The set of RF Budget (RFB) measurements within the Visual System Simulator (VSS) of the AWR Design Environment (AWRDE) is well suited for analyzing RF systems for items such as gain, noise figure, third-order intermodulation distortion, gain compression, etc. Using measurements of this nature is often termed cascade analysis. There are several RFB measurements that assist in the cascade analysis and with these tools, optimizing the system for best noise figure, distortion or both simultaneously can be achieved. This article focuses on using VSS measurements to optimize the RF chain for best cascade performance in terms of noise figure, third-order intercept or a combination of the two.
There are many cascade analysis tools available, in fact the basic algorithms of a cascade analysis are simple enough to implement in a spread sheet. One may ask, what advantages does VSS offer to cascade analysis that cannot be accomplished with far simpler tools? Here are a few:
- VSS can simulate impedance mismatch between elements. With the delay of transmission lines modeled in, amplitude ripple vs. frequency is something VSS is capable of measuring.
- Gain compression is taken into account for the active stages. Gain, noise and distortion are all affected as an active stage is driven into gain compression. Normally simpler tools or spread sheet equations only use linear, non compressed gain in these computations.
- Performance in the presence of blocker signals. Out of band signals that can occur in an over-the-air RF system can be added.
Optimizing an RF chain is very application specific. The receiver designer will most likely be primarily interested in system sensitivity which is highly related to noise figure. The transmitter designer, on the other hand, may be more interested in spectral emissions and excess noise. Both receiver and transmitter designs may need to optimize for modulation metrics such as EVM or ACPR. What ties many of the RF chain’s figures of merit is noise and third-order intercept (IP3) which can be determined from an analysis where a CW signal is the stimulus. So the supposition is that by optimizing the RF system for noise figure and IP3, other related metrics will also be naturally optimized in the process.
So why concentrate only on noise and IP3? One reason is that most components available such as amplifiers, mixers, etc. are only specified in terms of CW performance. Noise figure and IP3 are almost uniformly specified in such components whereas ACPR and EVM are not commonly specified. Another reasons for the primary reliance on noise figure and IP3 is that these are not modulation format specific and one can optimize for the general CW case and follow up with the modulation format specific performance metrics.
Other cascade performance items to consider in addition to noise and IP3 are gain, power and gain compression. The relation between component gain compression, which is expressed as P1 dB, is especially important in that as the headroom between signal power and P1 dB shrinks, distortion especially increases. Also gain starts dropping which can result in unexpected cascade performance.
Optimization of a system for noise figure, IP3, P1 dB, etc. can be easily achieved with AWRDE’s optimizer. Set some goals and let the optimizer choose various items such as fixed attenuation values , amplifier gain, etc. Optimization is convenient tool to use, however, it is very instructive to know which components have the most influence on system performance. With this knowledge the designer can concentrate on the hopefully few components that most affect performance. This can lead to a more efficient design (possibly fewer devices such as amplifiers required) or it can lead to the designer being able to focus attention on the components that matter the most to meeting system performance requirements. Visualization tools, meaning viewing the same performance data but in a different manner, can lead to further insights to quickly diagnose which components most affect system performance.
This article first covers some theory on noise and third-order intermodulation in a cascaded system. Next, the tools within VSS that help in cascade analysis are discussed. Finally, tools outside of AWRDE that work in conjunction with VSS that show further visualization techniques are discussed.
Note on nomenclature: lower case variables designate linear terms, upper case variables designate log scale terms in dB.
Noise Figure Theory
Noise figure is the dB representation of noise factor, f. This relationship between the two is: NF = 10*log10(f). Noise factor by definition is the ratio of input signal-to-noise ratio to output signal-to-noise ratio of a two port device:
For a device whose input and output impedances are matched to the system’s characteristic impedance, Si is the available signal power from the source and So is the available signal power at the output of the device. Available gain is the ratio of output signal power to input signal power, or g = so/si . Input noise, ni, is the available noise power delivered from a resistance equal to the characteristic impedance of the system. This value is kTB where k is Boltzmann’s constant, T is temperature which by definition is 290 deg K and B is the measurement bandwidth. With a 1 Hz measurement bandwidth, the input noise power is approximately -174 dBm/Hz. Now noise factor can be simplified to:
For cascade modelling purposes it is instructive to consider a device noise model as:
Treat the device as noiseless and include the device noise power adding to the system noise at the input of the noiseless device. Another useful graphical representation is shown here:
The device noise, nD, is added to input noise, ni at the input port of the noiseless device. Both the signal and the combined input noise are amplified by gain, G. So now the noise factor equation can be stated in terms of device noise:
The same noise model can be used for devices with loss:
One observation is that when the device impedance is matched to the system’s characteristic impedance then the output noise of the lossy device is kTB given that the input noise is also kTB. So under these conditions, the noise factor is given by:
Noise figure of a mixer requires some extra consideration given that these devices contain more than two ports and noise figure is by definition constrained to two ports. An article on noise figure of a mixer can be found with this link.
