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Detection, Estimation modulation theory part 1 - Vantress H.

Vantress H. Detection, Estimation modulation theory part 1 - Wiley & sons , 2001. - 710 p.
ISBN 0-471-09517-6
Download (direct link): sonsdetectionestimati2001.pdf
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J _ oo
The subscript emphasizes the estimator is unrealizable. We see that the operation inside the integral is a convolution, which suggests the block
Jr COS 03ct
r(t) f Sa(0>) au(t) _
'VU J Sa(u) + Jjt
Fig. 6.60 Demodulation with delay.
580 6.4 Linear Modulation: Communications Context
diagram shown in Fig. 6.60a. Using an argument identical to that in Section 6.4.1, we obtain the block diagram in Fig. 6.60b and finally in Fig. 6.60c.
The optimum demodulator is simply a multiplication followed by an optimum unrealizable filter.! The error expression follows easily:
This result, of course, is identical to that of the unmodulated process case. As we have pointed out, this is because linear modulation is just a simple spectrum shifting operation.
6.4.3 Amplitude Modulation: Generalized Carriers
In most conventional communication systems the carrier is a sine wave. Many situations, however, develop when it is desirable to use a more general waveform as a carrier. Some simple examples are the following:
1. Depending on the spectrum of the noise, we shall see that different carriers will result in different demodulation errors. Thus in many cases a sine wave carrier is inefficient. A particular case is that in which additive noise is intentional jamming.
2. In communication nets with a large number of infrequent users we may want to assign many systems in the same frequency band. An example is a random access satellite communication system. By using different wideband orthogonal carriers, this assignment can be accomplished.
3. We shall see in Part II that a wideband carrier will enable us to combat randomly time-varying channels.
Many other cases arise in which it is useful to depart from sinusoidal carriers. The modification of the work in Section 6.4.1 is obvious. Let
the succeeding steps are identical. If this is not true, the problem must be re-examined.
As a second example of linear modulation we consider single-sideband amplitude modulation.
,eo Sa(co)(N0/2P) dw
-CO Sa{CO) + No/IP 7
s(t, a(0) = ) ait).
Looking at Fig. 6.60, we see that if
c\t) a(t) = k[a(t) + a high frequency term]
t This particular result was first obtained in [46] (see also [45]).
Amplitude Modulation: Single-Sideband Suppressed-Carrier 581
6.4.4 Amplitude Modulation: Single-Sideband Suppressed-Carrierf
In a single-sideband communication system we modulate the carrier cos a)ct with the message a(t). In addition, we modulate a carrier sin a>ct with a time function a(t), which is linearly related to a(t). Thus the transmitted signal is
s(t, a(t)) = VP [a(t) cos a>ct a{t) sin u>ct]. (505)
We have removed the Vl so that the transmitted power is the same as the DSB-AM case. Once again, the carrier is suppressed.
The function a(t) is the Hilbert transform of a(t). It corresponds to the
output of a linear filter h(r) when the input is a(t). The transfer function
of the filter is
(-j, <o > 0,
H(jaj) = io, <o = 0, (506)
1+j> < 0.
We can show (Problem 6.4.2) that the resulting signal has a spectrum that is entirely above the carrier (Fig. 6.61).
To find the structure of the optimum demodulator and the resulting performance we consider the case Tf = oo because it is somewhat easier. From (505) we observe that SSB is a mixture of a no-memory term and a memory term.
There are several easy ways to derive the estimator equation. We can return to the derivation in Chapter 5 (5.25), modify the expression for ds(t, a(t))jdAr, and proceed from that point (see Problem 6.4.3). Alternatively, we can view it as a vector problem and jointly estimate a(t) and a(t) (see Problem 6.4.4). This equivalence is present because the transmitted signal contains a(t) and a(t) in a linear manner. Note that a state-
Fig. 6.61 SSB spectrum.
t We assume that the reader has heard of SSB. Suitable references are [47] to [49].
582 6.4 Linear Modulation: Communications Context
variable approach is not useful because the filter that generates the Hilbert transform (506) is not a finite-dimensional dynamic system.
We leave the derivation as an exercise and simply state the results. Assuming that the noise is white with spectral height N0/2 and using (5.160), we obtain the estimator equations
a(t) = ^ J ^ Vp |cos wcz Ra(t, z) - sin wcz [J* h(z - y)Ra(t,y) rfyj | {;r(z) VP [ia(z) cos a)cz a(z) sin a>cz]} dz (507)
a(t) = jjr ^ VP jcos <ocz [j ^ h{t - y)Ra(y, z) dyJ - sin cocz Ra(t, j)|
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