an overview o modelling of the ultra wideband indoor channels
TRANSCRIPT
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An Overview of Modeling of Ultra Wide Band
Indoor Channels
Z. Irahhauten, H. Nikookar and G. Janssen
Center for Wireless Personal Communications (CWPC)
Department of Electrical Engineering, Mathematics and Computer Science-Delft University of Technology
Mekelweg 4, 2628 CD Delft, The Netherlands
Phone:+31-15-278 13 89, Fax: +31-15-278 40 46
Email: {Z.Irahhauten, H.Nikookar, G.Janssen}@EWI.tudelft.nl
Abstract In this paper an overview of the reported param-eters of the Ultra Wide Band (UWB) indoor wireless channelis presented. First, an introduction to UWB technology as well
as UWB wireless channels is provided. Then, impulse responsemodel for the wireless indoor channel is introduced. The availableUWB channel measurements results in indoor environments areconsulted and accordingly, the major UWB channel parametersare presented and compared to those of conventional narrowband(i.e. narrowband as well as wide-band) systems (CNS). Thenovelty of this work is related to gathering different UWBwireless channel parameters, analysis and comparison, leadingto a conclusion on modeling of impulse radio channel.
I. INTRODUCTION
The world is now in a stage of major telecommunications
revolutions. The need for multimedia communications and
new flexible communication capabilities with high data rates
and high Quality of Service (QoS) requirements becomeincreasingly important. To fulfill these demands, advanced
research is needed in the field of communications. New
wireless communication systems based on UWB technology
have been introduced recently. The Federal Communication
Commissions (FCC) recognized the significance of UWB
technology in 1998 and initiated the regulatory review process
of the technology. Consequently, FCC authorized the UWB
technology for commercial uses with different applications,
different operating frequency bands as well as the transmitted
power spectral densities.
Generally, UWB communications is based on the transmis-
sion of very short pulses with relatively low radio energy. It has
been in use for military applications and it may see increased
use in the future for wireless communications and ranging
according to its fine time resolution and its material penetration
capability. UWB radio signals occupy a bandwidth more than
the 25% of the center frequency [1]. This large bandwidthallows a very high capacity and accordingly, high processing
gains that allow access of large number of users to the system.
Meanwhile, since UWB can be a carrierless (i.e. baseband)
radio technology, it requires no mixer. Therefore, the imple-
mentation of a such system can be made simple, which means
that low cost transmitters/receivers can be achieved when
compared to the conventional radio frequency (RF) carrier
systems.Several ways exist to build a model of the mobile radio
propagation channel. One major way, which is concentrated
in this paper, is to use stochastic methods, which describe the
random behaviour of the UWB wireless channel at any time
and for different propagation environments using a statistical
approach.
The structure of the paper is as follows. In section II the
impulse response model of UWB channel is introduced, and
relevant channel parameters are presented based on the results
of UWB measurements reported in the literature. Comprehen-
sive comparison and analysis of those channel parameters of
UWB and CNS is given in section III. Concluding remarks
appear in section IV.
II. UWB MEASUREMENTS AND MODELS
The UWB wireless channel can be fully described by its
time-variant impulse response function h(t, ), which can beexpressed as follows:
h(t, ) =Nn=1
an(t)(t n(t))ejn(t) (1)
where is the delay, t refers to the impulse response at instantt and is the Dirac delta function. The parameters of thenth path an , n , n and N are amplitude, delay, phase andnumber of multipath components, respectively. When UWB is
a baseband signal, the phase in equation 1 can be kept out of
consideration. The recent results of UWB measurements and
channel modeling can be found in [2][9]. In the following,
important models for UWB channel parameters are discussed.
Power delay profile
The average received power as function of the excess
delay is called Power Delay Profile (PDP). UWB
measurements performed in an office building show that
the PDP is an exponential decreasing function with the
delay [2], [3]. The Time Decay Constant (TDC) seems
to follow a Lognormal distribution with mean of39.8 nsand standard deviation of 1.2 dB in an office building [2].Moreover, the reported results in [2], shows that the
mean TDC is 29 35 ns and 41 56 ns for LOS andNLOS, respectively. However, the same author of [2],
introduced another model refers to double exponential
model based on clustering to characterize the PDP inUWB channel [4] and the corresponding parameters
are shown in Table I. Furthermore, the reported results
of [9] show that double exponential decay model seems
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to match the UWB channel measurements for LOS as
well as NLOS propagation. The UWB measurements
performed in corridor show also that cluster phenomenon
can be observed [5].
