Topic: BusinessIndustry

Last updated: March 26, 2019

1 . Introduction of spectrum analyzer?
Spectrum analyzer is a measuring instrument which is use to displays an electrical signal according to its frequency .
Each frequency component contained in the input signal is displayed in the analyzer as a signal level corresponding to that frequency.
If you are designing, product, or doing field service/repair of electrical devices or systems, we want a tool that will help us to analyze the electrical signals that are passing through or being transmitted by your system or device. By analyzing the characteristics of the signal once its gone through the whole device/system, you can find the performance, find problems, troubleshoot, etc.
How do we measure these electrical signals in order to see what happens to them as they pass through our device/system and therefore verify the performance? We need a passive receiver, meaning it doesn’t do anything to the signal -it just displays it in a way that makes it easy to analyze the signal. This is called a spectrum analyzer. Spectrum analyzers usually display raw, unprocessed signal information such as voltage, power, period, wave shape, sidebands, and frequency. They can provide you with a clear and precise window into the frequency spectrum. 1
Depending upon the application, a signal could have several different characteristics. For example, in communications, in order to send information such as your voice or data, it must be modulated onto a higher frequency carrier. A modulated signal will have specific characteristics depending on the type of modulation used. When testing non-linear devices such as amplifiers or mixers, it is important to understand how these create distortion products and what these distortion products look like. Understanding the characteristics of noise and how a noise signal looks compared to other types of signals can also help you in analyzing your device/system.
Understanding the important aspects of a spectrum analyzer for measuring all of these types of signals will help you make more accurate measurements and give you confidence that you are interpreting the results correctly.
The electronics industry uses spectrum analyzers to examine the frequency spectrum of radio frequency (RF) and audio signals. These devices display the individual elements of these signals, as well as the performance of the circuits producing them. Through the use of spectrum analyzers, organizations can determine what modifications may be needed to reduce interference and thus improve the performance of Wi-Fi systems and wireless routers.
Today, as spectrum analysis software and digital or spectrum analyzer app offerings have become more common, more analyzers are able to do analogy -to-digital conversion and sample a significant input signal and frequency range. A modern spectrum analyzer may be able to show displayed average noise level, calculating the average noise level detected by the device. These detectors are typically capable of sample detection, peak detection or average detection. 3

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2.Types of Tests Made

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Modulation, distortion, and noise are the most common spectrum analyzer measurements
It’s important to make sure your system is working properly confirms by Measuring the quality of the modulation and that the information is being transmitted properly. Understanding the spectral content is important, especially there is very limited bandwidth in the communication. The amount of power being transmitted (for example, to overcome the channel impairments in wireless systems) is another key measurement in communications. The example of common modulation measurements are Tests such as modulation degree, sideband amplitude, modulation quality, occupied bandwidth. 1
In communications, both the receiver and transmitter are critical in the measuring distortion. Excessive harmonic distortion at the output of a transmitter can interfere with other communication bands. The pre-amplification stages in a receiver must be free of inter modulation distortion to prevent signal crosstalk. An example is the inter modulation of cable TV carriers that moves down the trunk of the distribution system and distorts other channels on the same cable. Inter modulation, harmonics, and spurious emissions are including in the common distortions measurements.
The signal you want to measure the noise often. Any active circuit or device/system will generate noise. Tests such as noise figure and signal-to-noise ratio (SNR) are important for characterizing the performance of a device and its contribution to overall system noise.
If we want to understand the operation of the spectrum analyzer and the spectrum analyzer performance required for your specific measurement and test specifications it is important for all of these spectrum analyser measurements. This will help you choose the right analyzer for your application as well as get the most out of it.2
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3. Measurement Categories

Traditionally, when you want to look at an electrical signal, you use an oscilloscope to see how the signal varies with time. This is very important information; however, it doesn’t give you the full picture. To fully understand the performance of your device/system, you will also want to analyze the signal(s) in the frequency-domain. This is a graphical representation of the signal’s amplitude as a function of frequency The spectrum analyzer is to the frequency domain as the oscilloscope is to the time domain. (It is important to note that spectrum analyzers can also be used in the fixed-tune mode (zero span) to provide time-domain measurement capability much like that of an oscilloscope.)
The figure shows a signal in both the time and the frequency domains. In the time domain, all frequency components of the signal are summed together and displayed. In the frequency domain, complex signals (that is, signals composed of more than one frequency) are separated into their frequency components, and the level at each frequency is displayed.2
Frequency domain measurements have several distinct advantages. For example, let’s say you’re looking at a signal on an oscilloscope that appears to be a pure sine wave. A pure sine wave has no harmonic distortion. If you look at the signal on a spectrum analyzer, you may find that your signal is actually made up of several frequencies. What was not discernible on the oscilloscope becomes very apparent on the spectrum analyzer.
Some structures are inherently frequency area orientated. For example, many telecommunications systems use what is referred to as Frequency Division Multiple Access (FDMA) or Frequency Division Multiplexing (FDM). In these structures, one of a kind users are assigned extraordinary frequencies for transmitting and receiving, together with with a cellular smartphone. Radio stations also use FDM, with every station in a given geographical location occupying a specific frequency band. These forms of systems ought to be analyzed within the frequency domain so as to make certain that no person is meddling with users/radio stations on neigh uninteresting frequencies. How measuring with a frequency domain analyzer can greatly lessen the amount of noise present within the dimension because of its capacity to slim the size bandwidth . 3
From this view of the spectrum, measurements of frequency, power, harmonic content, modulation, spurs, and noise can easily be made. Given the capability to measure these quantities, if we use the spectrum analyzer we can determine the total harmonic distortion, occupied bandwidth, signal stability, output power, inter modulation distortion, power bandwidth, carrier-to-noise ratio, and a host of other measurements.
The different between Oscilloscope waveforms and Spectrum analyzer waveforms
Oscilloscope waveforms vs Oscilloscope waveforms wave forms

