Re: [Discuss-gnuradio] usrp_spectrum_sense vs. usrp_fft

[killoqg]

 Hi,

 I'm having the same problem: the output of my spectrum analyzer and the
output of the spectrum_sense.py doesn't match. I'm trying to detect activity
int the GSM900 band, but it's impossible with the outputs I obtain.

 I was trying to correct the code and I think that your formula is
incorrect. I'll try to explain:
 
  P=E/N, with P=power of signal, E=energy of signal and N=number of points

 1) The output of the c2mag (gr.complex_to_mag_squared) is the energy of the
signal, so the power of the same signal will be these values divided by N
(number of points), I think the square root is not necessary.

 2) For the same reason, when you make
10*math.log(tb.power/tb.fft_size/tb.fft_size) I think it should be
10*math.log(tb.power/tb.fft_size) 

  3) After that, the last operation (signalPower /= tb.fft_size) is
unnecessary, it's already done in 1)

The incorrect range of values could be a problem of the ref-scale parameter.
I tried to correct the output values using -20*math.log10(tb.ref_scale) but
they're still out of range.

 In my case, either the maximum values of the output doesn't match with the
ones in the spectrum analyzer view. 

 Does anybody have an answer to this problem? It seems that the
spectrum_sense is used in several projects, but how does it function?

 Thanks in advance.
   

adib_sairi wrote:
> 
> 
> 
> TMob wrote:
>> 
>> Hi,
>> 
>> I am currently trying to use the USRP to sense the 802.11 channels for
>> activity. So far, I am using the usrp_spectrum_sense to do this. Each
>> time I get the callback from gr.bin_statistics_f, I calculate the signal
>> power in the returned data vector using the following formula:
>> 
>>         for bin in m.data:
>>             signalPower +=
>> 20*math.log10(math.sqrt(bin)/tb.fft_size)-20*math.log10(tb.fft_size)-10*math.log(tb.power/tb.fft_size/tb.fft_size)
>>         signalPower /= tb.fft_size
>> 
>> According to previous posts, this should give me the signal power at the
>> given center frequency in dBm.
>> Unfortunately, it turned out that the values that I get using this code,
>> vary very much, e.g. with the FFT size and the gain. When I leave gain
>> and FFT size per default I get values from -28 through +5 (dBm) which
>> definitely does not correspond to dBm. Is there any mistake in the
>> formula? Is this really dBm that I get?
>> 
>> Because the usrp_fft.py example shows more realistic values (around -50 -
>> -60dBm) than the usrp_spectrum_sense.py, I was wondering if somebody
>> could explain how usrp_fft gets to these values. All I can see in the
>> source code there is that a USRP source is defined and connected to the
>> scope. But where is the conversion into dBm done? Can this be applied to
>> usrp_spectrum_sense somehow?
>> 
>> Thanks,
>> 
>> TMob
>> 
> 
> did anyone have the answer to this question? is it because of the
> usrp_fft.py have the windowing block? regarding the windowing block, do
> any body know why the blackmanharis is chosen?
> 
> Adib 
> 
> 

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