Estimate coherent BPSK or QPSK bit error rate in additive white Gaussian noise from Eb/N0. This is a baseline digital-link result before coding, fading, interference, or implementation losses.
Bit error rate
3.872e-6
Coherent AWGN detection
Symbol error rate
3.872e-6
1 bits per symbol
Eb/N0 linear
10.000
Power ratio
Modulation
BPSK
1 bits per symbol
Expected spacing
1 / 258,257
bits per expected error
The curve shows how bit errors collapse as energy per bit rises; the constellation sketch shows the noise spread that must stay away from the decision boundary.
Bit error probability vs Eb/N0
log scale, lower is better
10.00 dB
3.87e-6
Decision space
1 bits per symbol
shrinks as Eb/N0 rises
noise crosses a decision boundary
This tool is open source and the underlying logic is fully transparent. You can inspect the code, understand the calculations, and contribute improvements. If you want to use the tool in your own website, course page, or learning platform, you can also embed it directly and start from a ready-made iframe setup for this exact tool.
Open source: review the implementation and see how the results are produced.
Embeddable: preview this tool, copy the iframe, and use it in your own site or LMS.
Estimate coherent BPSK or QPSK bit error rate in additive white Gaussian noise from Eb/N0. This is a baseline digital-link result before coding, fading, interference, or implementation losses.
Bit error rate
3.872e-6
Coherent AWGN detection
Symbol error rate
3.872e-6
1 bits per symbol
Eb/N0 linear
10.000
Power ratio
Modulation
BPSK
1 bits per symbol
Expected spacing
1 / 258,257
bits per expected error
The curve shows how bit errors collapse as energy per bit rises; the constellation sketch shows the noise spread that must stay away from the decision boundary.
Bit error probability vs Eb/N0
log scale, lower is better
10.00 dB
3.87e-6
Decision space
1 bits per symbol
shrinks as Eb/N0 rises
noise crosses a decision boundary
This tool is open source and the underlying logic is fully transparent. You can inspect the code, understand the calculations, and contribute improvements. If you want to use the tool in your own website, course page, or learning platform, you can also embed it directly and start from a ready-made iframe setup for this exact tool.
Open source: review the implementation and see how the results are produced.
Embeddable: preview this tool, copy the iframe, and use it in your own site or LMS.