Agilent Spectrum Analysis Basics - Agilent Technologies

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Sometimes third-order performance is given as TOI (third-order intercept). This is the mixer level at which the internally generated third-order distortion would be equal to the fundamental(s), or 0 dBc. This situation cannot be realized in practice because the mixer would be well into saturation. However, from a mathematical standpoint, TOI is a perfectly good data point because we know the slope of the line. So even with TOI as a starting point, we can still determine the degree of internally generated distortion at a given mixer level. We can calculate TOI from data sheet information. Because third-order dynamic range changes 2 dB for every dB change in the level of the fundamental tone(s) at the mixer, we get TOI by subtracting half of the specified dynamic range in dBc from the level of the fundamental(s): TOI = A fund – d/2 where A fund = level of the fundamental in dBm d = difference in dBc between fundamental and distortion Using the values from the previous discussion: TOI = –30 dBm – (–85 dBc)/2 = +12.5 dBm Attenuator test Understanding the distortion graph is important, but we can use a simple test to determine whether displayed distortion components are true input signals or internally generated signals. Change the input attenuator. If the displayed value of the distortion components remains the same, the components are part of the input signal. If the displayed value changes, the distortion components are generated internally or are the sum of external and internally generated signals. We continue changing the attenuator until the displayed distortion does not change and then complete the measurement. Noise There is another constraint on dynamic range, and that is the noise floor of our spectrum analyzer. Going back to our definition of dynamic range as the ratio of the largest to the smallest signal that we can measure, the average noise of our spectrum analyzer puts the limit on the smaller signal. So dynamic range versus noise becomes signal-to-noise ratio in which the signal is the fundamental whose distortion we wish to measure. We can easily plot noise on our dynamic range chart. For example, suppose that the data sheet for our spectrum analyzer specifies a displayed average noise level of –110 dBm in a 10 kHz resolution bandwidth. If our signal fundamental has a level of –40 dBm at the mixer, it is 70 dB above the average noise, so we have a 70 dB signal-to-noise ratio. For every dB that we reduce the signal level at the mixer, we lose 1 dB of signal-to-noise ratio. Our noise curve is a straight line having a slope of –1, as shown in Figure 6-2. If we ignore measurement accuracy considerations for a moment, the best dynamic range will occur at the intersection of the appropriate distortion curve and the noise curve. Figure 6-2 tells us that our maximum dynamic range for second-order distortion is 72.5 dB; for third-order distortion, 81.7 dB. In practice, the intersection of the noise and distortion graphs is not a sharply defined point, because noise adds to the CW-like distortion products, reducing dynamic range by 2 dB when using the log power scale with log scale averaging. 74
Figure 6-2 shows the dynamic range for one resolution bandwidth. We certainly can improve dynamic range by narrowing the resolution bandwidth, but there is not a one-to-one correspondence between the lowered noise floor and the improvement in dynamic range. For second-order distortion, the improvement is one half the change in the noise floor; for third-order distortion, two-thirds the change in the noise floor. See Figure 6-3. 0 TOI SHI –10 –20 –30 3rd order 2nd order –40 –50 Noise (10 kHz BW) (dBc) –60 Noise (1 kHz BW) –70 –80 –90 2nd order dynamic range improvement 3rd order dynamic range improvement –60 –50 –40 –30 –20 –10 0 +10 Mixer level (dBm) Figure 6-3. Reducing resolution bandwidth improves dynamic range 75
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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
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While we have defined spectrum anal
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Since the output of a spectrum anal
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We need to pick an LO frequency and
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To separate closely spaced signals
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Agilent data sheets describe the ab
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This allows us to calculate the fil
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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 and 60: Because the input attenuator has no
Page 61 and 62: Noise figure Many receiver manufact
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: We can construct a similar line for
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
Page 111 and 112: Delta marker: A mode in which a fix
Page 113 and 114: Frequency stability: A general phra
Page 115 and 116: LO feedthrough: The response on the
Page 117 and 118: Residual responses: Discrete respon
Page 119 and 120: Video: In a spectrum analyzer, a te
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Agilent Spectrum Analysis Basics - Agilent Technologies

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