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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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1. Show that if M signals are linearly dependent, then sM(t) can be expressed in terms of Si(t):i = 1,..., M 1.
2. Continue this procedure until you obtain iV-linearly independent signals and M-N signals expressed in terms of them. N is called the dimension of the signal set.
3. Carry out the details of the Gram-Schmidt procedure described on p. 258.
Problem 4.2.8. Translation!Simplex Signals [18]. For maximum a posteriori reception the probability of error is not affected by a linear translation of the signals in the decision space; for example, the two decision spaces in Figs. P4.3a and P4.36 have the same Pr (e). Clearly, the sets do not require the same energy. Denote the average energy in a signal set as
1. Find the linear translation that minimizes the average energy of the translated signal set; that is, minimize
M-ary Signals
M
2 a^t) = 0.
?? f Pr (Ht) |s,|2 = J Pr (H,)Et **(/)*.
1=1 i=l JO
Additive White Gaussian Noise 381
<P2
Si
S2
*1
X
82
X
S3
S4
S3
s4
X
(a)
Fig. P4.3
(b)
2. Explain the geometric meaning of the result in part 1.
3. Apply the result in part 1 to the case of M orthogonal equal-energy signals representing equally likely hypotheses. The resulting signals are called Simplex signals. Sketch the signal vectors for M = 2, 3, 4.
4. What is the energy required to transmit each signal in the Simplex set?
5. Discuss the energy reduction obtained in going from the orthogonal set to the Simplex set while keeping the same Pr (c).
Problem 4,2.9. Equally correlated signals. Consider M equally correlated signals
* = h
I
1. Prove
1
M - 1
< p < 1.
2. Verify that the left inequality is given by a Simplex set.
3. Prove that an equally-correlated set with energy E has the same Pr () as an
orthogonal set with energy ?orth = E( 1 p).
4. Express the Pr () of the Simplex set in terms of the Pr (c) for the orthogonal set
and M.
Problem 4.2.10. M Signals, Arbitrary Correlation. Consider an M-ary system used to transmit equally likely messages. The signals have equal energy and may be correlated:
Pa = Jq st(t) sfi) dt, i,j = 1, 2,..M.
The channel adds white Gaussian noise with spectral height N0/2. Thus r(t) = VESi(t) + w(0, 0 < t < T: Hi9 = 1,..., M.
1. Draw a block diagram of an optimum receiver containing M matched filters. What is the minimum number of matched filters that can be used ?
2. Let p be the signal correlation matrix. The ij element is . If p is nonsingular, what is the dimension of the signal space?
3. Find an expression for Pr (*|#i), the probability of error, assuming #i is true. Assume that p is nonsingular.
4. Find an expression for Pr (c).
5. Is this error expression valid for Simplex signals ? (Is p singular ?)
382 4.8 Problems
Problem 4.2.11 (continuation). Error Probability [69]. In this problem we derive an alternate expression for the Pr (c) for the system in Problem 4.2.10. The desired expression is
1
Pt (.) - ~P (-30 j. ex [gjr)* *]
Develop the following steps:
1. Rewrite the receiver in terms of M orthonormal functions MO- Define
M
2
fc=
= 2 r* w*)-
fc=l
Verify that the optimum receiver forms the statistics

Jo
/* = f *( = 2
Jo fc = l
and chooses the greatest.
2. Assume that sm(0 is transmitted. Show
Pr (i\m) Pr (R in Zm)
(M M V
2 *">*** = max 2 fc=i * fc=i /
3. Verify that
_ 1 / ?\ exp [-(I/MO 2"=i
Pr(e) = A?65* r f) 2J. J.. mr*------------------------------
xexp (-J- max 2 *)- (P-2)
VVo ^ k=l '
4. Define
/(R) =exp {max [()" ? /A]}
and observe that (P. 2) can be viewed as the expectation of/(R) over a set of statistically independent zero-mean Gaussian variables, Rk, with variance No 12. To evaluate this expectation, define
and
Find p2(z). Define
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