Vector Signal Analyzer Principle

Vector signal analyzer is a commonly used instrument for radar and wireless communication signal analysis.

Analog swept-tuned spectrum analyzers use superheterodyne technology to cover a wide frequency range; from audio, microwave to millimeter-wave frequencies. Fast Fourier transform (FFT) analyzers use digital signal processing (DSP) to provide high-resolution spectrum and network analysis. Today's wideband vector modulated (also known as complex modulation or digital modulation) time-varying signals benefit greatly from FFT analysis and other DSP techniques. VSAs provide fast, high-resolution spectrum measurements, demodulation, and advanced time domain analysis capabilities, and are particularly suitable for characterizing complex signals such as pulses, transients, or modulated signals in communications, video, broadcast, radar, and software radio applications.

Figure 1 shows a simplified VSA block diagram. VSA uses a completely different measurement approach than traditional swept analysis; the analog IF section is replaced by a digital IF section that incorporates FFT and digital signal processing algorithms. Traditional swept-tuned spectrum analysis is an analog system; while VSA is essentially a digital system that uses digital data and mathematical algorithms to perform data analysis. VSA software can receive and analyze digital data from many measurement front ends, allowing you to troubleshoot throughout the entire system block diagram.

Figure 1. The vector signal analysis process requires that the input signal is an analog signal that is digitized and then processed using DSP techniques to provide data output; the FFT algorithm calculates the frequency domain results, and the demodulation algorithm calculates the modulation and code domain results.

An important feature of a VSA is its ability to measure and process complex data, i.e., amplitude and phase information. In fact, it is called "vector signal analysis" precisely because it acquires complex input data, analyzes the complex data, and outputs complex data results that contain amplitude and phase information. Vector modulation analysis performs the basic functions of a measurement receiver. In the next article, "Basics of Vector Modulation Analysis," you will learn about the concepts of vector modulation and detection.

With the appropriate front end, VSAs can cover RF and microwave frequency bands and provide additional modulation domain analysis capabilities. These improvements can be achieved through digital techniques such as analog-to-digital conversion and DSP including digital intermediate frequency (IF) techniques and fast Fourier transform (FFT) analysis.

As the signals to be analyzed become more complex, the latest generation of signal analyzers have transitioned to digital architectures and often have many vector signal analysis and modulation analysis capabilities. Some analyzers digitize the signal at the input of the instrument after amplifying it or performing one or more down-conversions. In most modern analyzers, the phase is retained along with the amplitude information to make true vector measurements. On the other hand, other front ends such as oscilloscopes and logic analyzers digitize the entire signal while also retaining the phase and amplitude information. Whether the VSA is part of a synthetic measurement front end or a separate software running internally or on a computer connected to the front end, its analysis capabilities depend on the processing power of the front end, whether the front end is a dedicated software for comprehensive measurements or a vector analysis measurement of dynamic signals and produces complex data results.

VSAs have distinct advantages over analog swept tuned analysis. One major advantage is that they are better able to measure dynamic signals. Dynamic signals are generally classified into two categories: time-varying or complex modulated. Time-varying signals are signals whose characteristics change during a single measurement sweep (e.g., bursts, thresholds, pulses, or transients). Complex modulated signals cannot be described by simple AM, FM, or PM modulation alone and include most modulation schemes used in digital communications, such as quadrature amplitude modulation (QAM).

Figure 2. Swept tune analysis shows the instantaneous response of a narrowband IF filter to an input signal. Vector analysis uses an FFT to convert a large number of time domain samples into a frequency domain spectrum.

Traditional swept spectrum analysis actually sweeps a narrowband filter through a series of frequencies, measuring one frequency at a time in sequence. This method of sweeping the input is feasible for stable or repetitive signals, but for signals that change during the sweep, the sweep result cannot accurately represent the signal.

Again, this technique only provides scalar (amplitude only) information, although some signal characteristics can be derived through further analysis of the spectral measurements.

