Environments - IEEE Xplore

Empirical

UWB Path Loss Models for Typical Office Environments

Sharlene Thiagarajah Antenna Propagation Unit TM Research & Development Sdn. Bhd. Malaysia

[email protected]

Borhanuddin Mohd. Ali, Sabira Khatun

Mahamod Ismail Electric, Electronic and Systems Engineering Universiti Kebangsaan Malaysia [email protected]

Computer and Communication Systems Engineering Universiti Putra Malaysia

(borhan, sabiral @eng.upm.edu.my

Abstract- This paper investigates the path loss characteristics of an UWB signal (0.1-2GHz) in a typical office building. Three statistical path loss models are presented, i.e. the nth power model and two attenuation factor dependent models. Attenuation factor for brick and gypsum wall, the two main obstructive walls in this experiment were investigated assuming free space path loss. Attenuation factor of 10.37dB and 3.86dB were obtained per gypsum and brick wail respectively. A new distance-dependent attenuation model for gypsum and brick wall is also presented. Comparison of the three proposed path loss models is included.

Section III proposes three statistical path loss models based on empirical data and discusses its performance. Section IV compares our results with similar works and finally, Section V provides the conclusion.

Keywords-component; path loss model; attenuation factor; UWB; propagation; measurements

sampling oscilloscope (DSO). A block diagram of our measurement configuration is shown in Fig. 1. The transmitter configuration consists of a periodic pulse generator, a splitter unit and an UWB antenna. Pulse repetition frequency (PRF) of 1 MHz was chosen to ensure the received pulse response is sufficiently decayed before the next pulse is transmitted. A 2-way (0°) splitter unit was used to supply a trigger signal to the DSO by a fixed length (20 m) coaxial cable. This is to ensure all recorded multipath profiles have the same absolute delay reference, so time delay measurements of the signals arriving at the receiving antenna via different propagation paths can be made. The receiver consists of a receiving antenna, a wideband low noise amplifier (LNA), and a 20 GSa/s DSO. Multipath profiles were captured and stored in the DSO for analysis.

I. INTRODUCTION U itra-wideband (UWB) radio has generated much interest in the telecommunications industry due to its inherent immunity to interferences and fades [ I1. UWB technology is vastly different from classical radio transmission as it uses a series of baseband sub-nanosecond pulses for communication without the use of a carrier. Due to its GHz bandwidth, the UWB spectral content is generally from zero through the microwave region of the spectrum. The power emission limit of a mere 0.5 mW between its transmission bandwidth is similar to common digital devices such as laptops, palm pilots and pocket calculators (2]. The low emission requirement coupled with its spectral spread produces a spectral power density practically at the noise floor. This allows UWB systems to operate in frequency bands where narrowband and wideband constant-envelope spreadspectrum systems already exist This paper describes an UWB experiment conducted in a typical office building to investigate path loss characteristics for various indoor topographies. 924 impulse responses were recorded in 24 locations over a single floor and analyzed according to path topography. Attenuation factor (AF) due to the two main obstructing walls in this expenment, the gypsum and brick wall were also investigated. The nth path loss model and the two statistical path loss models, an AF-path loss model and a distance-dependent-attenuation model are presented. The paper is organized as follows: Section II describes the equipment set-up, floor-plan and measurement procedures.

1-4244-0000-7/05/$20.00 ©2005 IEEE.

II. UWB EXPERIMENT An extensive measurement campaign was conducted in a typical office environment by probing the channel periodically with a series of low-duty cycle pulses (0.833 ns pulse width) and recording the response using a 20 GSa/Sec digital

1023

Tx Antenna

Rx Antenna

LNA

Fig. 1: Block Diagram of Measurement Apparatus

The DSO was set to log channel conditions 50 ns before trigger (pre-trigger measurement), with the remaining 450 ns to log post-trigger channel conditions. The time varying characteristics of the channel was observed by periodic repetition of the transmitted pulse. One hundred repetitive transmitted pulses were sampled and averaged per measure. Measurements were conducted in a typical office building shown in Fig. 2. Generally, the walls are made of brick while partitions between rooms (both sides of the corridor) are made of 2 inch thick gypsum board. The ceiling is covered with 1/2inch thick soft-board. The concrete floor is covered with thin office carpeting except along the corridor and spaces at both ends of the long corridor. The transmitter position (Tx), was fixed at the center of the hallway, while the receiver was moved to 24 different sites marked A-X. At every receiver location, 49 energy delay profiles (EDP) were recorded in a 7 x 7 square grid except for 12 locations along the corridor, where 28 EDP's were recorded in a 7 x 4 square grid. Grid spacing of 15 cm was observed. Only 28 CIR could be taken along the corridor due to space constraints. The measurement grid was designed to determine the robustness of the signal to fades i.e. fast-fading characteristics. Thus, a total of 924 channel impulse responses were recorded in this experiment. / '. !

