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Re: [Discuss-gnuradio] Spectrum Sense - Convert FFT Data into dBm propor

From: TMob
Subject: Re: [Discuss-gnuradio] Spectrum Sense - Convert FFT Data into dBm proportional values
Date: Wed, 4 Feb 2009 03:42:13 -0800 (PST)

Thanks for your reply Bruhtesfa, by subtracting the log of the FFT size, I
think I am normalizing the sum to the length of the FFT already. At least
this is why I am subtracting the FFT size.

In the meantime, I ran into another issue while playing around with a signal
generator and my spectrum sensing code. When I turn on the signal generator
on a specific channel, with a bandwidth of 20MHz I would expect to see the
values in the center frequency increase and also the ones of the
neighbouring 802.11 channels. So far so good, but if I do so, I also see an
increase in the values of all other channels that should not be interferred
by the signal.

I played around with the parameters of the USRP a little bit and found, that
this behaviour is dependent on the gain of the USRP. When I reduced the gain
to let's say 10dB, the signal from the signal generator would only appear in
the data of the center channel and its neighbors like it should be. Of
course setting a fixed but reduced gain would solve my problem for this
specific configuration but I need something that works for varying

Now, I looked around in the mailing list and in the source code of
usrp_spectrum_sense.py and didn't find any sort of automatic gain control
that would adjust the gain of the USRP to the received signal strength. I am
wondering if there is a way to implement something like this to receive more
valid results than the ones I am getting at the moment. Does anybody have an

Any help is very much appreciated!


>Eventhough, I am not clear about what you are doing(i am a new one),I 
>know from FFT theories that the computed FFT should be normalized by 
>1/sqrt(N) to preserve the energy(where N is size of FFT u take).
>That means, 10*log(N) should be substracted from the computed FFT power 
>to get same power as the one computed in time domain.
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