Hi Avi, nope, that's pretty specific, and I think the chances of someone else randomly having implemented exactly these methods are rather slim; I don't know of any implementation. However, BCJR is basically the basis of turbo decoding, and there's turbo decoders built-in to GNU Radio.
Best regards, Marcus On Wed, 2018-11-21 at 14:27 +0200, Avi Caciularu wrote: > Thanks for your reply. > What I actually meant is some kind of python implementation for one of the > following papers, for BPSK modulation: > https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4303066 > https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=297849 > > for linear ISI fir channel model. > > Thanks. > > On Wed, Nov 21, 2018 at 2:19 PM Müller, Marcus (CEL) <[email protected]> wrote: > > Hi Avi, > > > > I'm not quite sure what *exactly* you're looking for, i.e. if you're > > really after the EM algorithm to find a MAP / ML estimate of the > > channel coefficients, or whether you just want that channel estimate. > > > > I really like the gr-adapt [1] module of channel estimators, especially > > for its good documentation and examples, including recursive least > > square estimation. My estimation theory is a bit weak on that front, > > and I can't really tell you from the top of my head how EM compares to > > RLS etc. What I do know is that such algorithms typically make no > > guarantees on convergence rate¹; generally, Eigenvalue-based methods² > > behave more gracefully, and if I'm not completely mistaken, Karel's RLS > > belongs in that category. > > > > What's the reason you're asking for this? I'm not aware of EM being a > > common method for channel estimation, and from scrambling together my > > bits of random measurement theory/estimation theory knowledge and > > assembling the courage to say something about a field that I don't > > remotely feel confident talking about: you'd need to come up with a > > "coefficient likelihood function", something that takes in a very high- > > dimensional vector as argument, and which you iteratively improve with > > incoming data; that's basically a maximum likelihood parameter > > estimator in every iteration step? Feels like if you put knowledge into > > that ML step, you end up with a different form of parametric > > estimators. Cool stuff! But, and that's a honest question: why? > > > > Best regards, > > Marcus > > > > [1]https://github.com/karel/gr-adapt > > > > ¹ in fact, I'd expect that thing to only guarantee converging on a > > *local* minimum of error, not to the *global* one > > ² so-called spectral estimators, with "spectrum" as in "set of > > Eigenvalues", not so much as in "frequency domain". > > > > On Wed, 2018-11-21 at 10:50 +0200, Avi Caciularu wrote: > > > Does anyone know where I can find implementation of that? > > > _______________________________________________ > > > Discuss-gnuradio mailing list > > > [email protected] > > > https://lists.gnu.org/mailman/listinfo/discuss-gnuradio _______________________________________________ Discuss-gnuradio mailing list [email protected] https://lists.gnu.org/mailman/listinfo/discuss-gnuradio