The device noise, nD, can be derived from the specified Noise Figure of active devices such as amplifiers. For lossy devices, the noise figure of the device is the loss of the device. Device noise calculation is as follow:
A graphical analysis easily shows how to perform a cascaded system noise figure calculation:
System noise figure is given by:
The last term is the familiar Friis formula for noise factor.
Third-Order Intermodulation Theory
There are a few different distortion products that could be tracked for optimizing a RF system cascade, however it is the third-order intermodulation distortion that is most often analyzed. One chief reason is that the distortion products fall too close to the fundamental signal to be removed by filters. Unlike harmonic distortion where the distortion product in most situations is far enough away in frequency that filtering can be used to attenuator this type of distortion.
Third-order intermodulation, IM3, is traditionally measured by applying two equal amplitude fundamental tones to the input of the system. Non-linearities in primarily the active devices in the system will create the IM3 distortion products as shown:
The frequency difference between the two fundamental tones is also the frequency difference between the distortion products and their closest fundamental tone. The right figure above shows the third order relationship: if the tone powers change by 1 dB, the distortion product amplitudes change by 3 dB.
Plotting the fundamental and distortion power levels on a graph of output power vs. input power gives the concept of third-order intercept point, IP3:
Extrapolating the fundamental and IM3 power curves past their gain compression levels shows that at a certain power level these two curve intersect. Projecting this point onto the input power axis is the input third-order intercept point (IIP3) and likewise projecting this intersection point onto the output power axis is the output third-order intercept point (OIP3).
Spurious-free dynamic range is defined to be the signal-to-distortion value when the distortion product power levels equal the noise floor. This is depicted in the above graph.
Applying basic geometry to the above graphs relates IP3 to fundamental and IM3 distortion power levels:
The relation between IIP3 and OIP3 is the gain:
Calculating the cascaded IM3 distortion in an RF system is best illustrated with the following diagram:
Consider the fundamental and IM3 distortion at point A (output of the first amplifier):
Fundamental Power: SA [dBm] = Si [dBm] + G1 [dB]
IM3 Power is due to the OIP3 specification of the first amplifier:
IM3A [dBm] = 3*SA [dBm] – 2*OIP31 [dBm]
Now consider the fundamental and IM3 distortion at point B (output of the second amplifier):
Fundamental Power: SB [dBm] = Si [dBm] + G1 [dB] + G2 [dB]
IM3 Power from point A:
IM3B1 [dBm] = IM3A [dBm] + G2 [dB]
IM3 Power due to Amplifier 2:
IM3B2 [dBm] = 3*SB [dBm] – 2*OIP32 [dBm]
Total IM3 Power:
Notice how the IM3 products at the output of the second amplifier combine. The assumption is that over the bandwidth of the fundamental tones and the IM3 products that the group delay ripple is relatively flat. With this assumption the distortion products combine in a coherent manner. For more information on incoherent and coherent signal addition, please refer to this link.
System output IP3 can now be computed as:
System input IP3 can be computed as:
Digitally Modulated Signals
Noise figure and third-order intermodulation are directly applicable to digitally modulated signals. The spectrum of a digitally modulated signal can show signs of spectral regrowth and/or noise limited performance as shown here:
Spectral regrowth is primarily a third-order intermodulation distortion phenomenon and appears in what is known as the adjacent channel in the frequency spectrum. The following diagram shows how IM3 contributes to spectral regrowth:
Divide the channel into frequency segments and represent the average power of each segment by a CW tone of the same power as that segment. All possible combinations of any two tones in the model will show intermodulation distortion products falling into the adjacent channel. From this, one can conclude that spectral regrowth is directly related to IP3 and that by optimizing the RF chain for lower IM3 distortion this will result in lower level of spectral regrowth.