Arrival times
The arrival times of the multipath components for UWB
seem to follow a negative exponential distribution andthe arrival rate of the multipath components is found to
be 1/(2.3 ns) [4]. The number of multipath componentsarriving during an interval of maximum excess delay
Tmax is equal to N = .Tmax with is the meanarrival rate of multipath components. This parameter is
carefully investigated for different bin resolutions [10].
The number of multipath increases when the resolution
increases. The distribution of the number of path is also
examined and Rayleigh distribution gives the best fit
with standard deviation of = 7 and = 30 paths forLOS and NLOS, respectively [10].
RMS delay spread
The rms delay spread (RDS) parameter is a good measure
of multipath spread because it determines the frequency
selectively of the channel fading, which degrades the
performance of digital communication systems over radio
channels [11]. The RDS limits the maximum data trans-
mission rate, which can reliably supported by the channel.
In the case without using of diversity or equalization, the
RDS is inversely proportional to the maximum usable
data rate of the channel [12]. The RDS is defined as:
rms =
2 ()2 (2)
where and 2 are the first and the second momentsof the power delay profile respectively, and are defined as:
=n
a2nn /n
a2n =n
P(n)n /n
Pn(n)
(3)
where an, Pn, and n are amplitude, power anddelay, respectively. The results of UWB measurements
show that RDS follows a Normal distribution with
mean = 4.7 ns and = 8.2 ns and standarddeviation = 2.3 and = 3.3 for LOS and NLOS,respectively [6]. However, different statistics of RDS
found in [3] show mean = 812 ns and = 1419 nsand standard deviation = 0.3 1 and = 1 5 forLOS and NLOS cases, respectively. In [10], RDS values
of respectively, 9 ns and 11.5 ns for LOS and NLOSare reported.
Fading
A radio designer should know the fading margin, which
needs to be taken into account in the selection of
transmit power. The fading margin in UWB is found
to be 5 dB [7] and the same result is found in [10].The measurement results reported in [5] show that the
fading can be modeled by a Rice distribution with
Rice factor of 9 dB. The results of [4] show thatfading follows a Rayleigh distribution. However, in [2]
the Gamma distribution is reported to give the best
fit for the amplitude distribution. In [9], the empirical
distribution of path amplitudes is computed using a
Kolmogorov-Smirnov test with 1% significance level,and the results show that Lognormal distribution gives
the best fit when compared to the Rayleigh distribution
for both LOS and NLOS cases.
Correlation
Wireless communication systems usually suffer formmultipath fading caused by propagation mechanisms such
reflection, diffraction and scattering due to the obstacles
in the channel. To combat this problem, techniques like
diversity are used. This technique is more efficient in
the case where the received signals are independent,
but if the received signals are correlated, this leads to a
limitation of the performance of the system [13]. Two
types of correlation can be distinguished in the wireless
channel, temporal and spatial correlation. The former
means the correlation between multipath components
arriving at the same profile but at different delays. The
last means the correlation between multipath component
collected at different profile but at the same delay [14].
The temporal correlation is investigated in [2] between
powers of the multipath components and the results
show that the correlation coefficient does not exceed 0.2for UWB. The spatial correlation of UWB signals is
investigated in [15]. The results show that the higher the
separation distance, the lower the spatial correlation and
the correlation is higher for LOS than NLOS propagation.
Path loss
For developing a good UWB communication system, a
radio designer must know the path loss (PL) in order
to determine the coverage area of the system. Path lossis defined as the ratio of the transmit signal power
to the receive signal power [16]. Based on the UWB
measurements in [2], the path loss exponent and the
standard deviation of shadowing are 2.4 and 5.9 dB,respectively, and the best model for the shadowing is
the Lognormal distribution. Different values for the path
loss exponent are reported in [6] as 1.7 and 3.5 as wellas for the standard deviation of shadowing as 1.6 dBand 2.7 dB for LOS and NLOS, respectively. Accordingto [10], the path loss exponent and the standard deviation
of the shadowing for LOS are 1.7 and 1.5 respectively,
and for NLOS are 4.1 and 3.6, respectively. The relationbetween RDS and path loss is investigated in [6], [8].The results show that the higher the RDS, the higher the
PL.
III. COMPARISON AND ANALYSIS
The PDP in UWB decreases exponentially with the delay
because later paths experience more attenuation, and also the
possibility of reflection. Due to strong reflectors situating in
the channel, the PDP can follow a double exponential function.
