Originally posted Apr 10, 2014
CW signals, impulsive ones, and our old friend noise
In my mind, the fundamental task of spectrum analysis had always been a simple matter of separating one signal from another. That changed when a diagram from a spectrum-analyzer designer got me thinking a little more broadly and deeply. Here’s what I learned about making better measurements on signals that have different characteristics.
It’s fair to think of a spectrum analyzer sweeping a narrow filter across a selected chunk of the RF spectrum as a way to separate and measure the signals there. Sometimes the signals are closely spaced, sometimes not. Sometimes we’re measuring the separable parts of a single signal, sometimes we’re measuring different signals, and sometimes we aren’t sure.
Traditionally, most spectrum analysis hardware and techniques made an implicit assumption that the signals in question were CW, and therefore both the techniques and the results were valid for this case. What that meant for practical measurements was that we’d choose a resolution bandwidth (RBW) filter narrow enough to separate signals that were as closely spaced as we expected, or one that offered the frequency resolution we needed.
We might also choose a narrow RBW filter as a way to reduce the apparent noise floor of the measurement and thereby improve measurement accuracy. A factor of 10 reduction in the RBW reduces the noise power in the filter by the same amount (10 dB) while not affecting the measurement of the CW signals we’re trying to measure. That improves the signal/noise ratio (SNR) of the measurement.
While we get powerful benefits from narrow RBWs, there are tradeoffs, most obviously in measurement time. Because the maximum sweep rate of traditional filters varies as the square of the RBW, it can be painful to get the benefits of a narrow one when sweep rate is the limiting factor.
Of course, today’s RF spectrum is full of non-CW signals—even the spurs may be bursted or modulated—and so I haven’t yet described the whole story. Here’s the diagram that made things clearer for me:
The effective SNR of spectrum measurements varies according to the RBW in different ways for CW and impulsive signals, though noise is unaffected. The relationships are linear in this log-log format.
The diagram shows how SNR varies with RBW for different signal types. It can also be interpreted as showing how effectively different RBW values separate the signals we want to measure from the noise that makes the whole process difficult. It also shows how RBW can be adjusted to better separate one type of discrete signal from another, a different dimension than the frequency-domain separation we usually think of.
On the left, in blue, is the situation we’re all familiar with. Reducing RBW provides a benefit of 10 dB/decade in separating CW signals from noise.
The red curve at the right is an interesting opposite to the CW situation. For impulsive signals, a narrower RBW desensitizes the spectrum analyzer as it tends to block signals when the filter is narrower than the equivalent bandwidth of the signal.
The insight I got from the diagram is that the best RBW is one that is matched to the characteristics of the signal under test. This adjustment of RBW will provide the most effective separation and measurement of signals in an environment of noise and other signals.
Of course, some signals aren’t purely CW or impulsive, and you may need to do some experimentation or calculation to optimize your own measurements for speed, performance or other priorities.
Speaking of measurement speed, the sweep-rate penalty mentioned above for narrow RBWs is much less in some modern analyzers than in previous generations. Advanced digital filters and FFT techniques have made narrow-RBW measurements much less painful, and that’s a good topic for a future post.