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The **Bit Error Rate Analysis** app calculates BER as a
function of the energy per bit to noise power spectral density ratio
(*E*_{b}/*N*_{0})
and enables you to analyze BER performance of communications systems.

**Note**

The **Bit Error Rate Analysis** app is designed for
analyzing BERs. For example, if your simulation computes a symbol error rate (SER),
convert the SER to a BER before comparing the simulation results with theoretical
results in the app.

This topic describes the **Bit Error Rate Analysis** app and provides
examples that show how to use the app.

Requirements for Using MATLAB Functions with

**Bit Error Rate Analysis**AppCompute Error Rate Simulation Sweeps Using Bit Error Rate Analysis App

Requirements for Using Simulink Models with

**Bit Error Rate Analysis**App

You can open the **Bit Error Rate Analysis** app by using either
of these options.

MATLAB

^{®}Toolstrip: On the**Apps**tab, under**Signal Processing and Communications**, click**Bit Error Rate Analysis**.MATLAB command prompt: Use the

`bertool`

function. If the app is already open, another instance of the app opens.

The app consists of these three main components: an upper pane, a lower pane, and a separate BER Figure window.

The upper pane of the app is a data set viewer. The data set viewer lists sets of BER data from the current app session along with high level settings and options for showing the data. By default, this data set viewer is empty.

Sets of BER data, generated during the active

**Bit Error Rate Analysis**app session or imported into the session, appear in the data viewer. This figure shows the`simulation0`

BER data set loaded in the data viewer pane.The lower pane of the app has tabs labeled

**Theoretical**and**Monte Carlo**. The tabs correspond to the different methods you can use to generate BER data with the app.**Note**For direct comparisons between theoretical results and simulation results generated when using the

**Bit Error Rate Analysis**app, be sure that your MATLAB function or Simulink^{®}model simulation run from the**Monte Carlo**tab exactly matches the system defined by the parameters in the**Theoretical**tab.For more information, see the Compute Theoretical BERs Using Bit Error Analysis App, Run MATLAB Simulations in Monte Carlo Tab, and Run Simulink Simulations in Monte Carlo Tab sections.

A separate BER Figure window displays the BER data sets that have

**Plot**selected in the data viewer. The BER Figure window does not open until the**Bit Error Rate Analysis**app has at least one data set to display.

The components of the app act as one integrated tool.

If you select a data set in the data viewer, the app reconfigures the tabs to reflect the parameters associated with that data set and highlights the corresponding data in the BER Figure window. This feature is useful if the data viewer displays multiple data sets and if you want to recall the meaning and origin of each data set.

If you select data plotted in the BER Figure window, the app reflects the parameters associated with that data in the app panes and highlights the corresponding data set in the data viewer.

**Note**You cannot click a data point while the app is generating Monte Carlo simulation results. Before selecting data for more information, you must wait until the app generates all of the data points.

If you configure the

**Theoretical**tab in a way that is already reflected in an existing data set, the app highlights that data set in the data viewer. This feature prevents the app from duplicating its computations and entries in the data viewer but still enables the app to show results that you requested.If you close the BER Figure window, you can reopen the figure window by selecting

**BER Figure**from the**Window**menu in the app.If you select options in the data viewer that affect the BER plot, the BER Figure window automatically reflects your selections. Such options relate to data set names, confidence intervals, curve fitting, and the presence or absence of specific data sets in the BER plot.

**Note**

If you want to observe the addition of theoretical data to a plot with Monte Carlo simulation data displayed but do not yet have any data sets in the

**Bit Error Rate Analysis**app, you can follow the workflow described in the Use Theoretical Tab in Bit Error Rate Analysis App section.If you save the BER Figure window using the

**File**menu, the resulting file contains the contents of the window, but not the**Bit Error Rate Analysis**app data that led to the plot. To save an entire**Bit Error Rate Analysis**app session, see the Save Bit Error Rate Analysis app Session section.

You can use the **Bit Error Rate Analysis** app to generate
and analyze theoretical BER data. Theoretical data can be useful for comparison
with your simulation results. However, closed-form BER expressions exist for
only certain kinds of communications systems. For more information, see Analytical Expressions and Notations Used in BER Analysis.

To access app capabilities related to theoretical BER data, follow these steps.

Open the

**Bit Error Rate Analysis**app, and select the**Theoretical**tab.Set the parameters to reflect the communications system performance that you want to analyze.