-H

-1

We identified three main topographies, the path along the corridor (PAC), NLOS paths and Partial LOS paths, where half its grid points are LOS and the other half obstructed, i.e. sites A, K, J, F. The NLOS path was also analyzed for two sub-topographies, Hard-NLOS which consists of sites with no direct path and no reflected signals (in rooms) and Soft-NLOS sites which also have no direct path but a high degree of reflected signals present, i.e. sites B, L, G. A. Antenna Polarization The polarization characteristic of the UWB signal was investigated to determine the suitability of polarization diversity in a UWB system. We calculated the total energy captured using different antenna polarization at I m Tx-Rx separation, as shown in Fig. 3-5. Table 1 compares the results obtained. Based on this preliminary result, it can be concluded that total energy captured is highest for vertical polarized transmissions compared to horizontal polarized transmissions at 32.77dB and 21.23 dB respectively.

.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...........

L!

IT

-

-L1.

..

..

READING ROOM

BOOK RACK

4

1'K.A B

..

~~UBRARYf

t5

OFFICE

BOOK RACK

.At .......

BIAR

Fig. 3: CIR with both Antennas Vertically Polarized

READIN

~~~~~~~ROOM ......

Measurement grid

OFFICE MDF ROOM ..* b b * * 4*EW>, .+, S LAB

K*F

A

CIN

v..........

Ref Im

-(Txt VP, Rx =- HP

I)

(6,0 (0,0)

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~......

I

~A

OFFIC

OFFICE

0* -_

O.

DISCUSSION -ff M OFFICE

OF-FICE

4-*T

** - - *O-IC * -...F

/

F. W *. ^Ful.ck

=_

Fig. 4: CIR with Cross Polarized Antennas IJ .

~~~~~F-JSGIISI

OFFICE

v

Ref lm

(Tx-Rx- =-HP) ----

* OFFICE

l

I

--

t?e I. (T.HP lxP ~~-~

i SF G -~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~- - -

-.

.-.

-- .................................................-.-........ . ...

...............

..

Fig. 2. Floor Plan with Measurement Locations (A-X)

Fig. 5: CIR with Both Antennas Horizontally Polarized

1024

_ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....

Table 1: Energy Captured with Different Antenna Polarization Antenna Polarization

TxTx|

Total Received

Mean

TOA (ns)

MED (ns)

~Energy (13dB)

RY

Rx

Table 2: Mean Path Loss Exponent, n and Standard Deviation, a

Vp-p (V)

I Vertical

Vertical

32.77

5.95

12

280

2 Vertical

Horizontal

13.68

2.22

16

87

3 Horizon

Horizontal

21.23

3.29

12

264

tal

::o

- Topography -;

N

The vertical polarized transmission captures roughly 113 more energy compared to the horizontal polarized transmission. It is interesting to note that the TOA and MED statistics was similar for both vertical and horizontal transmissions. It appears that polarization diversity is possible in an UWB system. Further investigation on the potential of utilizing polarization diversity to increase multiple access capacity or data rate transmissions would be an interesting topic of research.

:

1-4 walls (gyp & brick)

0.16

2.68

None

8

2.87

2.39

1-4 walls (gyp & brick)

Hard-NLOS (a) (Rooms)

5

2.22

2.77

1-2 gypsum wall

(b) Soft-NLOS (B, L, G) Partial LOS Paths (Part-LOS)

3

3.71

1.20

1-4 walls (gyp &

4

4.0

1.60

1-2 gypsum wall

AU Paths (A-X)

2

Paths Along Corridor (PAC)

12

3

Non-LOS (NLOS)

Paths

brick)

