6.4. Case Study: Improving Range of UWB Using RAKE Receivers
The fine time resolution of multipaths induced by the channel (typically in the order of 100300 picoseconds) can be exploited using a RAKE receiver to capture a significant amount of energy found in the multipath components, and to benefit from multipath diversity gain. Energy capture is important in UWB receiver design, and the number of detectable multipath
components
could be as large as a few hundred since UWB signals occupy bandwidth greater than 500 MHz or have a
fractional
bandwidth greater than 0.25. Clearly, classical RAKE receiver design solutions, such as MRC or selection combining, are no longer appropriate from the viewpoint of complexity and performance trade-offs. The realistic trade-off between the energy capture and RAKE diversity level in dense multipath for UWB systems have been quasi-analytically/experimentally presented in [54]. The experimental results are shown in Figure 6.56.
S
OURCE
: M. Z. Win and R. A. Scholtz, "On the Energy Capture of Ultrawide Bandwidth Signals in Dense Multipath Environments,"
IEEE Communications Letters
[54]. IEEE, 1998. Used by permission.
In this section, we examine the throughput range trade-off of UWB communication systems that
employ
two variants of RAKE receiver structures in multipath fading channels: (a) the generalized selection combining receiver (hereafter, referred to as GSC(
N,L
)) that combines a subset of
N
resolvable multipaths with the highest instantaneous SNR out of
L
resolvable multipaths in a similar fashion to MRC; and (b) MRC with finite tap statistics or partial MRC (hereafter, referred to as PMRC(
N, L
)) that combines
N
multipaths with the largest mean SNR. Figures 6.57 a and b show the schematic for these receiver structures respectively.
Clearly, PMRC is simpler to implement compared to GSC owing to the simpler selection
circuitry
complexity (i.e., the need to select
N
paths with the largest instantaneous SNR in the latter). The motivation for performance analyses of the previous two suboptimal receiver structures is based on the propagation measurements of UWB signals at Virginia Tech [84] that indicates the significant amount of energy that is found in a few dominant multipaths, particularly for the LOS case. For example, approximately 80% of the receiver energy could be captured by combining only 12 dominant multipaths. However, for the NLOS situation, approximately 75% of energy is captured by combining 30 dominant multipaths.
In [85], the performance comparisons between GSC and PMRC over Nakagami-
m
channels have been studied in the context of spread spectrum communications but with only a few multipaths. In [86], the authors resorted to a semianalytical approach for analyzing the average bit error rate (ABER) performance of GSC(
N,L
) and PMRC(
N, L
) at a fixed distance. In contrast, we now examine the throughput range performance of UWB for two different reduced-complexity receiver structures over generalized fading channels using exact and approximate ABER expressions (rather than the semianalytical approach discussed in [86]).
We assume a UWB system operating in a Rayleigh or a Nakagami-
m
fading environment and present analytical expressions for the throughput as a function of the distance for the GSC(
N,L
) and PMRC(
N, L
) receiver structures. The bit error probability for an
M
-ary PAM system is given by
Equation 6.153
where
M
is the alphabet
size
and
c
= log
2
M
denotes the number of information bits carried by each symbol. The instantaneous SNR/bit is
g
= (
E
b
/N
)
a
2
where
E
b
denotes received energy per bit,
N
o
is noise spectral density, and
a
denotes random fading amplitude. The average SNR per bit at the receiver input can be computed as
, where
P
r
is the average received signal strength and
T
b
denotes the bit duration (time between transmitted
pulses
). The received power at a distance
d
from the transmitter can be
modeled
as
Equation 6.154
where
n
denotes the
path
loss exponent,
L
s
is the system loss factor not
related
to propagation (
L
s
1),
G
t
,
G
r
} denotes the transmitter and receiver antenna gains, respectively. Using those expressions, it can be shown that the maximum achievable throughput,
R
b
, at distance
d
, is given by
Equation 6.155
The noise spectral density is
N
=
kTF
where
k
is Boltzman's constant,
T
is system temperature, and
F
denotes the noise figure. From (6.155) it is apparent that for fixed transmit power, throughput is a function of
d
n
. The average SNR per bit required to evaluate (6.155) for a given bit error rate performance can be found by averaging (6.153) over the probability distribution function (PDF) of GSC(
N
,L) or PMRC(
N
, L).