Click

**Plot**.

For an example that shows how to generate and analyze theoretical BER data
using the **Bit Error Rate Analysis** app, see the Use Theoretical Tab in Bit Error Rate Analysis App section.

For information about the combinations of parameters available on the
**Theoretical** tab and the underlying functions that
perform BER computations, see the Available Sets of Theoretical BER Data section.

This example shows how to use the app to generate and plot theoretical BER data. In particular, the example compares the performance of different modulation orders for QAM in a communications system that includes an AWGN channel.

**Run Theoretical BER Example**

Open the

**Bit Error Rate Analysis**app, and select the**Theoretical**tab.Set these parameters to the values specified in this table.

Parameter Value **E**_{b}/ N_{0}range`0:18`

(default)**Channel type**`AWGN`

(default)**Modulation type**`QAM`

**Modulation order**`4`

Click

**Plot**. The app creates an entry in the data viewer and plots the data in the BER Figure window. Although the specified*E*_{b}/*N*_{0}range is 0:18, the plot includes only BER values that exceed 10^{-8}.Change the

**Modulation order**parameter to`16`

, and click**Plot**. The app creates another entry in the data viewer and plots the new data in the same BER Figure window (not pictured).Change the

**Modulation order**parameter to`64`

, and click**Plot**. The app creates another entry in the data viewer and plots the new data in the same BER Figure window.Click one of the curves to view the modulation order for that curve. The app responds to this action by adjusting the parameters in the

**Theoretical**tab to reflect the values that correspond to that curve.Remove the curve corresponding to 64-QAM from the plot (but not from the data viewer), by clearing

**Plot**for the last entry in the data viewer. To restore the curve for 64-QAM to the plot, in the data viewer, select**Plot**for that curve.

The **Bit Error Rate Analysis** app can generate a large set of
theoretical BERs. Parameters in the **Theoretical** tab enable
you to configure the channel type, modulation type and order, error detection
and correction channel coding, and synchronization error used when the app
computes the theoretical BER. The app adjusts the combination of selectable
parameter values based on your choices so that the configuration is always valid
or uses a dialog box to inform you of valid parameter values.

The app computes the theoretical BER for these modulation types, assuming Gray ordered binary transmission data. The app uses these BER functions to perform underlying computations and limits the modulation order to practical limits.

`berawgn`

— For AWGN channel systems with no coding and perfect synchronization`berfading`

— For fading channel systems with no coding and perfect synchronization`bercoding`

— For systems with channel coding`bersync`

— For systems with BPSK modulation, no coding, and imperfect synchronization`berconfint`

— For error probability estimate and confidence interval of Monte Carlo simulation`berfit`

— For fitting curves to nonsmooth empirical BER data

To compute the BER for higher modulation orders than permitted in the app, use
the BER functions. For more information about specific combinations of
parameters, see the reference pages for the BER functions listed in the
**Bit Error Rate Calculation and Estimation**
function group of the Test and Measurement category.

Using the **Monte Carlo** tab with the **Simulation
environment** parameter set to **MATLAB**, you can
use the **Bit Error Rate Analysis** app in conjunction with your own
MATLAB communications system simulation functions to generate and analyze
BER data. The app calls the simulation specified by the **Function
name** parameter for each specified
*E*_{b}/*N*_{0}
value, collects the BER data from the simulation, and creates a plot. The app
also enables you to adjust the
*E*_{b}/*N*_{0}
range and the stopping criteria for the simulation.

To make your own simulation functions compatible with the app, see the Prepare MATLAB Function for Use in Bit Error Rate Analysis App example on the **Bit Error Rate Analysis** app reference
page.

This example shows how the **Bit Error Rate Analysis** app can run the
`viterbisim`

MATLAB simulation.

To run this example, follow these steps.

Open the

**Bit Error Rate Analysis**app, and select the**Monte Carlo**tab.Set these parameters to the specified values shown in this table.

Parameter Value **E**_{b}/ N_{0}range`0:5`

**Simulation environment****MATLAB**(default)**Function name**`viterbisim`

(default)**Number of Errors**`100`

(default)**Number of bits**`1e8`

(default)Click

**Run**. The app runs the simulation function once for each specified*E*_{b}/*N*_{0}value and gathers BER data.**Note**While the

**Bit Error Rate Analysis**app runs the configured simulation, it cannot process certain other tasks, including plotting data from the other tabs of the user interface. However, you can stop the simulation by clicking**Stop**in the Monte Carlo Simulation dialog box.After computing the BER for each of the specified

*E*_{b}/*N*_{0}values, the app creates a listing in the data viewer.The app also plots the data in the BER Figure window.