Sc&enarios Part-LOS + NLOS

12

1.79

5.57

1-2 gypsum walls

* Part-LOS + Soft-

7

3.16

7.15

1-4 wals (gyp &

Part-LOS + PAC

16

2.48

7.48

1-2 gypsum wall

*

III. PATH LOSS MODELS

NLOS

We investigated three statistical path loss models to determine the best indoor prediction model for UWB communications. They are,

*

brick)

Fig. 6 plots the relative path loss vs. distance for all paths A-X and for the three main topographies PAC, NLOS and Part-LOS, while Fig. 7 plots the relative path loss vs. distance for the Hard-NLOS and Soft-NLOS topographies. Parameters n and a were computed using linear regression (shown as dotted line in Fig. 6 and Fig. 7) in a minimum mean square error (MMSE) sense, and summarized in Table 2. Our results show path loss is more accurately predicted when parameters n is determined as a function of topography. Path loss exponent, n = 2.13 was obtained from the scatter plot of all 24 paths, irrespective of topography, with a standard deviation, ay of 1 1.46 dB.

* Distance dependent path loss model described by the path loss exponent n, and large-scale shadowing, a * Attenuation factor (AF) path loss model which assumes free space path loss with AF for gypsum and brick wall (the two main obstructions in this experiment) and * A new distance-dependent-attenuation model which also assumes free space path loss with a distance-dependent attenuation calculated for gypsum and brick wall. A. Distance-Dependent Path Loss Model We define path loss as the dB reduction in power between the Tx-Rx locations. Based on our measurements, the received energy is first spatially averaged over its 49 (or 28) grid. Relative path loss is then obtained by integrating the average energy in each measurement site over the entire observation window and normalizing it to the total received energy taken at a reference distance of 1m [3]. For narrowband systems when a measurement database is large, the distribution of path loss values over a wide range of distances tends to a lognormal distribution. We assume the distribution of the large-scale path loss for UWB systems is also lognormal [4,51, thus the n h power path loss model can be written as,

PL(d) = PL(do) + 10 n log (d / do ) + X,

-(dB) 11.4 6

I

4

Std ObsLtruction ;: :0: i -;;; : ;Paths ; 00 0 Dev.,De De

n No. of Pathsa - ;0 ; 24 2.13

60]

I

PAC (dB)

x

Par-LOS

NLOS

O All

Paths(dB)

50 NLOS y = 2.8724x * 1.1M5

a 40-

R2 _o09407

e

\

30-

,:

--

--

0 O 09

0

e-

I

=. _2 --

(1)

I.

- @

-

10-

PL(d0) is the path loss at a reference distance, do of lm, n is

the mean path loss exponent, d is the Tx-Rx separation (m) and X, is the zero-mean log-normally distributed random variable with a standard deviation, a in decibels. The standard deviation, a, captures the path loss deviation from its mean value, and is also referred to as shadowing [5]. In addition, a, also provides a quantitative measure of the accuracy of the model used to predict the path loss for a given topography.

1025

-----

S -

,,A_lM PaSh (A. y - 2.125x * .1049 E3 R@F a 0.20085

4a-'--------

PAC

y0=0151x .12.186 Fe = 0.0173

O

2

4

6

8

10

Dlstace (dB) = 10 log 1d4do0

Fig. 6: Relative Path Loss vs. Distance

12

14

a

50

The PAC, Soft-NLOS and NLOS topographies were chosen since part of the Part-NLOS grid is located at the landing at the end of the corridor, while the remaining grid locations can be generally classified as Soft-NLOS. Based on empirical results, it appears that the Part-LOS topography has a very distinct characteristic and cannot be grouped together with the NLOS, Soft-LOS or PAC topographies. Shadowing increased dramatically to 5.57dB, 7.15dB and 7.48 dB when grouped with the NLOS, Soft-LOS or PAC topographies respectively.

-

60

Had-NLOS {Rooms)

x Sot0-BLOS

(B,L,G)

