On Sat, 21 Oct 2006 10:04:34 -0500, "Richard_K" <ngyh80@hotmail.com>
wrote:
>
>Is fractionally spaced equalizer (FSE) applicable to the linear adaptive
>transversal filter? I noticed that most of the FSE is applied for the
>decision feedback equalizer (non-linear).
>
Interestingly FSE is only applicable to the FF (feed-forward) section
of an equalizer and not the FB (feedback) section. The FB section of a
DFE needs actual symbols and there are no fractional symbols so FB is
a symbol rate filter.
>May I know what are the advantages of using FSE over symbol spaced
>equalizer?
FSEs are usually more tolerant of sampling phase errors ie a symbol
rate equalizer needs almost perfect sampling in the center of the eye
for good performance but an FSE can do reasonably well without that
requirement as it has more than one sample per symbol.
But of course there is no free lunch and FSE has its own distinct
problems specifically a tendency for tap wander.
Also without being as curt as some other poster, I do also suggest
that you get acquanted with the basics first from a couple of good
books. Sayed's Adaptive filtering book is very good for these topics.
Kal
Reply by Vladimir Vassilevsky●October 21, 20062006-10-21
Richard_K wrote:
> Is fractionally spaced equalizer (FSE) applicable to the linear adaptive
> transversal filter? I noticed that most of the FSE is applied for the
> decision feedback equalizer (non-linear).
>
> May I know what are the advantages of using FSE over symbol spaced
> equalizer?
My dear Richard K,
Before bothering about the advanced concepts, you should learn the ABC
basics first. The classic book of Proakis will help you.
Vladimir Vassilevsky
DSP and Mixed Signal Design Consultant
http://www.abvolt.com
Reply by Richard_K●October 21, 20062006-10-21
Is fractionally spaced equalizer (FSE) applicable to the linear adaptive
transversal filter? I noticed that most of the FSE is applied for the
decision feedback equalizer (non-linear).
May I know what are the advantages of using FSE over symbol spaced
equalizer?
Thanks.