|Subject:||Re: [Discuss-gnuradio] Multipath Fading|
|Date:||Fri, 08 May 2015 12:13:38 +0200|
|User-agent:||Mozilla/5.0 (X11; Linux x86_64; rv:31.0) Gecko/20100101 Thunderbird/31.6.0|
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 .
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, which, if I don't misread the source, is really just Clarke's model of a Rayleigh channel, 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; 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/.
 GRC file also available: https://gist.github.com/marcusmueller/1bbf7704afec20c4f00d
 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
 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.
On 05/07/2015 07:24 PM, Ritvik Pandey wrote:
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