System noise as viewed in the spectrum of the digitally modulated signal can also impede the adjacent channel power ratio (ACPR) in the form of signal-to-noise ratio (SNR). Optimizing for low noise figure can result in higher SNR which in turn improves ACPR. A situation may exist where simultaneous optimization of IM3 and noise is required to meet spectral regrowth performance in the adjacent channel and meet SNR requirements in the alternate channel.
Error vector magnitude, EVM, is another metric that can be impacted by both system noise and IM3. When viewed on a constellation diagram, the line between the reference point and the measured (or simulated) point is the error vector.
The constellations below show that of a 16 QAM modulation. The left plot shows the effect of noise on the constellation points and the right plot shows the effect of IM3 distortion on the constellation points.
Again, optimizing the system for noise and IM3 performance will also help in achieving EVM performance goals.
Gain compression, or P1 dB, is illustrated on a plot of power out vs. power in for either a device or an entire system.
The input power and output power where the gain drops by 1 dB is considered the input P1 dB and output P1 dB powers respectively. In VSS the relationship between OP1 dB and IP1 dB is: OP1 dB = IP1 dB – Uncompressed Gain – 1 dB. The following plot shows an example:
The blue trace is the normalized gain, which is 0 dB when measured at power levels where no gain compression takes place. The green trace is the input power to a device and the red trace is the output power of an amplifier with 10 dB gain. Output gain compression occurs at an output power level of +10 dBm. The input gain compression is output gain compression – uncompressed gain of 10 dB – 1 = +1 dBm input power.
VSS Measurements for Cascade Analysis
Within VSS, the RF Budget (RFB) Analysis suite of measurements uses a CW source for cascade analysis of items such as gain, distortion, noise, power, etc. Since there is a strong relationship between CW noise and distortion performance and modulation metrics, RFB is a good choice for optimizing the CW system followed up with verification with a modulation stimulus. One note is that even though the stimulus for RFB is a CW source, two-tone intermodulation measurements such as IP3 and IP2 are still calculated using the assumption that the CW source is a two-tone signal whose tone powers are equal. The total power of the two tones combined is equal to the single tone CW power set by the power parameter of the source.
The RFB measurements can be displayed where the x-axis is a swept parameter such as frequency, power or a parameter setting using the SWPVAR element in the system diagram. This plot shows frequency as the x-axis for the node power (P_node) measurement of an RF system:
However, the more insightful benefit of using RFB is the ability to display the x-axis as the stages in the RF system as shown here for the same P_node measurement at a single frequency of 15 GHz:
Optimizing the cascade depends heavily on knowledge of RF performance at each stage in the system. Knowing which components or set of components in a system is most contributing to noise or distortion is key to optimizing the system for dynamic range.
Now many of the measurements within RFB that assist in dynamic range measurement and optimization will be examined. The following is not an exhaustive list of all the measurements within the RFB suite.
Gain may be a secondary consideration for dynamic range optimization, however it might be a good diagnostic to quickly ensure each component’s approximate gain looks reasonable. There are a few different gain definitions to choose from including available gain, operating point power gain, linear gain and transducer gain. Since the default setting for noise figure measurement uses transducer gain in its calculation, this might be your best choice. This is the C_GT measurement.
Power is most useful for tracking any individual component’s relative difference between power and its P1 DB compression level. P_node is the measurement shows the average power at the output of each component in the system. The blue trace in the following plots is the P_node measurement.
Gain compression measurement, C_P1DB, can be displayed as being referred to the system input or the output of each device, that is either IP1 dB or OP1 dB. Cumulative P1 dB tracks the system P1 dB as the signal progresses through the RF chain. In the above plot this cumulative P1 dB trace shows the system’s output P1 dB gain compression power at the output of each device. Block contribution is another method of plotting C_P1DB, this is the red trace in the above plot.
The following plot shows input C_P1DB setting. In this case input P1 dB is referred to the input port of the system. In other words it is the cumulative input gain compression at each device minus the gain that is in front of that device.
Headroom is the dB difference between each device’s P1 DB value and the node power. C_HDRM is a measurement in the RFB suite that computes this headroom value. More information on this measurement can be found at this link.