Adjust the

**E**parameter to_{b}/N_{0}range`[5 5.2 5.3]`

and the**Number of bits**parameter to`1e5`

. Click**Run**to produce a new set of results.The app runs the simulation function using the new

*E*_{b}/*N*_{0}values and computes new BER data. The app then creates another listing in the data viewer.The app also plots the new data set in the BER Figure window, adjusting the horizontal axis to accommodate the new

*E*_{b}/*N*_{0}values.The BER values for the 5 dB

*E*_{b}/*N*_{0}setting differ between the two sets of data because the number of bits processed by the two simulations was different. If you want the computed BER to converge to a stable value, set the number of bits high enough to ensure that at least 100 bit errors occur. For more information about the criteria used by the**Bit Error Rate Analysis**app to terminate simulations, see the Assign Function Stopping Criteria section.

When you create a MATLAB simulation function for use with the **Bit Error Rate
Analysis** app, control the simulation run duration by setting the
target number of errors and maximum number of bits. The simulation stops the
current
*E*_{b}/*N*_{0}
when either limit is reached. For more information about this requirement, see
the Requirements for Using MATLAB Functions with Bit Error Rate Analysis App section.

After you create your function, set the target number of errors and maximum
number of bits on the **Monte Carlo** tab of the app.

Typically, a **Number of errors** parameter value of at least
`100`

produces an accurate error rate. The **Number
of bits** value prevents the simulation from running too long.
Depending on the
*E*_{b}/*N*_{0}
value and other aspects of the communications system modeled (such as modulation
characteristics and channel conditions), reaching 100 bit errors might not be
realistic. However, if fewer than 100 errors occur because the **Number
of bits** parameter value is too small, the returned error rate
might be misleading. You can use confidence intervals to gauge the accuracy of
the error rates that your simulation produces. As you increase the confidence
level, the accuracy of the computed error rate decreases.

As an example, follow the procedure described in the Use MATLAB Function with Bit Error Rate Analysis App section and set
the **Confidence Level** parameter value to
`95`

for each of the two data sets. The
confidence intervals for the second data set are larger than those for the
first data set because the BER values associated with the second data set
are based on only a small number of observed errors.

**Note**

As long as your function is set up to detect and react to the
**Stop** button in the **Bit Error Rate
Analysis** app, you can use the button to prematurely stop a series
of simulations. For more information, see Assign Function Stopping Criteria.

After you run a simulation with the **Bit Error Rate Analysis** app,
the resulting data set in the data viewer has an active menu in the
**Confidence Level** column. By default the
**Confidence Level** value is
`off`

, meaning the simulation data in the BER
Figure window does not show confidence intervals.

To show confidence intervals in the BER Figure window, set
**Confidence Level** to `90%`

,
`95%`

, or `99%`

.

The plot in the BER Figure window automatically responds the
**Confidence Level** value change. This figure shows a
sample plot.

For an example that plots confidence intervals for a Simulink simulation, see the Use Simulink Model with Bit Error Rate Analysis App section.

To find confidence intervals for levels not listed in the **Confidence
Level** menu, use the `berconfint`

function.

After you run a simulation with the **Bit Error Rate Analysis** app,
the BER Figure window plots individual BER data points. To fit a curve to a data
set that contains at least four points, select **Fit** for that
data in the data viewer.

The plot in the BER Figure window automatically responds to this selection. This plot shows a curve fit to a set of BER results.

For greater flexibility in the process of fitting a curve to BER data, use the
`berfit`

function.

When you create a MATLAB function for use with the **Bit Error Rate Analysis** app,
ensure the function interacts properly with the user interface. This section
describes the inputs, outputs, and basic operation of a function that is compatible
with the app.

**Input Arguments**

The **Bit Error Rate Analysis** app evaluates your entries in fields of the
user interface and passes data to the function as these input arguments (in
sequential order).

One value from the

**E**vector each time the_{b}/N_{0}range**Bit Error Rate Analysis**app runs the simulation function**Number of errors**value**Number of bits**value

**Output Arguments**

Your simulation function must compute and return these output arguments (in
sequential order). The **Bit Error Rate Analysis** app uses these output
arguments when reporting and plotting results.

