Agilent Spectrum Analysis Basics - Agilent Technologies

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So if we change the resolution bandwidth by a factor of 10, the displayed noise level changes by 10 dB, as shown in Figure 5-2. For continuous wave (CW) signals, we get best signal-to-noise ratio, or best sensitivity, using the minimum resolution bandwidth available in our spectrum analyzer 2 . Figure 5-2. Displayed noise level changes as 10 log(BW 2 /BW 1 ) A spectrum analyzer displays signal plus noise, and a low signal-to-noise ratio makes the signal difficult to distinguish. We noted previously that the video filter can be used to reduce the amplitude fluctuations of noisy signals while at the same time having no effect on constant signals. Figure 5-3 shows how the video filter can improve our ability to discern low-level signals. It should be noted that the video filter does not affect the average noise level and so does not, by this definition, affect the sensitivity of an analyzer. 2. Broadband, pulsed signals can exhibit the opposite behavior, where the SNR increases as the bandwidth gets larger. 3. For the effect of noise on accuracy, see “Dynamic range versus measurement uncertainty” in Chapter 6. In summary, we get best sensitivity for narrowband signals by selecting the minimum resolution bandwidth and minimum input attenuation. These settings give us best signal-to-noise ratio. We can also select minimum video bandwidth to help us see a signal at or close to the noise level 3 . Of course, selecting narrow resolution and video bandwidths does lengthen the sweep time. Figure 5-3. Video filtering makes low-level signals more discernable 60
Noise figure Many receiver manufacturers specify the performance of their receivers in terms of noise figure, rather than sensitivity. As we shall see, the two can be equated. A spectrum analyzer is a receiver, and we shall examine noise figure on the basis of a sinusoidal input. Noise figure can be defined as the degradation of signal-to-noise ratio as a signal passes through a device, a spectrum analyzer in our case. We can express noise figure as: F = S i /N i S o /N o where F= noise figure as power ratio (also known as noise factor) S i = input signal power N i = true input noise power S o = output signal power N o = output noise power If we examine this expression, we can simplify it for our spectrum analyzer. First of all, the output signal is the input signal times the gain of the analyzer. Second, the gain of our analyzer is unity because the signal level at the output (indicated on the display) is the same as the level at the input (input connector). So our expression, after substitution, cancellation, and rearrangement, becomes: F = N o /N i This expression tells us that all we need to do to determine the noise figure is compare the noise level as read on the display to the true (not the effective) noise level at the input connector. Noise figure is usually expressed in terms of dB, or: NF = 10 log(F) = 10 log(N o ) – 10 log(N i ). We use the true noise level at the input, rather than the effective noise level, because our input signal-to-noise ratio was based on the true noise. As we saw earlier, when the input is terminated in 50 ohms, the kTB noise level at room temperature in a 1 Hz bandwidth is –174 dBm. We know that the displayed level of noise on the analyzer changes with bandwidth. So all we need to do to determine the noise figure of our spectrum analyzer is to measure the noise power in some bandwidth, calculate the noise power that we would have measured in a 1 Hz bandwidth using 10 log(BW 2 /BW 1 ), and compare that to –174 dBm. For example, if we measured –110 dBm in a 10 kHz resolution bandwidth, we would get: NF = [measured noise in dBm] – 10 log(RBW/1) – kTB B=1 Hz –110 dBm –10 log(10,000/1) – (–174 dBm) –110 – 40 + 174 24 dB 4. This may not always be precisely true for a given analyzer because of the way resolution bandwidth filter sections and gain are distributed in the IF chain. Noise figure is independent of bandwidth 4 . Had we selected a different resolution bandwidth, our results would have been exactly the same. For example, had we chosen a 1 kHz resolution bandwidth, the measured noise would have been –120 dBm and 10 log(RBW/1) would have been 30. Combining all terms would have given –120 – 30 + 174 = 24 dB, the same noise figure as above. 61
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Agilent Spectrum Analysis Basics Ap
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Table of Contents — continued Cha
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Some measurements require that we p
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Figure 1-3. Harmonic distortion tes
Page 9 and 10: While we have defined spectrum anal
Page 11 and 12: Since the output of a spectrum anal
Page 13 and 14: We need to pick an LO frequency and
Page 15 and 16: To separate closely spaced signals
Page 17 and 18: Agilent data sheets describe the ab
Page 19 and 20: This allows us to calculate the fil
Page 21 and 22: Some modern spectrum analyzers allo
Page 23 and 24: On the other hand, the rise time of
Page 25 and 26: The width of the resolution (IF) fi
Page 27 and 28: The “bucket” concept is importa
Page 29 and 30: Peak (positive) detection One way t
Page 31 and 32: The normal detection algorithm: If
Page 33 and 34: EMI detectors: average and quasi-pe
Page 35 and 36: The effect is most noticeable in me
Page 37 and 38: Thus, the display gradually converg
Page 39 and 40: In some cases, time-gating capabili
Page 41 and 42: Timeslots 0 1 2 3 4 5 6 7 Timeslots
Page 43 and 44: Gated sweep Gated sweep, sometimes
Page 45 and 46: In Chapter 2, we did a filter skirt
Page 47 and 48: Custom signal processing IC Turning
Page 49 and 50: Chapter 4 Amplitude and Frequency A
Page 51 and 52: Following the input filter are the
Page 53 and 54: Improving overall uncertainty When
Page 55 and 56: Examples Let’s look at some ampli
Page 57 and 58: In a factory setting, there is ofte
Page 59: Because the input attenuator has no
Page 63 and 64: Using these expressions, we’ll se
Page 65 and 66: Let’s first test the two previous
Page 67 and 68: This is the 2.5 dB factor that we a
Page 69 and 70: When we add a preamplifier to our a
Page 71 and 72: 2D dB 3D dB 3D dB 3D dB With a cons
Page 73 and 74: We can construct a similar line for
Page 75 and 76: Figure 6-2 shows the dynamic range
Page 77 and 78: Dynamic range versus measurement un
Page 79 and 80: Let’s see what happened to our dy
Page 81 and 82: The range of the log amplifier can
Page 83 and 84: Chapter 7 Extending the Frequency R
Page 85 and 86: Next let’s see to what extent har
Page 87 and 88: In examining Figure 7-5, we find so
Page 89 and 90: Can we conclude from this discussio
Page 91 and 92: Amplitude calibration So far, we ha
Page 93 and 94: From the graph, we see that a -10 d
Page 95 and 96: Pluses and minuses of preselection
Page 97 and 98: Table 7-1 shows the harmonic mixing
Page 99 and 100: Figure 7-15. Which ones are the rea
Page 101 and 102: Figure 7-18. The image suppress fun
Page 103 and 104: Other examples of built-in measurem
Page 105 and 106: Digital modulation analysis The com
Page 107 and 108: Data transfer and remote instrument
Page 109 and 110: Summary The objective of this appli
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Delta marker: A mode in which a fix
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Frequency stability: A general phra
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LO feedthrough: The response on the
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Residual responses: Discrete respon
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Video: In a spectrum analyzer, a te
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