Digital Signal Processing Reference
In-Depth Information
for optimizing channel capacity. The formulas are different because the first
term in Eq. (10.33) is not a constant. The construction in Eq. (10.33) can at
best be regarded as “pseudo” water filling.
Let
F ( e )
| 2 is nonzero, and
F
be the set of frequencies in [
π, π ] for which
|
c be the complementary set. From Eqs. (10.31) and (10.33) we have
p 0 = γ
F
let
F
S qq ( e ) S ss ( e )
|
2 π
S qq ( e )
2 π
H ( e )
| 2
|
H ( e )
| 2
F
so that
p 0 +
S qq ( e )
|
2 π
H ( e )
| 2
F
γ =
S qq ( e ) S ss ( e )
|H ( e ) | 2
,
(10 . 34)
2 π
F
An expression for the minimized error
E mse can be obtained by substituting Eq.
(10.33) into Eq. (10.30) which yields
S qq ( e ) S ss ( e )
|
+
F
1
γ
2 π
S ss ( e )
E mmse =
2 π ,
H ( e )
| 2
F
c
where the second term arises from the fact that when F ( e )=0,theintegrand
in Eq. (10.30) reduces to S ss ( e ). Substituting for γ from Eq. (10.34) yields
the final expression
S qq ( e ) S ss ( e )
|
2
2 π
+
H ( e )
| 2
S ss ( e )
2 π
F
E mmse =
(10 . 35)
p 0 +
S qq ( e )
|
2 π
F
c
H ( e )
| 2
F
Since the mean squared error has been minimized without the zero-forcing con-
straint, this is called the pure-MMSE solution.
10.3.2 MMSE transceiver with zero forcing (ZF-MMSE)
The zero-forcing constraint in Fig. 10.5 implies
F ( z ) H ( z ) G ( z )=1 .
(10 . 36)
In this case the reconstruction error is simply the noise q ( n ) filtered by G ( e ) .
The error spectrum is therefore
S qq ( e )
S ee ( e )= S qq ( e )
G ( e )
| 2 =
|
|
F ( e )
| 2 |
H ( e )
| 2
The mean squared reconstruction error is
E mse = π
−π
S qq ( e )
2 π
(10 . 37)
|
F ( e )
| 2 |
H ( e )
| 2
 
Search WWH ::




Custom Search