Filters coefficients in symbol spaced DFE
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From: alberto.fuggetta on 20 Jun 2010 08:23 Hi, I'm trying to equalize a channel with sever multipath using a DFE (12,12) with LMS adaption algorithm. The relative power of the replicas are quite high w.r.t the main path. (max -4 dB). The equalizer is catastrophic. From the learning curve analysis I can observe that the error is still high after processing the training sequence. Morover, the forward filter coefficients are very small compared to the feedback filter ones (10^-3 vs 0.2). Is there any conclusion I can draw from these info? Thanks Alberto
From: Vladimir Vassilevsky on 20 Jun 2010 11:09 alberto.fuggetta wrote: > Hi, > > I'm trying to equalize a channel with sever multipath using a DFE (12,12) > with LMS adaption algorithm. > The relative power of the replicas are quite high w.r.t the main path. (max > -4 dB). The equalizer is catastrophic. > From the learning curve analysis I can observe that the error is still high > after processing the training sequence. > Morover, the forward filter coefficients are very small compared to the > feedback filter ones (10^-3 vs 0.2). > Is there any conclusion I can draw from these info? > Thanks Feedback path adaptation is nasty nonlinear problem. Your filter either falls into a local minimum or the adaptation is unstable. Vladimir Vassilevsky DSP and Mixed Signal Design Consultant http://www.abvolt.com
From: cpshah99 on 20 Jun 2010 12:15 >Hi, > >I'm trying to equalize a channel with sever multipath using a DFE (12,12) >with LMS adaption algorithm. >The relative power of the replicas are quite high w.r.t the main path. (max >-4 dB). The equalizer is catastrophic. >From the learning curve analysis I can observe that the error is still high >after processing the training sequence. >Morover, the forward filter coefficients are very small compared to the >feedback filter ones (10^-3 vs 0.2). >Is there any conclusion I can draw from these info? >Thanks > >Alberto > Check the eigenvalue spread of the channel. If it is very high then LMS will not perform well. Try to use RLS and see if you get any better performance. Refer to Proakis Comms or Haykin's Adaptive Filter Theory book. Chintan
From: steveu on 20 Jun 2010 12:42 > > >alberto.fuggetta wrote: > >> Hi, >> >> I'm trying to equalize a channel with sever multipath using a DFE (12,12) >> with LMS adaption algorithm. >> The relative power of the replicas are quite high w.r.t the main path. (max >> -4 dB). The equalizer is catastrophic. >> From the learning curve analysis I can observe that the error is still high >> after processing the training sequence. >> Morover, the forward filter coefficients are very small compared to the >> feedback filter ones (10^-3 vs 0.2). >> Is there any conclusion I can draw from these info? >> Thanks > >Feedback path adaptation is nasty nonlinear problem. Your filter either >falls into a local minimum or the adaptation is unstable. Or maybe his symbol timing has not been locked down well enough for a one sample per symbol equalizer to pull in. Trying 2 samples per symbol might provide insight into the system's behaviour. Steve
From: alberto.fuggetta on 20 Jun 2010 14:09
Hi Steve, already tried with 2 samples per symbol but the result does not change. :-( I also tried computing the received samples autocorrelation matrix, just multiplying the samples vector for its complex conj. Is it correct? >> >> >>alberto.fuggetta wrote: >> >>> Hi, >>> >>> I'm trying to equalize a channel with sever multipath using a DFE >(12,12) >>> with LMS adaption algorithm. >>> The relative power of the replicas are quite high w.r.t the main path. >(max >>> -4 dB). The equalizer is catastrophic. >>> From the learning curve analysis I can observe that the error is still >high >>> after processing the training sequence. >>> Morover, the forward filter coefficients are very small compared to the >>> feedback filter ones (10^-3 vs 0.2). >>> Is there any conclusion I can draw from these info? >>> Thanks >> >>Feedback path adaptation is nasty nonlinear problem. Your filter either >>falls into a local minimum or the adaptation is unstable. > >Or maybe his symbol timing has not been locked down well enough for a one >sample per symbol equalizer to pull in. Trying 2 samples per symbol might >provide insight into the system's behaviour. > >Steve > > |