The RFB noise measurements are noise power spectral density (NO_node), Noise figure (C_NF) and signal-to-noise ratio (C_SNR). This plot shows noise power spectral density (blue trace) and noise figure (red and green traces):
The red trace is C_NF with the cumulative noise figure setting and the green trace is the C_NF measurement with block contribution setting. The block contribution is how much the noise figure increases for each individual stage.
For the C_SNR measurement, shown below, the noise is measured in a noise bandwidth that is selectable.
Third-order Intermodulation Distortion
Regarding third-order intermodulation, the relevant measurements are input IP3, output IP3 and third-order intermodulation distortion level (IM3). IP3 can either be displayed as cumulative performance or block contribution. Block contribution is the device’s input or output IP3 value. This graph shows input IP3 and IM3:
Blue trace is the cumulative input IP3, red trace is block contribution input IP3 and the green trace is the IM3 level.
This next graph is for output IP3:
Using the Optimizer
The natural thought for optimizing the RF system for any set of desired cascaded performance goals is to use AWRDE's optimizer. Setting up an using the optimizer for VSS simulations is much like that for circuit simulations. Details on using the optimizer and setting optimization variables can be found at this link.
Here is an example of an RF chain to be used in an optimization:
The RF Attenuators between active elements are set so that their attenuation values are the optimization variables. This graph shows output power, input IP3 and NF all setup for optimization:
In most cases optimizing the output power or perhaps gain is a requirement. Without optimizing for a set power or gain, optimizing for only other performance goals such as noise figure or IP3 alone results in an unrealistic cascade.
One is not limited to RFB CW cascade optimization. Time domain analysis such as ACPR, EVM, BER can also be configured for optimization. This is a spectrum of an RF chain using a QAM modulated stimulus. Spectral regrowth is apparent in the adjacent channel whereas the alternate channel is SNR limited in performance metric:
ACPR is a standard measurement for VSS and can be configured to measure over a channel center frequency and bandwidth of the user’s choosing. Optimization goals can be set for total power and ACPR as shown in the following plot:
Cascade Optimization Strategies Using VSS
As demonstrated above, VSS through the RFB suite of measurements has many options for assessing cascade performance. Each situation is unique in what performance metric or set of metrics is of most importance or when more than one metric is to be optimized, what is the relative importance of each metric.
In general, one should always remember to monitor power levels. It is easy to start optimizing for noise or distortion only to realize that one of the stages is being overdriven. One key difference between using spreadsheet analysis and VSS is that VSS will be applying compressed gain for the stages that have power levels approaching or exceeding their P1 dB specifications. Noise and distortion calculations all take into account the true gain of each device and so if some of these parameters do not appear to be matching hand calculations or spread sheet calculations, check to make sure none of the stages are in compression using the P_node and C_P1DB measurements as demonstrated above in the power plot discussion.
Another consideration is whether the RF system is intended to be a receiver or transmitter. Receiver designers tend to be more interested in input performance metrics such as noise figure in IIP3. Transmitter designers may want to focus more on output performance metrics like OIP3 and excess noise, that is output noise above kTB.
There is definitely benefit of using the optimizer to achieve overall RF chain cascade metrics. However caution is warranted in that one can lose insight into which stages are contributing most to a particular performance outcome. Using RFB plots where the stages are the x-axis can help pinpoint which stages are the high contributors to adverse noise or distortion performance. A strategy that might be advised is to first manually tune the RF chain first to get some of the performance metric close or to identify the high contributors before attempting an optimization.
Alternate Visualization Tools for Cascade Analysis
As demonstrated above, VSS itself has a large selection of measurements in which to assess and optimize an RF chain’s cascade performance. But as in any engineering problem, being able to visualize the same data in a different format can give extra insights. Although VSS has many measurements targeted to RF cascade analysis, each situation is somewhat unique and different designers may have different ways in which they want to visualize the simulation data. For these opportunities scripting can be used to take the VSS measurement data and tailor the data in ways that makes most sense for the particular situation or to the designers preference.
For optimizing the RF chain for dynamic range, a couple example insights might be: one, the ability to identify the higher contributors to either noise or distortion and two, the sensitivity of each component’s noise or distortion on the overall system’s noise or distortion performance. The following section will discuss a couple of different noise and IP3 distortion visualization techniques that will quickly allow the designer to identify the component that contribute most to noise and IP3 distortion. Additionally a component noise and distortion sensitivity analysis algorithm will be presented.