-

a 40 H,wd-NLOS _yz-22tZX+I5A0!L -F = 0.6573

_~~~~~~~~~ --

=30 cc 20

-f

10

tO

0

-

S t-NLOS y =3.7149x . 1.8482 _- R2_

-lil 3

4

5

-

-

-

-

-

--5

7 B 6 Distance, 10 log id/do] (48)

6

-

10

12

11

13

14

Fig. 7: Regression Analysis of the Hard-NLOS and Soft-NLOS Topographies

In comparison, n = 0.16, 2.87 and 4.0 was empirically obtained for the PAC, NLOS and Part-LOS topographies with a standard deviation, a of 2.68dB, 2.39dB and 1.60dB respectively. The Hard-NLOS and Soft-NLOS topography were also analyzed to give n = 2.22 and n = 3.71 with standard deviation of 2.77 dB and 1.20 dB respectively. Due to the limited measurement sites available for the PartNLOS and Soft-NLOS topography, the standard deviation of the slow-fading obtained for these topographies cannot be taken seriously. However, these preliminary results are reported in this paper as an estimate until more measurements can be conducted to confirm them. The less than free-space path loss for the PAC topography at n = 0.16 indicates that the corridor structure acts like a waveguide, allowing the signal to be continuously reflected along its path. In comparison, a 10-15 dB drop in signal level has been reported for narrowband propagation in tunnels at 900 MHz [6]. It was further reported that narrowband radio propagation in tunnels is only feasible at frequencies above 2.4 GHz. Our results clearly show the suitability of UWB (0-2 GHz) in corridors and tunnels. In addition, since there were only four Part-NLOS paths, we investigated the potential correlation between the Part-NLOS paths with the PAC, Soft-NLOS and NLOS topographies respectively, in Fig. 8. 60 *

.....

Part-LOS *PAC

Part-LOS + Sof-NLOS

_y-3 15.7iL4224_

50 +

R2= 0.0196

Part-LOS + NLOS

-----

m

-

-

-

60

Pw-*LOS. NOS 0 _Par-LOS

373 ,- E

y-1.s

-n0S _l---------___ _- _ ---+

30

--0

> __________________;,,__ --

-

*

10 +

-

a*

*

1

X,____ '

10

2

0a

_-

y=

Part-LOS . PAC

2.4803x - 4.6011 6'= 0.4107

_- _- _- _- _-

B. Gypsum & Brick Attenuation Factor Model The nth power path loss model proposed included the effects of transmitter and receiver (Tx - Rx) separation and topography only. The accuracy of the nth power distance and topography dependent path loss model is given by the standard deviation or the shadowing parameter obtained for various

topographies. To further improve the propagation prediction model, we investigated the attenuation factor (AF) due to the two main obstructions between the Tx - Rx, i.e. gypsum and brick wall. The effect of office clutter, i.e. tables, chairs, desk-tops, book rack, cabinet and general office supplies and equipment were considered negligible for the purpose of this experiment. For simplicity, it is assumed that any kind of brick/gypsum wall that wholly or partially blocks the direct path between the Tx Rx is labeled a brick/gypsum wall. Assuming free space path loss (n = 2), the attenuation factor path loss model is given in (2). Let p be the number of gypsum walls and q the number of brick walls between Tx Rx. PL(d) [dB] = 20 log (4&d2) + (p x AF(gypsum)) + {q x AF(brick)}

3

4

5

6

9 -10 Dislance, 10 log 1d/d01 (dB) 7

a

11

12

13

Fig. 8: Correlation of the Part-LOS with the PAC, NLOS and Soft-NLOS Topographies

(2)

where, pL(d) is the mean path loss, d is the Tx - Rx distance and X is the wavelength of the transmitted pulse. No reference distance was used since free space propagation is assumed for all distances. For each of the discrete path loss measurements, the difference between the measured path loss and free space path loss for the same Tx - Rx separation were computed and recorded together with the number of gypsum and brick wall between them. The difference between the measured and the free space path loss shows the attenuation due to the cumulative effects of all brick and gypsum walls between the Tx - Rx paths. First, we investigated AF due to gypsum wall only since all locations with brick wall obstructions also had gypsum wall obstructing its path. For simplicity, it is assumed that every gypsum wall induces identical path loss regardless of distance. Using (3), the mean AF of 10.37dB /gypsum wall was obtained. N is the number of measurement sites used in the experiment. Mean AF,

2

[dB]

14

I1 1 Z[AttenuationlNo. of Wall]

(3)

Similarly to determine AF due to brick wall, only locations with brick walls obstructing its Tx-Rx path were selected. It is

