mohatta2015.pdf
signal processing from power amplifier operation control point of view
signal processing from power amplifier operation control point of view
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MORE DETAILS 59<br />
1/c<br />
r m —►<br />
H<br />
i<br />
,r<br />
*\ J<br />
z<br />
ψ<br />
sign^j<br />
φ<br />
C x y*—~ ^e'ay Α —<br />
Figure 3.2<br />
ZF DFE block diagram.<br />
Now suppose we are told so = —1, the incorrect value. Then,<br />
« 1 = -0.1[l-9(-l)] = -1.0,<br />
(3.8)<br />
giving a detected value of s(l) — — 1, which is incorrect. Then,<br />
z 2 = -O.I3/2 = -0.1[-7 - 9(-l)] = -0.2,<br />
(3.9)<br />
giving s(2) = — 1, which is also incorrect. Thus, if we start with an incorrect value<br />
for so, we detect incorrect values for the remaining symbols. This is called error<br />
propagation.<br />
3.2 MORE DETAILS<br />
So how well does the ZF DFE work in general? It depends. If we have high<br />
input SNR (performance is limited by ISI) and the decisions are all correct (ISI is<br />
completely removed), then it works well. However, sometimes we make an incorrect<br />
decision. This causes an incorrect subtraction of ISI for the next symbol, increasing<br />
the chances of making a second incorrect decision, and so on.<br />
How does it compare to MF? If performance is noise limited (low input SNR), we<br />
actually do worse than MF because we don't collect all the signal energy together.<br />
Instead, we only keep the signal energy in r\ (—lOsi) and treat the term 9si in r 2<br />
as a nuisance to be subtracted later. Thus, at low SNR, MF will perform better.<br />
However, at high SNR, performance is limited by ISI. If the detected values are<br />
correct most of the time, ISI is reduced and DFE will perform better than MF.<br />
We can determine an upper bound on output SINR by assuming correct subtraction<br />
of ISI. In this case, y\ can be modeled as<br />
2/1 -lOsi + n\. (3.10)