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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.

REFERENCES

[1] K. Siwiak, Ultra-Wide Band Radio: A New PAN and PositioningTechnology, IEEE Vehicu. Technol. Soc. News, pp. 49, Feb. 2002.

[2] D. Cassioli, M.Z. Win and A.R. Molisch, A Statistical Model for theUWB Indoor Channel, IEEE VTC Spring 53rd, vol. 2, pp. 11591163,Oct. 2001.

[3] J. Keignart and N. Daniele, Subnanosecond UWB Channel Soundingin Frequency and Temporal Domain, Ultra Wideband Systems andTechnologies, Digest of Papers. IEEE Conference on, pp. 2530, 2002.

[4] R.J.M. Cramer, R.A. Scholtz and M.Z Win, Evaluation of an Ultra-

Wide-Band Propagation Channel, Antennas and Propagation, IEEETransactions on, vol. 50, no. 5, pp. 561570, May 2002.

[5] J. Kunisch and J. Pamp, Measurement Results and Modeling Aspectsfor the UWB Radio Channel, Ultra Wideband Systems and Technolo-gies, Digest of Papers. IEEE Conference on, pp. 1924, 2002.


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[6] S.S Ghassemizadeh, R. Jana V. Tarokh, C.W Rice and W. Turin,A Statistical Path Loss Model for In-home UWB Channels, UltraWideband Systems and Technologies, Digest of Papers. IEEE Conference

on, pp. 5964, 2002.[7] M.Z Win and R.A. Scholtz, On the Robustness of Ultra-Wide Band-

width Signals in Dense Multipath Environments, IEEE CommunicationsLetters, vol. 2, no. 2, pp. 5153, Feb. 1998.

[8] S. Yano, Investigating the Ultra-wideband Indoor Wireless Channel,Conference IEEE VTC Spring, vol. 3, no. 55, pp. 12001204, 2002.

[9] J. Foerster and Q. Li, UWB Channel Modeling Contribution fromIntel, Report, Jun. 2002.

[10] L. Rusch, C. Prettie, D. Cheung, Q. Li and M. Ho, Characterizationof UWB Propagation from 2 to 8 GHz in a Residential Environment,

Article availble at www.intel.com, 2003.[11] A. Molisch, Statistical Properties of The RMS Delay Spread of Mobile

Radio Channels with Independent Rayleigh-Fading Paths, IEEE Trans.Vehicu. Technol., vol. 45, no. 1, pp. 201204, Feb. 1996.

[12] G. Janssen, P. Stigter and R. Prasad, Wideband Indoor Channel Mea-surements and BER Analysis of Frequency Selective Multipath Channelsat 2.4 GHz , 4.75GHz and 11.5 GHz , IEEE Trans. Vehicu. Technol.,vol. 44, no. 10, pp. 12721288, Oct. 1996.

[13] J.N. Pierce and S. Stein, Multiple Diversity with NonindependentFading, Proc. IRE, vol. 48, pp. 89104, Jan. 1960.

[14] H. Hashemi, The Indoor Radio Propagation Channel, Proc. of IEEE,vol. 81, no. 7, pp. 943968, Jul. 1993.

[15] C. Prettie, D. Cheung, L. Rusch, M. Ho, Spatial Correlation of UWB

Signals in a Home Environment, IEEE Conference on Ultra WidebandSystems and Technologies, pp. 6569, May 2002.

[16] L.W. Couch II, Digital and Analog Communication Systems. Prentice-Hall International, 1997.

[17] A.A.M. Saleh and R.A. Valenzuela, A Statistical Model for IndoorMultipath Propagation, IEEE J. Select. Areas Commun., vol. 5, pp.128137, Feb. 1987.

[18] B. J. Q. Spencer, M. Rice and M. Jensen, A Statistical Model for Angleof Arrival in Indoor Multipath Propagation, IEEE Vehicular TechnologyConference, vol. 3, no. 47, pp. 14151429, May 1997.

[19] K. Siwiak and A. Petroff, A Path Link Model for Ultra Wide BandPulse Transmissions, IEEE VTS Spring Vehic. Tech. Conf., vol. 2,no. 53, pp. 11731175, 2001.

[20] D. S. Polydorou and C. N. Capsalis, A New Theoretical Model forthe Prediction of Rapid Fading Variations in an Indoor Environment,Vehicular Technology, IEEE Transactions on, vol. 46, no. 3, pp. 748754, Aug. 1997.

[21] H. Zhang, T. Udagawa, T. Arita and M.Nakagawa, A Statistical Modelfor the Small-scale Multipath Fading Characteristics of Ultra WidebandIndoor Channel, Ultra Wideband Systems and Technologies, Digest ofPapers. IEEE Conference on, pp. 8186, 2002.

[22] T.S. Rappaport, Wireless Communications Principles and Practice.New Jersey: Prentice-Hall, 1996.

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