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
>

Reply by Vladimir Vassilevsky●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 cpshah99●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?
>

Can you explain your channel model? or how the impulse response looks?
Chintan

Reply by alberto.fuggetta●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 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 Vladimir Vassilevsky●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 alberto.fuggetta●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 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 steveu●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?
>> 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 cpshah99●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 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

Reply by Vladimir Vassilevsky●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?
> 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

Reply by alberto.fuggetta●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 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