Bit error rate of the simulation

Number of bits processed when computing the BER

**Simulation Function Operation**

Your simulation function must perform these tasks:

Simulate the communications system for the

*E*_{b}/*N*_{0}value specified in the first input argument.Stop simulating when the number of errors or number of processed bits equals or exceeds the corresponding threshold specified in the second or third input argument, respectively.

Detect whether you click

**Stop**in the**Bit Error Rate Analysis**app to stop the simulation in that case.

Use this template when adapting your code to work with the **Bit Error Rate
Analysis** app. You can open the template in an editor by entering
`edit bertooltemplate`

at the MATLAB command prompt. If you develop a simulation function without using
the template, be sure your function satisfies the requirements described in the
Requirements for Using MATLAB Functions with Bit Error Rate Analysis App section.

**Note**

To use this template, you must insert your own simulation code in the
places marked `INSERT YOUR CODE HERE`

. For a complete
example based on this template, see the Prepare MATLAB Function for Use in Bit Error Rate Analysis App example on the **Bit Error Rate Analysis** app
reference page.

function [ber,numBits] = bertooltemplateTemp(EbNo,maxNumErrs,maxNumBits,varargin) %BERTOOLTEMPLATE Template for a BERTool (Bit Error Rate Analysis app) simulation function. % This file is a template for a BERTool-compatible simulation function. % To use the template, insert your own code in the places marked "INSERT % YOUR CODE HERE" and save the result as a file on your MATLAB path. Then % use the Monte Carlo pane of BERTool to execute the script. % % [BER, NUMBITS] = YOURFUNCTION(EBNO, MAXNUMERRS, MAXNUMBITS) simulates % the error rate performance of a communications system. EBNO is a vector % of Eb/No values, MAXNUMERRS is the maximum number of errors to collect % before stopping the simulation, and MAXNUMBITS is the maximum number of % bits to run before stopping the simulation. BER is the computed bit error % rate, and NUMBITS is the actual number of bits run. Simulation can be % interrupted only after an Eb/No point is simulated. % % [BER, NUMBITS] = YOURFUNCTION(EBNO, MAXNUMERRS, MAXNUMBITS, BERTOOL) % also provides BERTOOL, which is the handle for the BERTool app and can % be used to check the app status to interrupt the simulation of an Eb/No % point. % % For more information about this template and an example that uses it, % see the Communications Toolbox documentation. % % See also BERTOOL and VITERBISIM. % Copyright 2020 The MathWorks, Inc. % Initialize variables related to exit criteria. totErr = 0; % Number of errors observed numBits = 0; % Number of bits processed % --- Set up the simulation parameters. --- % --- INSERT YOUR CODE HERE. % Simulate until either the number of errors exceeds maxNumErrs % or the number of bits processed exceeds maxNumBits. while((totErr < maxNumErrs) && (numBits < maxNumBits)) % Check if the user clicked the Stop button of BERTool. if isBERToolSimulationStopped(varargin{:}) break end % --- Proceed with the simulation. % --- Be sure to update totErr and numBits. % --- INSERT YOUR CODE HERE. end % End of loop % Compute the BER. ber = totErr/numBits;

**About Template for Simulation Function**

The simulation function template either satisfies the requirements listed in the Requirements for Using MATLAB Functions with Bit Error Rate Analysis App section or indicates where you need to insert code. In particular, the template:

Has appropriate input and output arguments

Includes a placeholder for code that simulates a system for the given

*E*_{b}/*N*_{0}valueUses a loop structure to stop simulating when either the number of errors exceeds

`maxNumErrs`

or the number of bits exceeds`maxNumBits`

, whichever occurs first**Note**Although the

`while`

statement of the loop describes the exit criteria, your own code inserted into the section marked`Proceed with simulation`

must compute the number of errors and the number of bits. If you do not perform these computations in your own code, clicking**Stop**in the Monte Carlo Simulation dialog box is the only way to terminate the loop.Detects when the user clicks

**Stop**in the Monte Carlo Simulation dialog box in each iteration of the loop

**Use Simulation Function Template**

Follow these steps to update the simulation function template with your own simulation code.

