Hi Ritvik,

to analyze whether you’re seeing attenuation, you should actually

compare the power of a un-faded and the faded version of a signal with a

*single* QT sink. You can configure that sink to have two inputs. Your

dB-scaled FFT display hardly lends itself to comparing powers.

To test the fading model, you really should use noise from the GNU Radio

noise source and not what you receive with a USRP, because in normal

cases, what a USRP receives won’t be white, and hence is sub-optimal to

test a channel model.

Therefore, the testing setup I’d propose looks somewhat like [0].

according to the theory power should attenuate after applying fading.

Talking of the gr::channels::fading_model: It’s solely based on

flat_fader_impl[1], which, if I don’t misread the source, is really just

Clarke’s model of a Rayleigh channel[2], scaled. This will not give you

a strong attenuation on average. Rayleigh fading is based on the idea

that due to a large number of real-world scatterers, the channel impulse

response (which is a function) comes from a Gaussian process[3]; because

we consider complex channels, the amplitude (as the combination of

independent I and Q) is Rayleigh distributed.

Now, you’re right, at no point there can be more power coming out of the

channel than what went into it, so the maximum output of the channel is

the input power, and on average, the power will be less. However, the

statistical properties of the actual power of an actual channel depend

on the statistical properties of the the absolute path lengths –

something that a fading channel model itself doesn’t simulate, because

it only cares about the path /differences/.

Greetings,

Marcus

[0]

https://gist.github.com/marcusmueller/1bbf7704afec20c4f00d/raw/6d5a0329e6fb8916be3e5eb4817e0c47b279a3c9/fading_test.grc.png

[0] GRC file also available:

https://gist.github.com/marcusmueller/1bbf7704afec20c4f00d

[1]

https://github.com/gnuradio/gnuradio/blob/master/gr-channels/lib/flat_fader_impl.cc

[2] Since you’re after modeling RF channels a bit closer to the physical

medium: Have a look at Appendix A /p.992 of

Clarke, R.H., “A statistical theory of mobile-radio reception,” /Bell

System Technical Journal/ , vol.47, no.6, pp.957,1000, July-Aug. 1968

URL:

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6779222&isnumber=6779217

[3] Central limit theorem: a sum of independent, identically

distributed random variables follows a Gaussian distribution, no matter

what the distribution of the individual variable is, as long as the

number of variables is sufficiently large.