Spectrum Analysis Fundamentals: Quickly Complete Efficient Measurements

       All electronic design engineers and scientists have performed electrical signal analysis, or signal analysis for short. Through this basic measurement, they can gain insight into signal details and obtain important signal characteristic information. However, the effectiveness of signal analysis depends mainly on the performance of the measurement instrument, and spectrum analyzers and vector signal analyzers are the two most commonly used test equipment for electrical signal analysis.

　　Spectrum analyzers are widely used multi-purpose measurement tools that can measure the magnitude of output signals relative to frequency in order to understand the spectral power of known and unknown signals. Vector signal analyzers can simultaneously measure the magnitude and phase of the output signal of the analyzer's intermediate frequency (IF) bandwidth, and are often used to perform in-channel measurements on known signals, such as error vector magnitude (EVM), domain code power, and spectral flatness. In the past, spectrum analyzers and vector signal analyzers were two separate instruments, but with the continuous advancement of measurement technology, measurement equipment manufacturers can now integrate them into one machine, which is generally called a spectrum analyzer.

　　With the powerful measurement and analysis capabilities provided by these analyzers, engineers can quickly and comprehensively understand the components or systems they are developing and designing. In order to make full use of the various functions of spectrum analyzers, users must understand how they operate to fully meet the measurement needs of specific applications.

　　Understand the basic principles of spectrum analyzer

　　In addition to understanding the various functions of the analyzer, users also need to understand the basic operating principles of spectrum analysis. In the past, oscilloscopes were often used to perform time domain measurements in order to observe the changes in electrical signals over a period of time, but this did not allow for a glimpse of the full picture of the signal. In order to fully understand the performance of a component or system, engineers must analyze the signal in the frequency domain, which is exactly what a spectrum analyzer does. However, with the significant advances in digital technology, the distinction between various instruments is no longer so clear. For example, some oscilloscopes can now also perform vector signal analysis, while signal analyzers are beginning to provide a number of time domain measurement functions. Despite this, oscilloscopes are still best suited for time domain measurements, while signal analyzers are the most ideal frequency domain measurement tools.

　　In the frequency domain, if the signal covers more than one frequency, the spectrum analyzer will divide it into spectrums according to the frequency and display the signal level in each frequency. At this time, there are many advantages to using frequency domain measurement technology. For example, the spectrum analyzer can clearly distinguish signal information that the oscilloscope cannot identify. In addition, when measuring signals with a spectrum analyzer, the user can narrow the measurement bandwidth to greatly reduce noise. Since many systems now operate in the frequency domain, the instrument must be able to analyze the signal in the frequency domain to avoid interference from adjacent channel frequencies.

　　When performing frequency domain measurements, engineers only need a spectrum analyzer to easily measure the signal's frequency, power, harmonic content, modulation, surges, and noise. After completing the above measurements, engineers can confirm total harmonic distortion, occupied bandwidth, signal stability, output power, intermodulation distortion, power bandwidth, carrier-to-noise ratio, and various other measurement results.

　　Fast Fourier transform (FFT) analyzers or swept-tuned analyzers are ideal tools for performing frequency domain measurements (or spectrum analysis). FFT analyzers capture a time domain signal and use digital sampling technology to convert the signal into a digital signal. They then perform the necessary mathematical operations to convert it into a frequency domain signal and finally display the spectrum distribution diagram on the screen. In addition, FFT analyzers provide real-time signal display capabilities, so they can capture periodic, random, and transient signals, and measure the phase and magnitude of the signal. In contrast, swept-tuned analyzers can sweep the entire frequency range that engineers are eager to observe in order to view signals at all frequencies. In this way, engineers can perform measurements in a wider dynamic range and frequency range. Swept-tuned analyzers are engineers' favorite and most widely used frequency domain measurement tools.

　　Whether FFT analyzers or swept tuned analyzers, they can be used for a wide variety of measurement applications such as spectrum monitoring, clutter emission, scalar network analysis, and electromagnetic interference to measure frequency, power modulation, distortion, and noise. These analyzers support a frequency range of 3 Hz to over 325 Hz, with a dynamic range of -172 dBm to +30 dBm.

