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

# Filters coefficients in symbol spaced DFE

Started by ●June 20, 2010

Reply by ●June 20, 20102010-06-20

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? > ThanksFeedback 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

Reply by ●June 20, 20102010-06-20

>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 stillhigh>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

Reply by ●June 20, 20102010-06-20

> > >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 stillhigh>> 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

Reply by ●June 20, 20102010-06-20

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 tothe>>> 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 > >

Reply by ●June 20, 20102010-06-20

steveu wrote:>> >>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.Feedforward coeffs ~ 0 -> error is not correlated with the signal -> adaptation process is not working right.> Steve >

Reply by ●June 21, 20102010-06-21

I tried with RLS instead of LMS but I always get bad results. Is there the possibility that the channel is so bad that any algorithm can work well with the DFE?> > >steveu wrote: > >>> >>>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 tothe>>>>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 aone>> sample per symbol equalizer to pull in. Trying 2 samples per symbolmight>> provide insight into the system's behaviour. > >Feedforward coeffs ~ 0 -> error is not correlated with the signal -> >adaptation process is not working right. > > > >> Steve >> >

Reply by ●June 21, 20102010-06-21

>I tried with RLS instead of LMS but I always get bad results. >Is there the possibility that the channel is so bad that any algorithmcan>work well with the DFE? >Can you explain your channel model? or how the impulse response looks? Chintan

Reply by ●June 21, 20102010-06-21

alberto.fuggetta wrote:> I tried with RLS instead of LMS but I always get bad results.Does your equalizer work at all, with a trivial channel, no spread? VLV

Reply by ●June 21, 20102010-06-21

Yes, it does. If I change the channel with a similar one, having same path delays and lower path gains, the equalizer has good performances. (BER = 10^-4 @ 12 dB)> > >alberto.fuggetta wrote: > >> I tried with RLS instead of LMS but I always get bad results. > >Does your equalizer work at all, with a trivial channel, no spread? > >VLV >