Hi Steve,
That's great! I've got almost the same results here, obtained in a
slightly different way. I carved the p1-rank, p2/p1 plane into 8 equal
ranges for each coordinate, and accumulated 8x8 histograms for three
cases: sym=mrsym, sym=mr2sym, and sym=neither. The first and last
histograms look just like your two, on a coarser scale.
Your result of 709/1000 for ntrials=10000 is very good news, too!
-- Joe
On 9/26/2015 4:49 PM, Steven Franke wrote:
> Joe -
>
> Just FYI - two plots attached. These are 2D histograms of the symbol
> rank and p2/p1 ratio for all symbols associated with successfully
> decoded files from my batch of 1000 JTSim files with SNR=-24dB. Note
> that each of these quantities (rank, p2/p1) is invariant under any
> multiplicative scaling factor applied to both p1 and p2.
>
> x-axis is the symbol's p1-rank. Lowest rank corresponds to highest p1.
>
> y-axis is p2/p1.
>
> The first plot, with yellow concentrated in the upper right is for
> symbols that were in error. The second plot, with yellow in the lower
> left is for symbols that are correct.
>
> Using the insight provided by these plots, I now have 709/1000 decodes
> for the batch of 1000 JTSim files at -24dB. This is using WSJT10, sfrsd,
> SFM and ntrials=10000. The erasure proabilities are assigned .by carving
> up the plane into 4 regions, and assigning the highest erasure
> probability to the upper right. That’s still without using the mr2sym’s.
>
> Getting closer! As time permits, I’ll see if I can reproduce these
> results using your sfrsd2 and rsdtest.
>
> Steve k9an
>> On Sep 26, 2015, at 10:27 AM, Joe Taylor <j...@princeton.edu
>> <mailto:j...@princeton.edu>> wrote:
>>
>> Hi Steve,
>>
>> On 9/26/2015 10:40 AM, Steven Franke wrote:
>>> Don’t worry about it Joe - I tried scaling up my metrics by a factor
>>> of 4 and then kvasd gave me 644 decodes. So it’s clear that kvasd
>>> wants bigger numbers. For now, I’ll just focus on trying optimize sfrsd…
>>>
>>> Your rsdtest looks like a good way to do quick runs to test sfrsd.
>>> At your leisure, can you point me to the s3_1000.bin file?
>>
>> With SVN revision 5929 I have remade the s3_1000.bin file into "stream"
>> format, which will make it easier to read in C. I posted a copy of the
>> file at
>> http://www.physics.princeton.edu/pulsar/K1JT/s3_1000.bin
>>
>>> I’m playing with “R” to do statistics on metrics. So far, it looks
>>> like the rank of p1 (index of p1 in a sorted list) and the ratio
>>> p2/p1 are the most powerful statistics for identifying symbols that
>>> are likely to be in error - at least for the SFM.
>>
>> Great! I am looking at similar things, but in a different (largely
>> qualitative) way. Will be interesting to see where this takes us.
>>
>>> Do you remember how you came up with the JTM? It looks like you
>>> use exp(x) to expand the distribution of the power associated with
>>> each symbol, and then normalize to the total exponentially-expanded
>>> power. Was this chosen empirically? Or based on a certain model
>>> for a fading channel?
>>
>> I think Ralf Koetter suggested the exp(x) to me; I think I also found it
>> in a Viterbi book. The detailed parameter choices were purely
>> empirical. We're not working with averaged transmissions yet, but the
>> dependence on "nadd" is intended to allow for different statistics of
>> elements of s3(i,j) when synchronized transmissions have been averaged.
>>
>> -- Joe
>>
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