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The effect of single BER pulses

In this example, we will consider a BPSK receiver and transmitter system with single data interference. The system itself is shown in the figure below.

image.png

At the top level, we see a random dataset generator, BPSK, and a pulse generator, which is shown in the figure below.

image.png

There are 3 pulse generators in this subsystem. Due to delays and adders, we increase the number of these pulses.

Next, we will set an auxiliary function to run the model.

In [ ]: 
# Enabling the auxiliary model launch function.
function run_model( name_model)
    
    Path = (@__DIR__) * "/" * name_model * ".engee"
    
    if name_model in [m.name for m in engee.get_all_models()] # Checking the condition for loading a model into the kernel
        model = engee.open( name_model ) # Open the model
        model_output = engee.run( model, verbose=true ); # Launch the model
    else
        model = engee.load( Path, force=true ) # Upload a model
        model_output = engee.run( model, verbose=true ); # Launch the model
        engee.close( name_model, force=true ); # Close the model
    end
    sleep(5)
    return model_output
end
Out[0]:
run_model (generic function with 1 method)

Let's run the model with the interference turned off and on.

In [ ]: 
EnableImpulse = 0;
run_model("BPSK_and_Impulse") # Launching the model.

BER = collect(BER)
println("BER: " * string(BER.value[end]))

EnableImpulse = 1;
run_model("BPSK_and_Impulse") # Launching the model.

BER = collect(BER)
print("BER: " * string(BER.value[end]))

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BER: [0.0, 0.0, 1501.0]
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BER: [0.002664890073284477, 4.0, 1501.0]

Conclusion

As we can see from the results, in our case, after adding interference, several values were identified incorrectly. Most likely, this is due to the fact that there were points that were defined as 1 instead of 0. To avoid this situation, use interpolation and averaging.

Blocks used in example