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Calculation of BER for 8-PSK

The Error Rate Calculation block calculates the error rate by bits (bit error rate, BER). Using this block, we can obtain BER data for the communication system and analyze the effectiveness of our system. In this example, we will look at a simple 8-PSK receiver and transmitter model. It is shown in the picture below.

image_2.png

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(0.5)
    return model_output
end
Out[0]:
run_model (generic function with 1 method)

Next, we will initialize the signal-to-noise ratio indicators for our model and declare a bit error variable.

In [ ]:
EbNoArr = collect(-9:3:9);
Eb_No = 0;
ber = zeros(length(EbNoArr));
BER = 0;

Now let's run the model at different values of the signal-to-noise ratio.

In [ ]:
for i in 1:length(EbNoArr)
    Eb_No = EbNoArr[i]
    run_model("8-PSK") # Launching the model.
    BER = collect(BER)
    ber[i]=BER.value[end][1]
    println("BER: $(ber[i])")
end
Building...
Progress 0%
Progress 21%
Progress 100%
BER: 0.4042624042624043
Building...
Progress 0%
Progress 53%
Progress 100%
BER: 0.351981351981352
Building...
Progress 0%
Progress 43%
Progress 100%
BER: 0.3016983016983017
Building...
Progress 0%
Progress 40%
Progress 100%
BER: 0.20646020646020646
Building...
Progress 0%
Progress 38%
Progress 100%
BER: 0.10556110556110557
Building...
Progress 0%
Progress 34%
Progress 100%
Progress 100%
BER: 0.042624042624042624
Building...
Progress 0%
Progress 20%
Progress 100%
Progress 100%
BER: 0.004329004329004329

We will construct a BER graph from both the model and theoretical calculations.

In [ ]:
using SpecialFunctions
function berawgn_psk(EbNo_dB, M)
    EbNo = 10 .^ (EbNo_dB ./ 10)
    k = log2(M)
    return 2 * erfc.(sqrt.(k .* EbNo) .* sin(π/M)) / k
end
ber_ref = berawgn_psk(EbNoArr, 8)
println("__Eb_No__: $EbNoArr")
println("_ber_ref_: $(round.(ber_ref, digits=3))")
println("ber_model: $(round.(ber, digits=3))")

p1 = plot(EbNoArr, ber_ref, seriestype = :scatter, marker = :rect, label = "Theoretical QPSK", yscale = :log10)
plot!(p1, EbNoArr, ber, seriestype = :scatter, marker = :diamond, label = "Model QPSK")
xlabel!(p1, "Eb/No (dB)")
ylabel!(p1, "BER")
title!(p1, "Bit Error Rate (Log Scale)")
# Second graph: normal scale
p2 = plot(EbNoArr, ber_ref, seriestype = :scatter, marker = :rect, label = "")
plot!(p2, EbNoArr, ber, seriestype = :scatter, marker = :diamond, label = "")
ylabel!(p2, "BER")
title!(p2, "Bit Error Rate (Linear Scale)")

# The general conclusion: two graphs side by side
plot(p2, p1, layout = (2, 1), size=(1000,400))
__Eb_No__: [-9, -6, -3, 0, 3, 6, 9]
_ber_ref_: [0.493, 0.426, 0.338, 0.232, 0.124, 0.041, 0.005]
ber_model: [0.404, 0.352, 0.302, 0.206, 0.106, 0.043, 0.004]
Out[0]:

Conclusion

As you can see in this example, the higher the signal-to-noise ratio, the lower the error.
Here we have figured out how to build a BER graph for a simple communication system model and learned how to apply this method to analyze the system.