Hello all,
Any discussion over statistical toolbox?
Thank you.
Regards,Kartik Patel
On Wed, Feb 1, 2017 1:32 AM, Kartik Patel kartikpatel1...@gmail.com wrote:
Hi Marcus,
Sorry for replying late. I was travelling.
My point is we can have a statistical module for GNU Radio. Although Scipy has
extensive library available, we can have it's wrappers for GNU Radio. We can use
those wrappers in GRC. Basically, all major statistical analysis can be done at
GRC level instead of going to the python/c++ backend.
There are some fundamental statistical tools (can be extended with suggestions
from community): 1. generation of RV, 2. various distributions and distribution
fitting, 3. regressions 4. hypothesis testing (including non-parametric testing
which basically check whether current samples matches a particular distribution
or not) 5. parameter estimations. We will need various distributions/functions
from Scipy.
So, consider a scenario where we have a block of "random variable generators"
which will get input from a block called "distribution" which will specify the
distribution as well as it's parameters. There can be another block for
"distribution fitting". Which will take two inputs: vector of samples and input
from "distribution" block. Consider a hypothesis testing scenario: Get a input
vector: Provide a condition of testing (like energy of vector should be greater
than some value).Consider a testing mechanism where we test whether a sample
vector is taken from a distribution or not (aka non-parametric goodness-of-fit
based testing): It may take input from a "distribution block" and set of
samples. and based on value of some "false alarm probability", it will give the
decision.
We can try to make these testing completely generic. Like, you can write whole
equation in textbox in GRC (may be. need to see how can we do it). It's similar
to some blocks in Simulink (not sure exactly which one, but I remember those).
Note1: the "distribution" block will provide a distribution object. It may work
internally, or externally. That's debatable.Note2: This is a idea. We can
discuss on various implementation approaches once the scope of project etc are
discussed.
Regards,Kartik Patel
On Thu, Jan 26, 2017 11:51 PM, Marcus Müller marcus.muel...@ettus.com wrote:
Hi Kartik,
I heartily agree with you, you need a lot of random variables, but the question
is: in which shape?
Do you need the noise source to produce more different types of amplitude
distributions? Do you need those in the channel models?
"Blocks for hypothesis testing" sounds pretty interesting. Can you flesh out
that idea a little more? In my head, I'm not sure what a hypothesis is here.
Best regards,
Marcus
On 01/26/2017 05:24 PM, Kartik Patel wrote:
Hi Martin,
Till now, based on my experience in communication systems, I saw extensive need
of probability and random variables.
So, now, if we are considering GNU Radio to be a full-fledged communication
systems simulator, I think we can have wrappers of statistical analysis
functions of Scipy. We can have GRC blocks for the same.
So, for an example, for spectrum sensing applications, instead of writing a code
with Scipy library, we can have some blocks for direct hypothesis testing.
Regards, Kartik Patel
On Thu, Jan 26, 2017 4:07 PM, Martin Braun martin.br...@ettus.com wrote:
On 01/26/2017 12:07 AM, Kartik Patel wrote:
> Hi,
>
> I am not sure how relevant is this, but it's worth a consideration.
>
> Can we have a probability and statistical toolbox? It may include
> various probabilistic distributions, their random number generators,
> their PDFs and CDFs. These are very much useful in a communication
> system analysis. (Example: middleton noise etc. for simulations). Even
> adding various statistical functions like hypothesis testing,
> regressions, distribution fitting etc. can be added.
Sure, although scipy has pretty good ones already. Can you elaborate on
how this would be useful for GNU Radio specifically?
-- M
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