Figure 5.3 (a) Synthesizer for generating the signal si(t). (b) Analyzer for generating the set of signal vectors ?si?.

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Figure 5.3 (a) Synthesizer for generating the
signal si(t). (b) Analyzer for generating the set
of signal vectors ?si?.
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Figure 5.4 Illustrating the geometric
representation of signals for the case when N ? 2
and M ? 3.
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Figure 5.5 Vector representations of signals
s1(t) and s2(t), providing the background picture
for providing the Schwarz inequality.
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5.3 Conversion of the continuous AWGN channel
into a vector channel
Figure 5.2 Additive white Gaussian noise (AWGN)
model of a channel.
Figure 5.3 (a) Synthesizer for generating the
signal si(t). (b) Analyzer for generating the set
of signal vectors ?si?.
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Figure 6.46 Signal constellation for (a) M-ary
PSK and (b) corresponding M-ary QAM, for M ? 16.
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Figure 5.7 Illustrating the effect of noise
perturbation, depicted in (a), on the location of
the received signal point, depicted in (b).
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Figure 5.8 Illustrating the partitioning of the
observation space into decision regions for the
case when N ? 2 and M ? 4 it is assumed that the
M transmitted symbols are equally likely.
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Figure 5.10 Detector part of matched filter
receiver the signal transmission decoder is as
shown in Fig. 5.9b.
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Figure 5.11 A pair of signal constellations for
illustrating the principle of rotational
invariance.
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Figure 5.12 A pair of signal constellations for
illustrating the principle of translational
invariance.
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Figure 5.13 Illustrating the union bound. (a)
Constellation of four message points. (b) Three
constellations with a common message point and
one other message point retained from the
original constellation.
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