The same model is found for CNS [17], [18] (see Table I).
According to Table I, the arrival rate of multipath components
is remarkably higher for UWB than for CNS. This is due to the
high time resolution in UWB, which means that more distinctpaths can be detected as illustrated in Figure 1 where BWrefers to the bandwidth.
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Received
signal
excess delay
1/BWCNS
1/BWUWB
Fig. 1. Illustration of the bandwidth (i.e. resolution) effect on the detectionof multipath components.
The statistics of RDS for UWB seem to be smaller to those
of CNS reported in [14]. This is due smaller maximum excess
delay as well as smaller TDC of the clusters in the UWB case.
The fading margin is much smaller in UWB than the
fading margin of CNS, which is about 25 dB. This is becausea large number of received pulses do not interfere with eachother due to the large bandwidth occupied by UWB signals
(see Figure 1). The condition for destructive interference
between pulses in indoor propagation environment can
be found in [19]. The reported Rice factor is remarkably
small, and consequently, the Rice distribution converges to
the Rayleigh distribution. However, since the number of
multipath components arriving at a same delay (i.e. same bin)
is remarkably small for UWB, the vector summation of the
signals at that same delay gives less amplitude fluctuations,
and the modeling of the fading with the Rayleigh distribution
seems inappropriate. Therefore, some distribution functions
are introduced to characterize the small-scale rapid fadingvariations in UWB indoor environments such as POCA
(POlydorou-CApsalis) [20] and NAZU (Nakagawa-Arita-
Zhang-Udagawa) [21] for the case of NLOS and LOS,
respectively.
Different reported path loss exponents are perhaps due to
different types of the environments in where the measurements
are performed. Additionally, the Lognormally distributed stan-
dard deviations of shadowing are smaller for UWB when com-
pared to those of CNS, which are found 312 dB [22]. Thiscan explained by the fact that UWB has a large bandwidth,
which allows high capability of penetration trough the walls.A comparison of main UWB and CNS channel parameters are
summarized in Table II.
IV. CONCLUSION
An overview of the reported UWB measurement results and
channel modeling for indoor propagation environments in the
literature was given. The UWB indoor wireless channel can
be described by the impulse response model. The reported
results show that the received UWB signal power decreases
exponentially with the excess delay. The double exponential
model can also be used to describe the PDP. The time decay
constant depends on the type of the environments. The arrival
times of the multipath components are negative exponentiallydistributed. The arrival rate of multipath components is higher
for UWB than CNS due to high time resolution. The re-
ported results indicate a limited correlation between powers
ParameterWin Model
[4]
UWB CNS
Saleh -
Valenzuela [17]
TDC of clusters
(ns)
1/2.30
1/45.5
84.1
27.9
Intraclusterarrival rate (ns-1)
Cluster arrival
rate (ns -1)
TDC within cluster
(ns)
1/5
1/300
20
60
Spencer et. al [18]
Building 2Building 1
7833.6
6.605.10
17.316.8
82.228.6
Intel Model
[9]
1/0.5
1/60
1.6
16
TABLE I
PDP DOUBLE EXPONENTIAL MODEL FOR THE UW B AND THE CNS.
UWB CNS
LOS
RDSNLOS
4-12 (ns)
8-19 (ns)
10-100 (ns)
up to 200 (ns)
Channel parameter
Fading margin
Fading
distribution
LOS Gamma / NaZu Rice
NLOS
5 dB 25 dB
RayleighGamma / PoCa
Path loss
exponent
LOS 1.5-2 1-3
NLOS 2.1-62.4- 4
Std. of
Shadowing
(Lognormal)
LOS 1.1-2.1dB 3-6 dB
NLOS 6-12 dB2-5.9 dB
Temporal correlation 0.2 0.2-0.4
Number of paths distribution Rayleigh Poisson
Arrival times distribution Exponential Exponential
TABLE II
COMPARISON OF THE PROPOSED CHANNEL PARAMETER IN THE
LITERATURE FOR UW B AN D CNS.
of multipath components at the same profile. UWB signal
are more robust against fading than CNS and the fading can
be modelled by the Gamma distribution. The RDS seems to
follow a Normal distribution, and its statistics are higher for
the case of NLOS. The RDS values are smaller for the UWB
than the CNS. The path loss increases with the distance. The
path loss exponent as well as the shadowing standard deviationdepends on the propagation medium, and they are smaller for
the UWB.
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