Place the code for setup tasks in the template section marked

`Set up parameters`

. For example, initialize variables such as those containing the modulation alphabet size, filter coefficients, a convolutional coding trellis, or the states of a convolutional interleaver.Place the code for these core simulation tasks in the template section marked

`Proceed with simulation`

. Determine the core simulation tasks, assuming that all setup work has already been performed. For example, core simulation tasks include filtering, error-control coding, modulation and demodulation, and channel modeling.Also in the template section marked

`Proceed with simulation`

, include code that updates the values of the`totErr`

and`numBits`

variables. The`totErr`

value represents the number of errors observed so far. The`numBits`

value represents the number of bits processed so far. The computations to update these variables depend on how your core simulation tasks work.**Note**Updating the numbers of errors and bits is important for ensuring that the loop terminates.

Omit from your simulation code any setup code that initializes

`EbNo`

,`maxNumErrs`

, or`maxNumBits`

variables, because the app passes these quantities to the function as input arguments after evaluating the data entered on the**Monte Carlo**tab.Adjust your code or the code of the template as necessary to use consistent variable names and meanings. For example, if your original code uses a variable called

`ebn0`

and the function declaration (first line) for the template uses the variable name`EbNo`

, you must change one of the names so that they match. As another example, if your original code uses SNR instead of*E*_{b}/*N*_{0}values, you must convert values appropriately.

Use the Bit Error Rate Analysis app to compute the BER as a function of ${\mathit{E}}_{\mathrm{b}}/{\mathit{N}}_{0}$. The app analyzes performance with either Monte Carlo simulations of MATLAB® functions and Simulink® models or theoretical closed-form expressions for selected types of communications systems. The code in the mpsksim.m function provides an M-PSK simulation that you can run from the **Monte Carlo** tab of the app.

Open the **Bit Error Rate Analysis** app from the Apps tab or by running the `bertool`

function in the MATLAB® command window.

On the **Monte Carlo** tab, set the ${\mathit{E}}_{\mathbf{b}}/{\mathit{N}}_{0}$** range** parameter to `1:1:5`

and the **Function name** parameter to `mpsksim`

.

Open the `mpsksim`

function for editing, set `M=2`

, and save the changed file.

Run the `mpsksim.m`

function as configured by clicking **Run** on the **Monte Carlo** tab in the app.

After the app simulates the set of ${\mathit{E}}_{\mathrm{b}}/{\mathit{N}}_{0}$ points, update the name of the BER data set results by selecting `simulation0`

in the **BER Data Set** field and typing `M=2`

to rename the set of results. The legend on the BER figure updates the label to `M=2`

.

Update the value for `M`

in the `mpsksim`

function, repeating this process for `M`

= `4`

, `8`

, and `16`

. For example, these figures of the **Bit Error Rate Analysis** app and BER Figure window show results for varying `M`

values.

**Parallel SNR Sweep Using Bit Error Rate Analysis App**

The default configuration for the Monte Carlo processing of the **Bit Error Rate Analysis** app automatically uses parallel pool processing to process individual ${\mathit{E}}_{\mathrm{b}}/{\mathit{N}}_{0}$ points when you have the Parallel Computing Toolbox™ software but for the processing of your simulation code:

You can use the **Bit Error Rate Analysis** app in conjunction with
Simulink models to generate and analyze BER data. The Simulink model simulates the performance of the communications system that
you want to study, while the **Bit Error Rate Analysis** app manages a
series of simulations using the model and collects the BER data.

**Note**

To use Simulink models within the **Bit Error Rate Analysis** app, you
must have the Simulink software.

To access the capabilities of the **Bit Error Rate Analysis** app
related to Simulink models, open the **Monte Carlo** tab, and then
set the **Simulation environment** parameter to
**Simulink**. If using parallel processing, the output must
be saved to a workspace variable so that the parallel running engine can collect
the results. For example, save the output of the Error Rate Calculation block to a
workspace variable by using a Signal To Workspace block configured
to save the output to the name specified for the **BER variable
name**.

For details about confidence intervals and curve fitting for simulation data, see the Plot Confidence Intervals and Curve Fit BER Points sections, respectively.

This example shows how the **Bit Error Rate Analysis** app can manage a
series of simulations of a Simulink model and how you can vary the plot. This figure shows the
`commgraycode`

model.

To run this example, follow these steps.