The VSA measurement process overcomes the limitations of scanning by taking a "snapshot" or time record of the signal and then processing all frequencies simultaneously to simulate a series of parallel filters. For example, if the input is an instantaneous signal, the entire signal event is captured (meaning that all information about the signal at that moment is captured and digitized); then an FFT operation is performed to obtain the relationship between the "instantaneous" complex spectrum and frequency. This process is performed in real time, so no part of the input signal is lost. For this reason, VSA is sometimes called "dynamic signal analysis" or "real-time signal analysis." However, the ability of the VSA to track rapidly changing signals is not unlimited. It depends on the computing power of the VSA.

Parallel processing brings another potential advantage to high-resolution (narrow resolution bandwidth) measurements: faster measurement time. If you have ever used a swept-tuned spectrum analyzer, you know that narrow resolution bandwidth (RBW) measurements over a small frequency span can be very time-consuming. A swept-tuned analyzer must sweep the frequency point by point slowly enough to allow the analog resolution bandwidth filter to settle. In contrast, a VSA measures the entire frequency span at once. However, due to the effects of the digital filters and DSP, the VSA has similar settling time. Compared to analog filters, the sweep speed of the VSA is mainly limited by the time required for data acquisition and digital processing. However, the settling time of the VSA is usually negligible compared to the settling time of the analog filter. For some narrowband measurements, the VSA can measure 1000 times faster than traditional swept-tuned analysis.

In swept-tuned spectrum analysis, the physical bandwidth of the swept filter limits the frequency resolution. VSAs do not have this limitation. VSAs can resolve signals spaced less than 100 μHz apart. The resolution of a VSA is usually limited by the frequency stability of the signal and measurement front end, as well as the time you want to spend on the measurement. The higher the resolution, the longer it takes to measure the signal (to obtain the required time record length).

Another extremely useful feature is the time capture capability. It allows you to record the actual signal intact and replay it later for various data analysis. The captured signal can be used for various measurements. For example, capture a digital communication transmission signal and then perform both spectrum analysis and vector modulation analysis to measure signal quality or identify signal defects.

Using digital signal processing (DSP) brings other advantages; it can provide measurement analysis in the time domain, frequency domain, modulation domain, and code domain simultaneously. An instrument that combines these capabilities is more valuable and can improve measurement quality. The VSA's FFT analysis allows you to easily and accurately view time and frequency domain data. The DSP provides vector modulation analysis, which includes analog and digital modulation analysis. The analog demodulation algorithm provides AM, FM, and PM demodulation results similar to those of a modulation analyzer, allowing you to see plots of amplitude, frequency, and phase versus time. The digital demodulation algorithm can be applied to a wide range of measurements for many digital communication standards (such as GSM, cdma2000®, WiMAXTM, LTE, etc.), and obtain many useful measurement displays and signal quality data.

It is clear that VSAs offer many important advantages, and when used with the right front end, they can provide even greater advantages. For example, when VSAs are used with traditional analog swept tuned analyzers, they can provide higher frequency coverage and greater dynamic range measurement capabilities; when used with oscilloscopes, they can provide wideband analysis; and when used with logic analyzers, they can probe FPGAs and other digital baseband modules in wireless systems.

As mentioned previously, the VSA is essentially a digital system that uses DSP for FFT spectrum analysis and demodulation algorithms for vector modulation analysis. The FFT is a mathematical algorithm that provides a time-to-frequency domain conversion of time-sampled data. The analog signal must be digitized in the time domain and then the FFT algorithm is performed to calculate the spectrum. Conceptually, the implementation of a VSA is very simple and straightforward: capture the digitized input signal and calculate the measurement result. See Figure 3. In practice, however, many factors must be considered to obtain meaningful and accurate measurement results.

Figure 3. Example of 1 kHz FFT analysis: digitizing the time domain signal and then converting it to the frequency domain using FFT

If you are familiar with FFT analysis, you know that the FFT algorithm makes several assumptions about the signal it is processing. The algorithm does not verify that these assumptions hold for a given input, which can produce invalid results unless the user or the instrument can verify these assumptions.