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I am using USRP to transmit and received MIMO frames similar to Wi-Fi standard that I created on my own. After receiving the signal, it is processed as described in the standard.

While I plot the QPSK constellation points after frequency and phase correction, I have a plot (shown above) which is noisy. Now I want to plot (BER Vs SNR). I know that we can calculate BER as: (Error Bits/Total No. of bits). But I am not sure how to calculate SNR from these complex symbols.

What I understand from online materials is that SNR is the noise variance that is estimated from around the constellation points (maybe some distance/deviation measurement form the true point. The question might be very silly, but I really appreciate anyone helps to understand how this is actually done. I am doing all the coding in python.

QPSK Constellation points after frequency and phase correction

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If you were to increase the dispersion about 3X, you'd no longer have a robust system, in that outliers of your 4 points would have moved into neighboring boxes, and your system would make Bit Errors.

Consider adding 10dB Noise Power to your datalink, and examine the 4 regions again.

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Your 4 points are of course not points at all. If these spreads are due to non-linearities and InterSymbolInterference, then as show, your SNR is infinite.

Add noise power that is 1% of the signal power. And think about the new points.

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for any set of data, you can compute the mean and standard_deviation/sigma/rms

I'd view the mean as signal power, and the sigma as noise power.

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  • \$\begingroup\$ I understand the part that if noise is increased the bits will shift to neighboring boxes, which means higher BER. But how do I calculate SNR exactly from this plot? Or you are saying I first measure the received signal power from an FFT plot, and calculate BER, then I decrease the transmit power by let's say 10dB and then measure BER. Is this is how it works? And then I plot BER Vs how the noise level was increased (SNR) ? \$\endgroup\$
    – SAM
    May 3 '20 at 16:27

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