Scripting in the Python environment can be used in conjunction with AWRDE. VSS is used to model and measure the RF system. RFB measurement data is then read into the Python script for further processing and graphical presentation. An example script can be found at this link.
Device Noise Relative to System Noise Power
Take a look at the following NF vs stage plot:
If one were to use this graph and try to determine the top three contributors to system noise figure, one might conclude that the elements highlighted in red would be the three highest contributors. But the surprise is that the first amplifier in the chain is a very low contributor and the amplifier highlighted in green is the highest contributor. This demonstrates that cumulative noise figure measurement is not the best visualization for optimizing cascaded noise performance of the RF chain.
One alternate way assessing noise contributors is to plot the device noise for each stage on the same plot as the system noise power density. Such a plot is shown here where the blue trace is the noise power density and the red dots are the device noise amplified by the device gain.
Device noise was derived earlier and it represents the noise of the device added at the input of the device. The important observation is that for elements whose device noise is relatively close to the system’s noise power density, the more influence that device will have on system noise figure. The green highlighted devices show the devices where the device noise is relatively close to the system noise power density at that stage.
Device IM3 Level Relative to System IM3 Level
There are parallels to noise analysis when trying to assess IP3 performance. For instance in this plot of input IP3 one might conclude that the highlighted stages are the biggest contributors to system IP3 performance:
All three highlighted stages show a similar degradation in IP3 performance, however for this particular example the mixer is not a significant contributor to overall system IP3 performance. But, that fact is not apparent in looking at cascaded IP3 alone.
In a similar manner as with noise, device IM3 level can be plotted relative to system IM3 level. This is depicted in the following plot where the blue trace is system IM3 level and the red dots are the IM3 levels produced by each active device alone:
Devices whose IM3 level is relatively close to the system IM3 level have more influence on system IP3 performance. So, this type of plot does show more insight into device IP3 contribution than cascaded IP3 plot alone.
Alternate Method of Analyzing Noise and Distortion Contributors
Device noise plotted along side of system noise power density does give more information on device noise contribution, but it does not give the whole story. Loss stages with high noise contribution are not well represented in plots of device noise and noise power density. For even more accuracy in assessing main noise contributors, a better alternative is to plot excess device noise referred to the input.
Similarly for device IM3 plotted with system IM3, much information is conveyed. However even more insight can be gained by referencing device IM3 to either the input or output.
So far noise and distortion have been considered separately. In some situations noise and distortion will need to be optimized together in order to achieve the best simultaneous noise and distortion performance. Referring device noise and device IM3 to either the input or output allows for simultaneous noise and distortion optimization.
Excess Device Noise Referred To Input
What is excess device noise referred to the input? To help with that question, a formula for calculating overall system noise figure as a function of device noise will be developed. Consider the noise model of a chain of three devices
Earlier device noise was defined as:
Output noise is:
Now system noise factor will defined in terms of device noise:
Excess noise is noise above the system’s input noise, which is kTB. Excess device noise referred to the input is the excess device noise divided by the linear system gain in front of the device. We will designate excess device noise referred to the input with nex .
It follows that system noise factor is:
Noise Figure in terms of excess device noise referred to the input expressed in dB:
So, excess noise referred to the input has been defined and an equation for how this relates to system noise figure has been developed. One can see that noise figure computed in this fashion shows the relative ranking of device excess noise contribution on overall system noise figure. Before showing how to use this information, a similar analysis is required for device IP3.
Device IP3 Referred to the Input
Consider the two active device system as shown where device IP3 are defined as input IP3 values:
In general, third-order intermodulation levels are calculated from signal input power and device IIP3
IM3 = 3P – 2IP3
This last expression computes system input IP3 in terms of device IIP3 referred to the input much like the above noise figure equation in terms of excess device noise referred to the input. And as in the case of system noise figure in terms of device excess noise, system IP3 in terms of device IP3 referred to the input gives a relative ranking of the importance of each device's IP3 performance on overall system IP3 performance.
Dynamic Range Plot
Presenting excess device noise referred to the input and device input IP3 referred to the input on the same graph is a very effective means of determining the devices that contribute most to system noise figure and IP3. Here is an example system showing cumulative input IP3 and Noise figure:
And here is the same system presented on the Dynamic Range plot:
The red dots are the device excess noise referred to the input and the blue diamond are the device input IP3 referred to the input. The higher the excess noise the more the noise figure contribution and the lower the device input IP3 the higher the device contribution. So in the above graph, pushing the red noise dots lower on the y-axis and pushing the blue diamonds higher on the y-axis yields a system with higher dynamic range.
