with any FFT-based IF architecture, the anti-alias filter is required to prevent aliasing (the folding of out-of-band signals into the ADC sampled data). This filter has many poles, and thus has substantial group delay. Even a very fast rising RF burst, downconverted to the IF frequency, will experience a delay of more than three cycles of the ADC clock (30 MHz)
through the anti-alias filter. The delay allows time for an impending large signal to be recognized before it overloads the ADC. The logic circuitry controlling the autorange detector will decrease the gain in front of the ADC before a large signal reaches it, thus preventing clipping. If the signal envelope remains small for a long time, the autoranging circuit increases the gain, reducing the effective noise at the input. The digital gain after the ADC is
also changed to compensate for the analog gain in front of it. The result is a floating point ADC with very wide dynamic range when autoranging is enabled in swept mode.
Figure 3-3. Autoranging keeps ADC noise close to carrier and lower than LO noise
or RBW filter response
Figure 3-3 illustrates the sweeping behavior of the PSA analyzer. The single-pole prefilter allows the gain to be turned up high when the analyzer is tuned far from the carrier. As the carrier gets closer, the gain falls and the ADC quantization noise rises. The noise level will depend on the signal level frequency separation from the carrier, so it looks like a step-shaped phase noise. However, phase noise is different from this autoranging noise. Phase
noise cannot be avoided in a spectrum analyzer. However, reducing the prefilter width can reduce autoranging noise at most frequency offsets from the carrier. Since the prefilter width is approximately 2.5 times the RBW, reducing the RBW reduces the autoranging noise.
Custom signal processing IC
Turning back to the block diagram of the digital IF (Figure 3-2) , after the ADC gain has been set with analog gain and corrected with digital gain, a custom IC begins processing the samples. First, it splits the 30 MHz IF samples into I and Q pairs at half the rate (15 Mpairs/s). The I and Q pairs are given a high-frequency boost with a single-stage digital filter that has gain and phase approximately opposit to that of the single pole analog prefilter. Next, I and Q signals are low-pass filtered with a linear-phase filter with nearly ideal Gaussian response. Gaussian filters have always been used for swept spectrum analysis, because of their optimum compromise between frequency domain performance (shape factor) and time-domain performance (response to rapid sweeps). With the signal bandwidth now reduced, the I and Q pairs may be decimated and sent to the processor for FFT processing or demodulation. Although FFTs can be performed to cover a segment of frequency span up to
the 10 MHz bandwidth of the anti-alias filter, even a narrower FFT span, such as 1 kHz, with a narrow RBW, such as 1 Hz, would require FFTs with 20 million data points. Using decimation for narrower spans, the number of data points needed to compute the FFT is greatly reduced, speeding up computations.
For swept analysis, the filtered I and Q pairs are converted to magnitude and phase pairs. For traditional swept analysis, the magnitude signal is video-bandwidth (VBW) filtered and samples are taken through the display detector circuit. The log/linear display selection and dB/division scaling occur in the processor, so that a trace may be displayed on any scale
without remeasuring.
Additional video processing features
The VBW filter normally smoothes the log of the magnitude of the signal, but it has many additional features. It can convert the log magnitude to a voltage envelope before filtering, and convert it back for consistent behavior before display detection.
Filtering the magnitude on a linear voltage scale is desirable for observing pulsed-RF envelope shapes in zero span. The log-magnitude signal can also be converted to a power (magnitude squared) signal before filtering, and then converted back. Filtering the power allows the analyzer to give the same average response to signals with noise-like characteristics, such as digital communications signals, as to CW signals with the same rms voltage. An increasingly common measurement need is total power in a channel or
across a frequency range. In such a measurement, the display points might represent the average power during the time the LO sweeps through that point. The VBW filter can be reconfigured into an accumulator to perform averaging on either a log, voltage or power scale.
Frequency counting
Swept spectrum analyzers usually have a frequency counter. This counter counts the zero crossings in the IF signal and offsets that count by the known frequency offsets from LOs in the rest of the conversion chain. If the count is allowed to run for a second, a resolution of 1 Hz is achievable.
Because of its digitally synthesized LOs and all-digital RBWs, the native frequency accuracy of the PSA Series analyzer is very good (0.1% of span) . In addition, the PSA analyzer includes a frequency counter that observes not just zero crossings, but also the change in phase. Thus, it can resolve frequency to the tens of millihertz level in 0.1 second. With this design, the ability to resolve frequency changes is not limited by the spectrum analyzer,
but rather is determined by the noisiness of the signal being counted.
More advantages of the all-digital IF
We have already discussed a number of features in the PSA Series: power/ voltage/log video filtering, high-resolution frequency counting, log/linear switching of stored traces, excellent shape factors, an average-across-the display-point detector mode, 160 RBWs, and of course, FFT or swept processing. In spectrum analysis, the filtering action of RBW filters causes
errors in frequency and amplitude measurements that are a function of the sweep rate. For a fixed level of these errors, the all-digital IF s linear phase RBW filters allow faster sweep rates than do analog filters. The digital implementation also allows well-known compensations to frequency and amplitude readout, permitting sweep rates typically twice as fast as older
analyzers, and excellent performance at even four times the sweep speed.
The digitally implemented logarithmic amplification is very accurate. Typical errors of the entire analyzer are much smaller than the measurement uncertainty with which the manufacturer proves the log fidelity. The log fidelity is specified at ± 0.07 dB for any level up to 20 dBm at the input mixer of the analyzer. The range of the log amp does not limit the log fidelity at low levels, as it would be in an analog IF; the range is only limited by noise around
155 dBm at the input mixer. Because of single-tone compression in upstream circuits at higher powers, the fidelity specification degrades to ± 0.13 dB for signal levels up to 10 dBm at the input mixer. By comparison, analog log amps are usually specified with tolerances in the ± 1 dB region.
Other IF-related accuracies are improved as well. The IF prefilter is analog and must be aligned like an analog filter, so it is subject to alignment errors. But it is much better than most analog filters. With only one stage to manufacture, that stage can be made much more stable than the 4-and 5-stage filters of analog IF-based spectrum analyzers. As a result, the gain
variations between RBW filters is held to a specification of ± 0.03 dB, ten times better than all-analog designs.
The accuracy of the IF bandwidth is determined by settability limitations in the digital part of the filtering and calibration uncertainties in the analog prefilter. Again, the prefilter is highly stable and contributes only 20 percent of the error that would exist with an RBW made of five such stages. As a result, most RBWs are within 2 percent of their stated bandwidth, compared to 10 to 20 percent specifications in analog-IF analyzers.
The most important purpose of bandwidth accuracy is minimizing the inaccuracy of channel power and similar measurements. The noise bandwidth of the RBW filters is known to much better specifications than the 2 percent setting tolerance, and noise markers and channel-power measurements are corrected to a tolerance of ± 0.5 percent. Therefore, bandwidth uncertainties contribute only ± 0.022 dB to the amplitude error of noise density and
channel-power measurements.
Finally, with no analog reference-level-dependent gain stages, there is no IF gain error at all. The sum of all these improvements means that the all-digital IF makes a quantum improvement in spectrum analyzer accuracy. It also allows you to change analyzer settings without significantly impacting measurement uncertainty. We will cover this topic in more detail in the next chapter.
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