1026

also assumed that every brick wall induces identical path loss regardless of distance. However in this experiment, all locations with brick wall obstructions also had gypsum wall obstructing its path, thus effective attenuation due to brick wall only is obtained by first reducing 10.37dB for each gypsum wall that was recorded along the affected path. Similarly using (3), the mean AF was calculated to be 3.86dB/ brick wall. Narrowband measurements at 914 MHz reported attenuation factor of 1.39dB/ soft partition and 2.38dB/concrete wall [7]. The attenuation factor for brick wall has yet to be found in any literature reviewed thus far. The accuracy of the proposed AF path loss model in (2) was investigated by calculating the standard deviation of the difference between the measured and predicted path loss. The results in Table 3 clearly indicate that the nth power path loss model gives better predicted path loss compared to the AF path loss model for all topographies and scenarios. Standard deviation of 2.39dB and 1.60 dB was obtained for the NLOS and Part-LOS topographies respectively using the nth power path loss model compared to a standard deviation of 5.36dB and 3.98dB with the AF path loss model.

C. Distance-Dependent Attenuation Model To further improve the AF path loss model in (2), we investigated the correlation between the AF and Tx - Rx separation. Free space propagation (n = 2) is still assumed for all paths. For simplicity, it is also assumed that any kind of brick/gypsum wall that wholly or partially blocks the direct path between Tx - Rx is labeled a brick/gypsum wall. It is also assumed that every gypsum wall induces identical path loss, and every brick wall induces identical path loss regardless of the actual distance of the obstructing wall from the receiver. The scatter plot of the average attenuation per wall vs. distance for both gypsum and brick wall is shown in Fig. 9. Linear regression analysis is shown in dotted line, using a minimum mean square error method (MMSE) to find the relationship between attenuation and distance for gypsum and brick wall obstructions.

Table 3: Performance Comparison

Path Loss Model

n!h

Power Model

N Topography o I

2

C Brick Wall

20.00

--

-_

a t5.00

R,egession FH {BrckWai) *_ - + y = _______________ -1.2§STau-5--___ ~25.6"4 ____ ~~ _ sy-. > 0.1325| ~~~~~~~R2-

_

_

s

_

_

if 10.00

I S

5t0-i

---

-

y. 0-00 v.wu

t

-(ypsaWa- ----

-0.7417a . 17.3-53 R2 = 0.7412

2

4

6

a

10

_

12

14

16

-5.too

Distace (m)

Fig. 9: Average Attenuation Per Wall vs. T-R Separation

10

Std

Std Dev, ::Std Des, e, es: :-: a(d.B); -::-- (dB)-:--

(DeVBdB

Non-LOS Paths (NLOS)

2.39

5.36

8.14

(a) Hard-NLOS (Rooms)

2.77

5.32

5.35

(b) Soft-NLOS

1.20

6.33

1.89

Part-LOS Paths

1.60

3.98

1.46

* Part-LOS + NLOS

5.57

7.17

6.70

* Part-LOS + Soft-NLOS

7.15

7.55

6.16

AF of -0.7417 dB/m and -1.2957 dB/m was obtained for gypsum and brick wall respectively. Let p be the number of gypsum walls and q the number of brick walls between the transmitter and receiver. Thus, the relationship between the average attenuation factor for brick/gypsum wall, AF(d) and distance, d is,

AF,,.. (d)

= [-0.7417 d +

17.353]p

[dB]

(4)

= [-1.2957 d +

25.694] q

[dB]

(5)

and

AFbrck (d)

By combining the free space path loss and the distancedependent attenuation factor for gypsum and brick wall in (4) and (5), a new distance-dependent attenuation factor path loss model can be written,

PL(d) = 20 log (4zd ) + AFgypSum(d) P = 20 log

25.00

Distance Dependent Attenuation Model

Scenario

Distance Dependent Attenuation Factor tor Gypsum and Brick Wait Gypsum Wall

Nv d0;-0--

AF Model

+

AFbrick(d)

q

(42,Td) + [-0.7417 d + 17.353]p

+ [-1.2957 d + 25.694] q (6) The standard deviation of the difference between the measured and predicted path loss in (6) was also calculated and given in Table 3. Although the distance-dependant-AF path loss model shows a higher degree of accuracy (i.e. lower standard deviation), for the Part-LOS topography and the PartLos + Soft-NLOS scenario, there are several limitations for the (d) practical application of this proposed model. In (6) and AFbr, ^.(d) is represented in the form of -a(d) + b, where parameters a and b are found by a minimum mean square error method from empirical measurements (Fig. 7). Parameter a represents the attenuation factor per meter (dB/m) and d is the T-R separation distance and not the distance of the obstruction from the receiver position.