Highlighted here are some of the insights that the dynamic range plot provide:
Significant contributors to both IP3 and noise figure are easily spotted. In this example two devices are high IP3 contributors and two devices are high noise contributors. Cumulative IP3 and NF measurements do not give this kind of rapid insight.
The two highlighted blue diamonds indicate that these two stages are near equal contributors to system IP3 performance. From the cumulative IIP3 graph, one might conclude that only one of these devices is a major contributor. If one were to concentrate on improving only one of these devices for IP3 performance, only modest system improvement would occur. This graph tells the designer that two devices need attention.
The two highlighted excess device noise dots indicate that these two stages are near equal noise contributors. Improving only one device’s noise performance will only go so far in improving overall system noise figure.
Another insight that is easily spotted are the devices with the highest and lowest dynamic ranges. The closer the input referred IP3 and device noise values are to each other, the lower the dynamic range. For instance, the highlighted device above has the lowest dynamic range: that is the worst combination of noise figure and IP3 performance.
In general a balanced system will lead to higher dynamic range as defined by system noise figure minus system input IP3. In a balanced system, the individual device noise figures and input IP3 values are distributed as equally as possible. For instance, using the Dynamic Range plot, the following is an example of a well balanced system:
The first item on the x-axis which is labelled "System" is the system noise figure (red) and system IIP3 (blue). This example shows relatively low noise figure and high IIP3. A bisector line between the system NF and system IIP3 (red dashed line) is drawn. The y-axis value of this bisector line is the average of system NF and system IIP3. Distributing the individual device excess noise referred to the input and device IIP3 referred to the input such that the bisector line goes through the midpoint of these two values leads to a balanced system.
The same system, but with the attenuator settings at different values is shown here:
This system configuration shows higher system noise figure and lower system IIP3, that is worse dynamic range. See how the individual noise and IP3 referred to the input do not evenly straddle the bisector line. This is an indication of a system that is not very well balanced.
It is recognized that achieving a well balanced system may not be possible due to system constraints such as mandatory choice of active devices. So balancing a system can be thought of as a goal in these cases. The closer to a balanced system, the higher the dynamic range. Adding fixed attenuators between active stages is one way of distributing the gain to achieve a balanced system.
Dynamic Range Plot For Transmitters
Noise figure and input IP3 are performance measures more important to receiver designs. For transmitter designs, output IP3 and output noise power density are more important. Using a similar development as above, one can choose to define device excess noise referred to the output as being device noise plus system gain from the device to the end of the RF chain. Device output IP3 referred to the system input is similarly device’s output IP3 value plus the system gain that follows the device. A dynamic range plot can be constructed with output referred noise and IP3 as shown:
Very similar insights as with the input referred noise and IP3 can be derived from the output facing measures.
Identifying the top noise and distortion contributors gets one a long ways in optimizing the cascaded performance. One item is sensitivity, that is the change in system noise figure and IP3 as a function of the change in each device noise figure and IP3. Units are dB/dB.
The above equation for computing noise figure as a function of excess device noise can be used. Change each device’s noise figure by a small amount, say 0.1 dB, measure the change in system noise figure and divide by the change used for the device’s noise figure. Change in system IP3 is computed in a similar fashion using the equation for system IP3 as a function of device IIP3 referred to the input.
Here is the sensitivity analysis for an example system:
For the top devices in the noise figure sensitivity list, a 1 dB change in the device’s noise figure would lead to a 0.31 dB change in system noise figure. For the top device in the IP3 sensitivity list, a 1 dBm change in device IP3 leads to a 0.30 dB change in system IP3.
Optimizing an RF system for noise and distortion performance is a very challenging process. With the Visual System Simulator (VSS) that is part of the AWR Design Environment, there are a number of measurements that target cascade analysis. Optimizing for noise figure and IP3 using CW stimulus will, because of their direct correlation, lead to optimization when the stimulus is a digitally modulated signal. With the aid of scripting in conjunction with VSS derived measurements, additional insights in regards to top noise and distortion contributors and device sensitivities are available.