How to capture transient and sudden RF interference sources? Puyuan spectrum analyzer can help!

Due to the limited and non-renewable radio spectrum resources, and the sharp increase in the number of users and radio applications, the current RF spectrum is becoming more and more crowded and busy. How to use the limited RF spectrum resources more efficiently is the primary issue facing current radio practitioners.

At the same time, the difficulty of product design and testing has also increased. Engineers must not only ensure that their products comply with relevant regulations, but also need to pay attention to the impact of other RF interference sources on their products. How to capture these occasional RF interference sources and fully understand the multi-dimensional information such as signal frequency, power, probability, time, etc. is an important challenge facing RF engineers today.

Compared with traditional RF receivers or spectrum analyzers, real-time spectrum analyzers provide many excellent functions for radio monitoring and spectrum management applications. The real-time analysis characteristics of real-time spectrum analyzers can quickly and reliably capture transient, sudden, elusive and complex signals; however, capturing them is not enough, how to present the details and characteristics of the signal? This requires the use of the probability density spectrum display function in the real-time spectrum analysis mode, which is also what we often call persistence display.

What is a probability density spectrum?

Figure 1 Schematic diagram of probability density spectrum

Probability density refers to the number of times a frequency and amplitude point appears within a collection interval, and is usually also called a persistence diagram. The X-axis represents frequency, the Y-axis represents amplitude, the Z-axis represents the number of occurrences, and the T-axis represents time. By using color to represent the Z-axis and brightness to represent the T-axis time, four-dimensional data can be displayed in a two-dimensional interface.

As shown in Figure 1, in the density spectrum view, a white trace is also displayed. This trace shows the real-time spectrum of the most recent sampling interval. When positive peak, negative peak or average detection is used, the white trace obtains detection data from all data in the sampling interval; when sampling detection is used, the last FFT calculation result is used. In order to display the signal status over a longer time range, multiple probability density graphs can be displayed on the display. The latest probability density graph is displayed with the maximum brightness. The longer the time is from the current time, the lower the brightness of the probability density graph.

Discover the detailed characteristics of the measured signal. The probability density spectrum is particularly suitable for analyzing signal characteristics that are difficult to detect with traditional methods. We compare the monochrome spectrum display of a traditional spectrum analyzer with the probability density spectrum analysis screen of the RSA real-time spectrum analyzer to illustrate the difference between the two.

Figure 2: The left picture shows the traditional frequency sweep monochrome spectrum display, and the right picture shows the probability density spectrum display.

Figure 2 shows the analysis and display of the same Wlan signal using the traditional swept monochrome spectrum and probability density spectrum. We can clearly see that the probability density spectrum can clearly display the uplink and downlink content of the Wlan signal, while the traditional spectrum analyzer can only use a monochrome track to display the peak amplitude of the signal. This is because the probability density spectrum uses color-graded color temperature technology to use colors to represent the frequency and time information of different signal occurrences.

§ First, the frequency of signal occurrence is represented by the depth of color. Events with low frequency of occurrence are represented by lighter colors, and events with high frequency of occurrence are represented by darker colors.

§ Secondly, the time information of the signal appearance is displayed by adjusting the color brightness. The signal that has just appeared is displayed with the highest brightness. The longer the time is from the current time, the lower the brightness of the signal.

In contrast, traditional scanning spectrum analyzers use the "power accumulation" principle and can only display the signal with the highest energy in the maximum hold trace, but cannot distinguish whether there are other signals at the same frequency point or frequency band.

Discovering hidden signals

The probability density spectrum is very effective in detecting hard-to-find instantaneous signals or finding time-varying small energy signals hidden under the spectrum of other signals.

Figure 3 Probability density spectrum to discover hidden signals

Usually, transient small signals hidden under large signals are difficult to detect, but they can have a serious impact on the quality and safety of the signal. With the emergence of real-time spectrum analyzers, even if the small signal appears irregularly and lasts for a short time, the probability density spectrum can be used to display the small signal on the screen, which makes it possible to further analyze and intervene in the signal.

When there are multiple signals overlapping in a frequency band, it is almost impossible to separate these signals with a traditional analyzer. The display usually only shows the result of the signal energy superposition at each frequency point.

In Figure 4, in the 2.4GHz frequency band, there are WLAN signals, Bluetooth transmission signals, and multiple signals with low repetition rates and wide frequency bands covering the entire frequency band. When there are multiple different signals in the same frequency band, it is difficult to check which signals exist with the traditional single-color swept spectrum, and it is even impossible to distinguish different signals. By applying the probability density spectrum, the whole problem can be easily solved.

Figure 4 Probability density spectrum to distinguish the same frequency signal

When two time-varying signals overlap, the probability density spectrum is a handy tool for showing the difference between the signals. In the figure above, we can easily distinguish between multiple overlapping signals on the same channel. The color of the signal tells us how often each signal appears, and the brightness of the signal tells us when each signal appears.

In addition, we can easily analyze that the low-frequency noise-like broadband signal interferes with all other signals, and it is easy to see from the display which signal is stronger and which signal is weaker than the interference signal.

Observing the crowded spectrum

Figure 5 Probability density spectrum observation of crowded spectrum

Figure 5 is an example of some dynamically changing signals, transient mutation signals, and time-sharing display of multiple signals within the same frequency band. If we cannot effectively view all these signals, we may not know whether they are working effectively in time-sharing or whether they will cause serious interference to each other.

The probability density spectrum can not only help us view time-varying RF signals that could not be displayed separately or even at all before, but also accurately analyze the specific information contained in the signal. Even when they are completely overlapped, the probability density spectrum display can still distinguish and reveal the properties of multiple signals that change over time, and can clearly distinguish and view their amplitude and frequency. In traditional monochrome displays, it is impossible to present such results.

Summarize:

The probability density spectrum demonstrated above is just a small function of the RIGOL RSA series real-time spectrum analyzer.