mohatta2015.pdf

176 PRACTICAL CONSIDERATIONS
adaptive MMSE linear equalization
direct adaptation
indirect adaptation
parametric estimation
nonparametric estimation
Figure 8.1
Design choiera for adaptive MMSE LE.
Which adaptation approach we should use depends on several things. First, it
depends on the transmit signal structure. Some systems are designed with a certain
adaptation approach in mind. For example, periodic placement of pilot symbol
clusters is convenient for indirect adaptation, as the channel can be estimated at
each pilot symbol cluster and interpolated over the intervening intervals. A second
consideration is performance, which tends to favor indirect adaptation. When the
channel is varying rapidly, it can be easier to track the channel coefficients rather
than the equalizer weights. A third consideration is complexity, which tends to
favor direct adaptation.
So far, we've focused on MMSE linear equalization. For DFE, we can use direct
or indirect adaptation to determine the forward filter and backward filter weights.
For MLSD, MAPSD, and MAPPD, we can use channel estimation and noise power
estimation (for MAPSD and MAPPD) to obtain the necessary parameters.
8.2.1.1 Channel quality In addition to parameters needed to equalize the received
signal, one may also need to estimate channel quality. While there are various
definitions of channel quality, we are interested in the definition that includes the
effects of the equalizer. For example, channel quality could be defined as the output
SINR of a linear equalizer.
Estimating channel quality is important because the receiver may need to feed
back such information to the transmitter, in essence telling the transmitter how
well the equalizer is doing (or will do in the future). This information can be used
at the transmitter to adapt the transmit power (power control) or the data rate
(rate adaptation).
8.2.2 Equalizer selection
So we have learned about MF, LE, DFE, MLSD, MAPSD, and MAPPD. Which
one should we build? There is no easy answer, but there are basically two things
to consider: performance and complexity. Complexity translates into cost, size and
power consumption of the device you are building. Performance translates into
coverage (at which locations will the receiver work) for services like speech and
data rate (how fast the data is sent) for services like Internet web browsing. In
general, the better an equalizer performs, the more complex it is. Thus, there is a
trade-off between performance and complexity.