Open the

**Bit Error Rate Analysis**app. On the**Monte Carlo**tab, enter the Simulink model name and a BER variable name. The default value for the**Model name**parameter is`commgraycode`

. the default value for the**BER variable name**parameter is`grayBER`

.Click

**Run**.The

**Bit Error Rate Analysis**app loads the model into memory. The model initializes several variables in the MATLAB workspace. The app runs the simulation model once for each*E*_{b}/*N*_{0}value, gathers the BER results, and creates a listing for the BER results in the data viewer.The

**Bit Error Rate Analysis**app plots the data in the BER Figure window.To fit a curve to the series of points in the BER Figure window, select

**Fit**for the`simulation0`

data in the data viewer.The

**Bit Error Rate Analysis**app plots the curve.To indicate a 99% confidence interval around each point in the simulation data, set

**Confidence Level**to`99%`

in the data viewer.The

**Bit Error Rate Analysis**app displays error bars to represent the confidence intervals.

For another example that uses the **Bit Error Rate Analysis** app to
manage a series of Simulink simulations, see the Prepare Simulink Model for Use with Bit Error Rate Analysis App example on the **Bit Error Rate Analysis** app reference
page.

When you create a Simulink model for use with the **Bit Error Rate Analysis** app, you
must set it up so that the simulation ends when it either detects a target
number of errors or processes a maximum number of bits, whichever occurs first.
For more information about this requirement, see the Requirements for Using Simulink Models with Bit Error Rate Analysis App section.

After creating your Simulink model, set the target number of errors and the maximum number of
bits on the **Monte Carlo** tab of the **Bit Error Rate
Analysis** app.

Typically, a **Number of errors** parameter value of at least
`100`

produces an accurate error rate. The **Number
of bits** value prevents the simulation from running too long,
especially at large
*E*_{b}/*N*_{0}
values. However, if the **Number of bits** value is so small
that the simulation collects very few errors, the error rate might not be
accurate. You can use confidence intervals to gauge the accuracy of the error
rates that your simulation produces. Larger confidence intervals result in less
accurate computed error rates.

You can also click **Stop** in the Monte Carlo Simulation
dialog box to stop a series of simulations prematurely.

When you create a Simulink model for use with the **Bit Error Rate Analysis** app, ensure
the model interacts properly with the user interface. This section describes the
inputs, outputs, and basic operation of a model that is compatible with the
app.

**Input Variables**

The channel block must use the

`EbNo`

variable rather than a hard-coded value for*E*_{b}/*N*_{0}. For example, to model an AWGN channel, use the AWGN Channel block with the**Mode**parameter set to`Signal to noise ratio (Eb/No)`

and the**Eb/No (dB)**parameter set to`EbNo`

.The simulation must stop either when the error count reaches the value of the

`maxNumErrs`

variable or when the number of processed bits reaches the value of the`maxNumBits`

variable, whichever occurs first. You can configure the Error Rate Calculation block in your model to use these criteria to stop the simulation.

**Output Variables**

The simulation must send the final error rate data to the MATLAB workspace as a variable whose name you enter in the

**BER variable name**parameter in the**Bit Error Rate Analysis**app. The output error statistics variable must be a three-element vector that lists the BER, the number of bit errors, and the number of processed bits.The three-element vector format for the output error statistics is supported by the Error Rate Calculation block.

**Simulation Model Operation**

To avoid using an undefined variable name in blocks of the Simulink model, initialize these variables in the MATLAB workspace by using the preload callback function of the model or by assigning them at the MATLAB command prompt.

EbNo = 0; maxNumErrs = 100; maxNumBits = 1e8;

**Tip**Using the preload function callback of the model to initialize the runtime variables enables to you reopen the model in a future MATLAB session with runtime variables preconfigured to run in the app.

The

**Bit Error Rate Analysis**app provides the actual values based on values in the**Monte Carlo**tab, so the initial values in the model or workspace are not important.The app assumes the

*E*_{b}/*N*_{0}is used in the channel modeling. If your model uses the AWGN Channel block, and the**Mode**parameter is not set to`Signal to noise ratio (Eb/No)`

, adapt the block to use the*E*_{b}/*N*_{0}mode instead. For more information, see the AWGN Channel block reference page.To compute the error rate, use the Error Rate Calculation block with these parameter settings: select

**Stop simulation**, set**Target number of errors**to`maxNumErrs`

, and set**Maximum number of symbols**to`maxNumBits`

.If your model computes an SER instead of a BER, use the Integer to Bit Converter block to convert symbols to bits.