4.Principals of a Spectrum Analyzer

The important components in a spectrum analyzer are the ATT, mixer, IF (Intermediate Frequency) gain, IF filter, detector, video filter, local oscillator, sweep generator, and CPU. Let’s we can understand about these components in the spectrum analyzer clearly. 4

5. Characteristics

5. 1. Suitable Input Level
When the signal and local oscillator are connected with the mixer input, the suitable input level is the distortion level specification that doesn’t influence the measurement. The level in connection between the input signal and the distortion is specified at the mixer input level, not at the input connector. Therefore, the RF attenuator attenuates the input signal to a suitable mixer input level. 1

5. 2.Maximum Input Level
The maximum input level prevents damage to the input circuit. It is depend on the input levels to the Attenuator and Mixer. A mixer is a three-port device the mixer can converts a signal from one frequency to another frequency (sometimes called a frequency translation device).
We can apply the input signal to one input port, and the Local Oscillator signal to the other port. According to the definition, mixer is a non-linear device, which means that there will be frequencies at the output that were not present at the input. 4

The original input signals are output frequencies that will be produced by the mixer, plus the sum and difference frequencies of these two signals. It is the difference frequency that is of interest in the spectrum analyzer, which we will see briefly.
We call this signal the IF signal, IF signal mean Intermediate Frequency signal. The input is usually connected to the primary mixer with only the input attenuator control, often labelled RF level, between them. Accordingly RF can be applied directly to the mixer with no protection.

It is therefore very important to ensure that the input is not overloaded and damaged. One major and expensive cause of damage on spectrum analysers is the input mixer being blown when the analyser is measuring high power circuits. 3

5.3 Measurement Frequency Range
The measurement frequency range is determined by the centre frequency of the IF filter and the local oscillator frequency range. The IF filter is a band pass filter which is used as the “window” for detecting signals . Its bandwidth is also called the resolution bandwidth (RBW) of the analyzer and can be changed via the front panel of the analyzer.
By giving you a broad range of variable resolution bandwidth settings, the instrument can be optimized for the sweep and signal conditions, letting you trade-off frequency selectivity (the ability to resolve signals), signal-to-noise ratio (SNR), and measurement speed.
We can see from the slide that as RBW is narrowed, selectivity is improved (we are able to resolve the two input signals). This will also often improve SNR. The sweep speed and trace update rate, however, will degrade with narrower RBWs. The optimum RBW setting depends heavily on the characteristics of the signals of interest
Input Signal Freq = Local Signal Freq. – IF Freq.
The mixer uses to mixer the input signal and the local signal. IF filter use to filter the mixer output with centre frequency fc and displayed on the screen.
The IF filter, sometimes labelled as the resolution bandwidth adjusts the resolution of the spectrum analyzer in terms of the frequency. Using a narrow filter is the same as using a narrow resolution bandwidth on a radio receiver.
Choosing a narrow filter bandwidth or resolution on the spectrum analyzer will enable signals to be seen that are close together. Which will also reduce the noise level and enable smaller signals to be seen.2

5.3 Sideband Noise
It appears in the base of the spectrum because of noise in the internal local signal source. Sideband noise shows the signal purity, and the performance of nearby signal analysis is determined by this characteristic. It is specified by how many dB down from the center at an offset of 10kHz (or 100kHz) when the resolution bandwidth (RBW) is narrow enough, and a high purity signal is input .

For the local signal source, the dotted line spectrum is the ideal. However, it actually has sideband noise like the solid line. Masking occurs by the sideband noise when there is a nearby A or B signal and it is not possible to detect it.
5.4 Resolution bandwidth for frequency (RBW)
Two input signals can be seen as two spectrum waveforms only if they exceed the 3dB bandwidth of the IF filter.The 3dB bandwidth of this IF filter is called the resolution bandwidth RBW. Resolution is an important specification when you are trying to measure signals that are close together and want to be able to distinguish them from each other. We saw that the IF filter bandwidth is also known as the resolution bandwidth (RBW). This is because it is the IF filter bandwidth and shape that determines the resolvability between signals.
In addition to filter bandwidth, the selectivity, filter type, residual FM, and noise sidebands are factors to consider in determining useful resolution. We shall examine each of these in turn. 1

Selectivity=(60dB Bandwidth )/(3dB Bandwidth)

e.g. The specification of MS8609A: Selectivity

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