6.4.1. GSC(
N,L
) with Independent but Nonidentically Distributed Fading Statistics
Exact Analysis of GSC(
N,L
)
Using the moment-generating function (MGF) approach, we obtain
Equation 6.156
where
f
g
(.)
denotes the MGF of the GSC(
N
,L) output SNR,
g
gsc
. The MGF expressions for the GSC(
N
,L) output SNR in a myriad of fading environments has been developed in [87,88], namely
Equation 6.157
where
Equation 6.158
while
S
L
is the set of all
permutations
of integers [1, 2
, . . ., L
], and
s
s
= [
s
(1),
s
(2), . . ., (
L
)] which permutes the integers [1, 2, . . .,
L
]. In (6.157),
f
s
(
k
)
(.),
F
s
(
k
)
(.), and
f
s
(
k
)
(.) denote the PDF, cumulative distribution function (CDF), and the MGF of SNR of the
s
k
th
multipath, respectively.
Approximate Analysis of GSC(
N,L
)
The throughput range performance of UWB systems
employing
GSC receivers can be
analyzed
using the previous equations. However, as the number of resolvable multipath components
increases
, it becomes
computationally
intensive
to evaluate (6.156) along with (6.157), and turns out to be
impractical
for the case
L
15. As such, an approximate ABER formula is needed to predict the performance of GSC(
,
m
(
K
+ 1) and
in the analytical expression for independent and identically distributed multipath components. This hypothesis is further
validated
by the numerical results presented in the following subsection.
6.4.2. PMRC(
N, L
) with Independent but Nonidentically Distributed Fading Statistics
It is not difficult to show that the MGF of PRMC (
N, L
) output SNR may be computed as
Equation 6.159
where
f
g
k
(.) denotes the MGF of the
k
th
strongest (mean SNR) diversity path. Hence, the ABER performance of PMRC may be analyzed using (6.156) and (6.159), and the maximum achievable throughput can be easily computed as before.
6.4.3. GSC(
N,L
) with Equally Correlated Nakagami-
m
Fading Statistics
In order to gain significantly from the use of diversity, there must be a sufficient level of statistical independence in the fading of the received signal in different diversity paths. In UWB communication systems with closely received resolvable multipath components, an assumption of independent multipath signals may lead to grossly overestimated diversity gains. As such, a study of the effect of multipath correlation on the performance of a UWB communication system is important. In this section, we will provide analytical expressions to evaluate the ABER performance of the GSC(
N,L
) receiver structure in equally correlated Nakagami-
m
multipath fading channels.
Let
g
(1)
, . . .,
g
(
L
) denote the instantaneous SNRs in
ascending
order, namely
g
(1)
<
g
(2)
< . . . <
g
(
L
)
. Using the property of exchangeability, the joint PDF of the ordered set of instantaneous SNR can be
expressed
as
Equation 6.160
The joint PDF of the ordered instantaneous SNRs in a Nakagami-
m
channel can be obtained using Equation 21 of [90] and (6.160) as
Equation 6.161
where
m
1/2 denotes the fading severity index,
r
denotes the power correlation coefficient
,
v
k
=
l
k
+
m
,
, and
g
k
(., ., .) is defined as
Equation 6.162
It is apparent from (6.161) that we can compute the MGF
f
g
gsc
(.) in a similar fashion to GSC with i.n.d fading statistics [88], namely
Equation 6.163
where
I
(
., .
) is defined as
Equation 6.164
where
,
Using the identity in Equation 3.381.3 of [91], functions
G
k
(.) and
f
k
(
., .