To send data from the Error Rate Calculation block to the MATLAB workspace, set the

**Output data**parameter to`Port`

, attach a To Workspace (Simulink) block to the Error Rate Calculation block, and set the**Limit data points to last**parameter of the To Workspaceblock to`1`

. The**Variable name**parameter in the To Workspace block must match the value you enter in the**BER variable name**parameter of the**Bit Error Rate Analysis**app.**Tip**More than one To Workspace block exists. Select the To Workspace block from the DSP System Toolbox™ / Sinks sublibrary.

Frame-based simulations often run faster than sample-based simulations for the same number of bits processed. With a frame-based simulation, because the simulation processes a full frame of data each frame, the number of errors or number of processed bits might exceed the values you enter in the

**Bit Error Rate Analysis**app.If your model uses a callback function to initialize variables in the MATLAB workspace upon loading the model, before you click

**Run**in the**Bit Error Rate Analysis**app, make sure that one of these conditions is met:The model is in memory (whether in a window or not), and the variables are intact.

The model is not currently in memory. In this case, the

**Bit Error Rate Analysis**app loads the model into memory and runs the callback functions.

If you clear or overwrite the variables set in the model, clear the model from memory by calling the

`bdclose`

(Simulink) function at the MATLAB command prompt.When you click`bdclose all`

**Run**in the**Monte Carlo**tab, the app reloads the model.

The data viewer gives you flexibility to rename and delete data sets and to reorder columns in the data viewer.

To rename a data set in the data viewer, double-click its name in the

**BER Data Set**column and type a new name.To delete a data set from the data viewer, select the data set, then select

**Edit > Delete**.**Note**If the data set originated from the

**Theoretical**tab, the**Bit Error Rate Analysis**app deletes the data without asking for confirmation. You cannot undo this operation.

The **Bit Error Rate Analysis** app enables you to export individual
data sets to the MATLAB workspace or to MAT-files. Exporting data enables you to process
the data outside the **Bit Error Rate Analysis** app. For example, to
create customized plots using data from the **Bit Error Rate Analysis**
app, export the app data set to the MATLAB workspace and use any of the plotting commands in MATLAB. To reimport a structure later, see the Import Bit Error Rate Analysis app Data Set section.

To export an individual data set, follow these steps.

In the data viewer, select the data set you want to export.

Select

**File > Export Data**. Set**Export to**to indicate the format and destination of the data.`Workspace arrays`

— Export the selected data set to a pair of arrays in the MATLAB workspace. Use this option if you want to access the data in the MATLAB workspace (outside the app) and if you do not need to import the data into the**Bit Error Rate Analysis**app later.Under

**Variable names**, set**E**and_{b}/N_{0}**BER**parameters to specify the variable names for the*E*_{b}/*N*_{0}values and BER values, respectively.If you want the

**Bit Error Rate Analysis**app to use your chosen variable names even if variables by those names already exist in the workspace, select**Overwrite variables**.`Workspace structure`

— Export the selected data set to a structure in the MATLAB workspace. If you export data using this option, you can import the data structure into the**Bit Error Rate Analysis**app later.Set the

**Structure name**parameter to specify a workspace structure name.If you want the

**Bit Error Rate Analysis**app to use your chosen variable name even if a variable with that name already exist in the workspace, select**Overwrite variables**.`MAT-file`

— Export the selected data set to a structure in a MAT-file. If you export data using this option, you can import a MAT-file data structure into the**Bit Error Rate Analysis**app later.Set the

**Structure name in file**parameter to specify a MAT-file name. The structure name in the file will also use this name.

Click

**OK**. If you set**Export to**to`MAT-file`

, the**Bit Error Rate Analysis**app prompts you for the path to the MAT-file that you want to create.

**Examine an Exported Structure**

This section describes the contents of the structure that the **Bit Error
Rate Analysis** app exports to the workspace or to a MAT-file. This
table describes the fields of the exported data structure. When you want to
manipulate exported data, the fields that are most relevant are
`paramsEvaled`

and `data`

.