         Dissecting the Internals of a Spectrum Analyzer

　　To understand the working principle of a spectrum analyzer, we need to analyze its internal hardware structure. Figure 1 shows the main components of a traditional swept tuned analyzer. As we will see later in this article, modern signal analyzers have completely replaced the analog hardware components with digital circuits, especially the intermediate frequency and baseband parts. Nevertheless, looking at the block diagram below will help you quickly understand the basic working principle of the analyzer.

　　

Figure 1 Block diagram of a traditional swept spectrum analyzer

　　The analyzer shown above uses a 3-port mixer to shift the input signal from one frequency to another. The mixer sends the input signal to one port and sends the local oscillator (LO) output signal to another port. Since the mixer is a nonlinear element, the frequencies that appear at the output are not present at the input. These frequencies are the original input signal and the addition and subtraction of the two frequencies. This difference frequency signal is also called the IF signal.

　　In addition, the IF filter shown in the figure above is a bandpass filter, which can be used as a "window" to detect signals. Users can change the analysis bandwidth (RBW) directly on the analyzer panel. This analyzer provides a variety of variable RBW settings, so users can obtain the best measurement results under different scanning and signal conditions, and obtain excellent frequency selectivity, signal-to-noise ratio (SNR), and measurement speed. Generally speaking, narrowing the RBW helps to improve selectivity and SNR characteristics, so that more subtle frequency distributions can be observed, but the scanning speed and trajectory update rate will decrease. The optimal RBW setting is closely related to the signal characteristics. [page]

　　The detector converts the analyzer's IF signal to baseband or video signal, which can then be further converted to a digital signal that can be viewed on an LCD screen. By using an envelope detector in conjunction with an analog-to-digital converter (ADC), users can convert the video output signal to a digital signal and display the signal size on the Y-axis of the analyzer display.

　　Users can choose from a variety of detector modes to clearly display the measured signal. When analyzing sine waves, engineers usually use the positive detection mode, which displays the maximum signal at a curve display point over a period of time. This mode is also called a segmented display (display bucket) or bin. In addition, the negative detection mode can display the minimum signal; and the sample detection mode can display the signal size at the midpoint of each bin time interval.
 

　　Normal (Rosenfell) mode is the ideal detection mode if both signal and noise need to be displayed simultaneously, because this "intelligent" detection mode dynamically changes the detection method according to the input signal. If the signal rises and falls during the segment duration, it can be assumed that the signal is noise, and the positive and negative detection modes are used alternately. If the signal keeps rising during the process, it is inferred that it is a normal signal and the positive peak detection mode is used.

　　Users can use average detection and video filtering to smooth the variation of envelope-detected amplitude. Average detection uses all data collected in the bin time interval to perform smoothing. This feature is effective for measuring noise or noise-like signals in digital modulated signals. Engineers often use true root mean square (RMS) detectors to perform power average detection, such as measuring the power of complex signals.

　　The video filter is a low-pass filter placed after the envelope detector and before the analog-to-digital converter. This component determines the bandwidth of the video amplifier and can be used to average or smooth the curve displayed on the screen. By changing the video bandwidth (VBW) setting, you can reduce the peak-to-peak variation of the spectrum analyzer noise, so you can easily find signals that are obscured by noise.

　　You can also use the video averaging and curve averaging functions to smooth the variation of envelope detection amplitude. By using the video averaging function to reduce the cutoff frequency of the video filter, the video system will no longer produce rapid variations with the envelope signal passing through the IF frequency. The smoothness that the analyzer can produce is determined by the ratio of VBW to RBW. When the ratio is below 0.01, it can provide better smoothing effect.

　　The curve averaging function averages two or more frequency scans point by point, and re-averages the new value at each display point with the averaged data. The final displayed curve will gradually merge with the average curve of multiple scans. Curve averaging does not affect the scan time.

        Understanding Spectrum Analyzer Specifications

　　Before using a spectrum analyzer, please understand its specifications to ensure that the instrument can provide the measurement performance you need. After confirming the specifications, you can predict the analyzer's performance under specific measurement conditions and the accuracy of the measurement results. The following introduces the main spectrum analyzer specifications:

　　˙Frequency range

　　The frequency range is the range of frequencies that the analyzer measures. Please make sure that your spectrum analyzer covers the basic frequency range required for the measurement application, and confirm whether the instrument supports high-frequency harmonics or surge signals, or low-frequency fundamental and intermediate frequency (IF) signals.