) may be computed in closed form as
Equation 6.165
Equation 6.166
where
denotes the complementary incomplete Gamma function. It may be noted that (6.163) is much more
concise
, yet more general and
numerically
efficient, than Equation 36a of [90]. Now the throughput performance can now be easily analyzed using (6.155), (6.156), and (6.163).
The results discussed in this section use the assumptions in Table 6.2. Figure 6.58
compares
the accuracy of "approximate ABER analysis" against the "exact ABER analysis" using (6.157) for a GSC(
N,
12) receiver with i.n.d diversity branches in Rayleigh fading channels. It is evident that the throughput curves corresponding to approximate ABER match closely with the exact throughput curves. Readers are referred to [92] for further details.
Table 6.2. Assumptions on the System Parameters for Generating Numerical Results.
|
Transmit Power spectral d
|
41 dBm/Mhz
|
|
Transmitter Antenna Gain
|
0 dBi
|
|
Receiver Antenna Gain,
G
|
0 dBi
|
|
System Loss,
L
s
|
5 dB
|
|
Noise Figure,
F
|
6 dB
|
|
Path-loss exponent,
n
|
1.6 (LOS)
2.7 (NLOS)
|
|
Operating Bandwidth,
B
s
|
2.5 GHz
|
|
Center Frequency,
f
c
|
3.75 GHz
|
S
OURCE
: S. Gaur and A. Annamalai, "Improving the Range of Ultra Wideband Transmission using RAKE Receivers,"
Proc. IEEE Vehicular Technology Conference
[92]. IEEE, 2003. Used by permission.
Figure 6.59 draws a comparison between the throughput performance of GSC(
N
, 30) and PMRC(
N,
30) receiver structures in a Rayleigh environment with an exponentially decaying multipath intensity profile (
d
= 0.2). The average SNR of the
n
th
diversity path is
, where
denotes the average SNR/bit, and the parameter
Q
is
chosen
such that the constraint
is satisfied.
S
OURCE
: S. Gaur and A. Annamalai, "Improving the Range of Ultra Wideband Transmission using RAKE Receivers,"
Proc. IEEE Vehicular Technology Conference
[92]. IEEE, 2003. Used by permission.
Solving for C yields
Equation 6.167
As expected, the GSC receiver outperforms PMRC for a fixed number of combined paths,
N
. For instance, GSC(10,30) and PMRC(20,30) have identical throughput performances. From Figure 6.59 it is also apparent that as the number of combined paths
N
are increased, the throughput performance of PMRC becomes virtually identical to GSC. Based on these observations, we can conclude that deployment of a GSC(
N,L
) receiver is desirable for the cases in which
N << L
, whereas for
N
N,L
). Figure 6.60 depicts the performance of the 5-tap PMRC(5, 20) RAKE receiver in a Nakagami-
m
fading channel for varying fade distributions. It is evident that improving channel conditions
translates
into a considerable improvement in the receiver throughput. Figure 6.61
presents
the throughput analysis for a GSC(
N,
20) receiver for path loss exponent
n
= 3.5. A
comparative
study of Figures 6.60 and 6.61 suggests a major fallback in throughput with even the slightest increase in the path loss exponent
n
. The rate at which the throughput
falls
declines gradually as the range increases. Finally, Figure 6.62 illustrates the effect of multipath
correlations
on the throughput performance of the GSC receiver for different
numbers
of combined paths,
N
. The throughput performance degrades with increasing multipath correlation, as expected. Throughput falls by almost 50% for
r
= 0.8.
S
OURCE
: S. Gaur and A. Annamalai, "Improving the Range of Ultra Wideband Transmission using RAKE Receivers,"
Proc. IEEE Vehicular Technology Conference
[92]. IEEE, 2003. Used by permission.
S
OURCE
: S. Gaur and A. Annamalai, "Improving the Range of Ultra Wideband Transmission using RAKE Receivers,"
Proc. IEEE Vehicular Technology Conference
[92]. IEEE, 2003. Used by permission.
|