Field | Description |
---|---|

`params` | The parameter values in the Bit Error Rate
Analysis app, some of which might be invisible and
hence irrelevant for computations |

`paramsEvaled` | The parameter values evaluated and used by the Bit Error
Rate Analysis app when computing the data set |

`data` | The
E_{b}/N_{0},
BER, and number of bits processed |

`dataView` | Information about the appearance in the data viewer, which is
used by the Bit Error Rate Analysis app when
reimporting the data |

`cellEditabilities` | Indication whether the data viewer has an active
Confidence Level or
Fit entry, which is used by the
Bit Error Rate Analysis app when reimporting the
data |

**Parameter Fields**

The `params`

and `paramsEvaled`

fields are
similar to each other, except that `params`

describes the exact
state of the user interface, whereas `paramsEvaled`

indicates
the values that are actually used for computations. For example, in a
theoretical system with an AWGN channel, `params`

records but
`paramsEvaled`

omits a diversity order parameter. The
diversity order is not used in the computations because it is relevant for only
systems with Rayleigh channels. As another example, if you type
`[0:3]+1`

in the user interface as the range of
*E*_{b}/*N*_{0}
values, `params`

indicates `[0:3]+1`

, whereas
`paramsEvaled`

indicates `1 2 3 4`

.

The length and exact contents of `paramsEvaled`

depend on the
data set because only relevant information appears. If the meaning of the
contents of `paramsEvaled`

is not clear upon inspection, one
way to learn more is to reimport the data set into the **Bit Error Rate
Analysis** app and inspect the parameter values that appear in the user
interface.

**Data Field**

If your exported workspace variable is called `ber0`

, the
field `ber0.data`

is a cell array that contains the numerical
results in these vectors:

`ber0.data{1}`

lists the*E*_{b}/*N*_{0}values.`ber0.data{2}`

lists the BER values corresponding to each of the*E*_{b}/*N*_{0}values.`ber0.data{3}`

indicates, for simulation results, how many bits the**Bit Error Rate Analysis**app processed when computing each of the corresponding BER values.

The **Bit Error Rate Analysis** app enables you to save an entire
session. This feature is useful if your session contains multiple data sets that
you want to return to in a later session. To reimport a saved session, see the
Open Previous Bit Error Rate Analysis app Session
section.

To save an entire **Bit Error Rate Analysis** app session, follow these steps.

Select

**File > Save Session**.When the

**Bit Error Rate Analysis**app prompts you, enter the path to the file that you want to create.

The **Bit Error Rate Analysis** app saves the data in a MAT file or a
binary file that records all data sets currently in the data viewer along with
the user interface parameters associated with the data sets.

**Note**

If your **Bit Error Rate Analysis** app session requires particular
workspace variables, save those separately in a MAT-file using the
`save`

command in MATLAB.

The **Bit Error Rate Analysis** app enables you to reimport individual
data sets that you previously exported to a structure. For more information
about exporting data sets from the **Bit Error Rate Analysis** app, see
the Export Bit Error Rate Analysis app Data Set section.

To import an individual data set that you previously exported from the
**Bit Error Rate Analysis** app to a structure, follow these steps.

Select

**File > Import Data**.Set the

**Import from**parameter to either`Workspace structure`

or`MAT-file`

. If you select`Workspace structure`

, type the name of the workspace variable in the**Structure name**parameter.Click

**OK**. If you set**Import from**to`MAT-file`

, the**Bit Error Rate Analysis**app prompts you to select the file that contains the structure you want to import.

After you dismiss the Data Import dialog box (and the file selection dialog box, in the case of a MAT-file), the data viewer shows the newly imported data set and the BER Figure window the corresponding plot.

The **Bit Error Rate Analysis** app enables you to open previous saved
sessions. For more information about exporting data sets from the **Bit Error
Rate Analysis** app, see the Save Bit Error Rate Analysis app Session section.

To replace the data sets in the data viewer with data sets from a previous
**Bit Error Rate Analysis** app session, follow these steps.

Select

**File > Open Session**.**Note**If the

**Bit Error Rate Analysis**app already contains data sets, your are asked whether you want to save the current session. If you answer no and continue with the loading process, the**Bit Error Rate Analysis**app discards the current session upon opening a new session from the file.When the

**Bit Error Rate Analysis**app prompts you, enter the path to the file you want to open. It must be a file that you previously created using the**Save Session**option in the**Bit Error Rate Analysis**app.

After the **Bit Error Rate Analysis** app reads the session file, the
data viewer shows the data sets from the file.

If the **Bit Error Rate Analysis** app session requires particular
workspace variables that you saved separately in a MAT-file, you can retrieve
them by using the `load`

function at the
MATLAB command prompt. For example, to load the **Bit Error Rate
Analysis** app session named
`ber_analysis_filename.mat`

enter this command.

`load ber_analysis_filename.mat`