　　Frequency Accuracy

　　˙Frequency range

　　The frequency range is the range of frequencies that the analyzer measures. Please make sure that your spectrum analyzer covers the basic frequency range required for the measurement application, and confirm whether the instrument supports high-frequency harmonics or surge signals, or low-frequency fundamental and intermediate frequency (IF) signals.

　　Frequency Accuracy

　　˙Resolution

　　The resolution of a spectrum analyzer refers to the ability of the instrument to distinguish two adjacent signals of the same magnitude. The wider the RBW, the harder it is to distinguish two adjacent signals. The bandwidth of the RBW filter determines whether the analyzer can distinguish two equal-amplitude signals. For example, if the filter bandwidth is 3-dB, the interval between the two signals must be greater than or equal to 3-dB.

　　

Figure 2 In this two-tone test, two adjacent signals are separated by 10 kHz.

When RBW=10-kHz, it is not difficult to distinguish two signals of the same size, but the resulting distortion may be masked. If the RBW is 3-kHz and the selectivity is 15:1, this problem is easy to occur. In this case, the RBW required for measurement is 1 kHz, so two signals with a size difference of 60 dB must be separated by at least 30-dB bandwidth to distinguish the smaller signal.

　　Fortunately, the signals that engineers analyze are usually of different sizes. Since both signals trace out the filter shape at the same time, the smaller signal may be obscured by the filter edge of the larger signal. The greater the difference in signal size, the higher the probability of this happening, as shown in Figure 2.

　　RBW selectivity is the filter shape. It and phase noise are important factors in determining whether two adjacent signals of different sizes can be clearly distinguished. The narrower the RBW is set, the higher the resolution of the spectrum analyzer, but this will extend the overall frequency scan time because the RBW filter takes some time to reach full response. Spectrum analyzers with automatic coupled scan time can automatically select the fastest allowed scan time based on the selected frequency span, RBW and VBW. In addition, different spectrum analyzers have different scan speeds.

　　˙Sensitivity

　　The sensitivity of a receiver refers to the ability of the instrument to receive small signals under certain test conditions. All receivers (including spectrum analyzers) have some internally generated noise, and the sensitivity of a spectrum analyzer is expressed in dBm as the displayed average noise level (DANL) when the RBW is set to the minimum.

　　DANL is the noise level of the instrument at a specific bandwidth. If the input signal is lower than the noise level, we cannot measure this tiny signal with a spectrum analyzer. Generally speaking, the sensitivity of a spectrum analyzer is between -135 dBm and -165 dBm. To achieve the best sensitivity, set the RBW to the lowest, perform sufficient averaging, set the RF input attenuation to the lowest, and use a preamplifier. However, increasing the sensitivity of the spectrum analyzer may conflict with other measurement requirements such as reducing distortion or increasing dynamic range.

　　distortion

　　Distortion measurements such as third-order intermodulation and harmonic distortion are commonly used to analyze component characteristics, but it should be noted that the spectrum analyzer itself also generates distortion, which can lead to measurement errors. If the level of distortion inside the spectrum analyzer is comparable to the external distortion of the DUT, the measurement error will increase. In the worst case, the internal distortion may completely overwhelm the distortion of the DUT. Instrument manufacturers can set the distortion level of the spectrum analyzer directly or integrate it with the dynamic range specification.

　　

Figure 3 Second-order distortion increases as the square of the fundamental signal increment, while third-order distortion increases as the cube.
 

The third-order intercept (TOI) is a measure of whether a spectrum analyzer can handle large signals without distortion. Analyzers with higher TOI generally provide the best dynamic range and accuracy for measuring distortion and noise.

　　Before making a measurement, the user must first confirm whether the distortion generated by the analyzer will affect the measurement results. For example, when performing a two-tone test, the distortion products (third-order distortion) of the specified instrument are greater than 50 dB, and the second-order distortion (harmonic distortion) is greater than 40 dB. These values ​​can be used as the minimum specifications required for the analyzer. In order to reduce the measurement error caused by internal distortion, the internal distortion must be much smaller than the test specification. [page]

　　Figure 3 shows the distortion of a nonlinear component. In order to confirm whether the distortion is generated by the analyzer or caused by the DUT, we need to perform an attenuation test. First, attenuate the RF input signal by 10dB. If the size of the distortion product on the screen does not change, then it can be determined that the distortion is generated by the DUT. However, if the size of the signal displayed on the screen changes, then the distortion may be partially generated by the signal analyzer, rather than entirely caused by the DUT.

