I am trying to compare the BER performance of fractional and symbol spaced equalizers for channel equalization in colored noise scenario. I have found from simulation that for positively correlated noise, fractional equalizers perform better than symbol spaced equalizers. It is probably due to fact that with more samples we have more information in case of fractional equalizers. Is there any way of theoretically prove that fractional equalizers are better than symbol spaced equalizers for colored noise? Thanks


Yes of course, that's true.

FS equalizer sample the signal in rate faster than Nyquist rate which provide more information about the signal. It's similar when you increase the sampling frequency, you get better results.

If you need to understand that idea, you can read about oversampling, fractional sampling, and if you are working on sparse channel read you compressive sending working method.

  • $\begingroup$ Thanks for the reply. For a white noise, given that delay is properly compensated, both equalizers give the same performance. For colored noise i dont understand why fractional equalizer performed better. It seems like the noise samples are correlated to each other so, more samples means better performance. Is there any expression that verify this idea? $\endgroup$ – San Aug 23 '18 at 19:23
  • $\begingroup$ @San .. Could you share the code you are working on it please .. I need to check. $\endgroup$ – Zeyad_Zeyad Aug 25 '18 at 7:15

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