9.4 One-Sided Z-Transform 521
RemarksThe uniqueness of the Z-transform requires that the Z-transform of a signal be accompanied by
a region of convergence. It is possible to have identical Z-transforms with different regions of convergence,
corresponding to different signals.
nExample 9.6
Letc[n]=α|n|, 0< α <1, be a discrete-time signal (it is actually an autocorrelation function
related to the power spectrum of a random signal). Determine its Z-transform.
Solution
To find its two-sided Z-transformC(z)we consider its causal and anti-causal components. First,
Z(c[n]u[n])=
∑∞
n= 0
αnz−n=
1
1 −αz−^1
with the region of convergence of|αz−^1 |<1 or|z|> α. For the anti-causal component,
Z(c[−n]u[n])z=
∑∞
n= 0
αnzn=
1
1 −αz
with a region of convergence of|αz|<1 or|z|<| 1 /α|.
Thus, the two-sided Z-transform ofc[n] is (notice that the term forn=0 was used twice in the
above calculations, so we need to subtract it)
C(z)=
1
1 −αz−^1
+
1
1 −αz
− 1 =
z
z−α
−
z
(z− 1 /α)
=
(α− 1 /α)z
(z−α)(z− 1 /α)
with a region of convergence of
|α|<|z|<
∣
∣
∣
∣
1
α
∣
∣
∣
∣
For instance, forα=0.5, we get
C(z)=
−1.5z
(z−0.5)(z− 2 )
0.5<|z|< (^2) n
9.4 One-Sided Z-Transform........................................................................
In most situations where the Z-transform is used the system is causal (its impulse response ish[n]= 0
forn<0) and the input signal is also causal(x[n]=0 forn< 0 ). In such cases the one-sided Z-
transform is very appropriate. Moreover, as we saw before, the two-sided Z-transform can be expressed