          Dynamic Range

　　The dynamic range of a spectrum analyzer refers to the difference between the maximum and minimum signals that can be measured in one measurement, so that smaller uncertainties can be measured, as shown in Figure 4. The unit of dynamic range is dB. The dynamic range of an analyzer refers to the range in which signal measurements can be performed reliably. Dynamic range is often misunderstood and misjudged because the instrument's display range, measurement range, noise level, phase noise, and surge response all have a significant impact on dynamic range, as shown in Figure 5.

　

　Figure 4 You can visualize the dynamic range graphically.

This figure shows the signal-to-noise and signal-to-distortion curves in the same dynamic range graph. The dynamic range is maximum when the different curves intersect, i.e. the internally generated distortion level is equal to the displayed average noise level (DANL). This point is also the maximum mixer level.

　　To get the best dynamic range, choose an analyzer with the best sensitivity, that is, one with the narrowest RBW, minimal input attenuation, and thorough averaging. Confirm the analyzer's distortion by continuously attenuating the input signal and seeing if the signal level changes. Then, set the attenuator to the lowest setting without changing the signal level.

　　

Figure 5 shows the definition of each dynamic range.

　　This allows engineers to determine which dynamic range is best suited for a particular application.

 

Modern signal analyzer

　　Traditional spectrum analyzers cannot meet the test requirements of modern digital modulation wireless systems, but the emergence of new analyzers meets these requirements.

　　This type of system meets new test requirements such as channel power test, demodulation variable measurement, etc. In addition, the new spectrum analyzer has a full range of functions.

　　It supports a wider range of standards and features, such as excellent amplitude accuracy, span and frequency accuracy, correction factors, and

　　limit lines, test margins, and pass/fail indicators, so the above tests can be performed effectively. Some models even

　　Provides real-time signal capture function to capture all information related to a signal within a period of time.

　　

Figure 6 Basic block diagram of modern spectrum analyzers. This figure is a block diagram of an Agilent X-Series signal analyzer.
 

　　Compared to traditional spectrum analyzers, new analyzers are equipped with various different components, rearrange the functional modules, and move the ADC to the front end of the processing flow, as shown in Figure 6. The fully digital IF filter of the new analyzer can process the signal in a new way, thereby greatly improving the accuracy, dynamic range and speed.

　　Its built-in digital signal processor (DSP) allows the analyzer to measure increasingly complex signals, while also further increasing the dynamic range and accuracy, and speeding up the sweep speed. When a larger dynamic range is required, the signal can be processed through the sweep analysis mode, but if a fast sweep in a narrow bandwidth is required, the signal can be processed using the FFT analysis mode.

　　More importantly, the new spectrum analyzer provides a built-in one-touch power measurement function, including occupied bandwidth, channel power, and adjacent channel power measurement functions, and supports measurement software suitable for different specific applications. Therefore, it can provide one-touch measurements for general test applications, flexibly perform digital modulation analysis, and provide power and digital modulation measurement functions to meet the measurement needs of wireless communication applications.

　　These new models also have enhanced display capabilities, such as spectrograms that analyze the spectrum of signals that vary over time, and trace zooms that allow users to easily zoom in on trace data. I/Q baseband inputs help bridge the gap between baseband and RF signals, as shown in Figure 7. In addition, new spectrum analyzers offer wideband signal analysis capabilities that are ideal for high-speed aerospace and defense, emerging communications, and cellular mobile communications applications with bandwidths up to several hundred MHz.

　　

Figure 7 Agilent PXA and MXA analyzers offer optional I/Q baseband inputs and standard 500 MSa deep acquisition memory

　　Conclusion

　　Signal analyzers are excellent tools for measuring and analyzing the characteristics of various components and systems. To make good use of signal analyzers to accurately measure signals and properly interpret and analyze the measurement results, you need to have a basic understanding of their operating principles and characteristics.