AFg.p",,,

1027

Parameter b is a constant. To determine the accuracy of parameter a, more measurement have to be conducted in various office buildings, i.e. to determine if it is building/environment dependent. Parameter b on the other hand is essentially an offset used to fit the data, and will vary from building to building. An offset that varies from building to building and that cannot be associated with the physical surrounding is not useful as a propagation prediction tool. IV. PERFORMANCE COMPARISON

We did not compare our results and the experiments of Ghassemzadeh et al. [5] and Alvarez et al. [8] mainly due to the different UWB frequency range used in these experiments and the different classification of topographies used. However it is interesting to note that even with similar frequency ranges, the mean path loss exponent, n and the shadowing phenomenon obtained by Alvarez et al. and Ghassemzadeh et al. were vastly different. Alvarez et al. [8] reported a mean path loss exponent, n = 1.4 with a 0.35 dB shadowing for LOS paths for I 9 GHz compared to a mean path loss exponent of n = 2.07 and shadowing of 2.3 dB by Ghassemzadeh et al. [5] at 2 -8 GHz. However, Ghassemzadeh et al. used 600 measurement sites in their analysis compared to 44 measurement sites used by Alvarez et al., thus lending a measure of credibility to the accuracy of Ghassemzadeh et al. results. -

A comparison of the three proposed path loss models show that the distance dependent path loss model described by the path loss exponent n, and standard deviation, a, still gives the best path loss prediction with the lowest standard deviation, a of the difference between measured and predicted path loss. A comparison of mean path loss exponent, n and shadowing parameter obtained from this experiment and the works of Cassioli et al. in 2001 [4], Ghassemzadeh et al. in 2003 [51 and Alvarez et al. in 2003 [8] are given in Table 4. We first compared our results with the works of Cassioli et al. due to the similarity in the UWB frequency range (0 - 2 GHz) used. Cassioli et al. [4] reported a mean path loss exponent, n = 2.4 and shadowing of 5.9 dB for all its 14 locations, compared to the mean path loss exponent, n = 2.14 and shadowing of 11.46 dB obtained for all 24 locations in this experiment. However, Cassioli et al. did not analyze path loss according to topography or describe the different locations where the 14 measurement sites were taken.

V. CONCLUSION We performed a statistical evaluation of the path loss characteristics of the UWB channel in a typical office building. The accuracy of the nh power path loss model and two attenuation factor dependent path loss models were investigated for several path topographies and scenarios. It can be concluded that the nth power-path loss model gives better path loss prediction compared to the attenuation factor and the distance-dependent attenuation model. The new distance-dependent attenuation model did show good path loss prediction for the Part-LOS topography, but is not practical since the model is dependent on the building where the experiment is conducted.

Table 4: Comparison Of Mean Path Loss Exponent, n and Std Dev, ay

1

2

3

4

Mean Path Loss Exponent, n and Standard Deviation, (dB)

Topography

This Exp.

Cassioli et al. [31

All Paths (A-X)

n=2.13, a = 11.46

n=2.4,

Paths Along

n=0.16, a = 2.68

NLOS Paths

n=2.87, (Y = 2.39

Hard-NLOS

n=2.22, a = 2.77

reported

Soft-NLOS

n=3.7 1, a -1.20

reported

n=4.0, a = 1.60

Corridor

Partial LOS Paths

UWB

Ghassemzade h et al. [41

= 5.9

reported

Not

Not reported

reported

Not

n-1.4, o=0.35 (LOS)

n=2.07, a =2.3 (LOS)

Not

reported

Not

reported

n=2.95, a =4.1

Not

n=4.1a =

Not reported

n=3.2, a =1.21

Not reported

reported

Not

Not reported

Not reported

0-2 GHz

1-9 GHz

2-8 GHz

Not

1

Alvarez et al. [7J

[1]

[2] [3] [4] [5]

1.87

[6]

Freq.(GHz)

0.1-2 GHz

No. of Sites

24

14

44

600

2-19 m

6- 18

4-18m

0.8-10.5 m

T-R Separation

REFERENCES

[7]

[8]

1028

S. Thiagarajah, B. M. Ali, V. Prakash and M. H. Habaebi, "An Overview: Emerging Research Issues On Ultra-Wideband Impulse Radio", in Proc. World Multi-Conf on Systematics, Cybernetics and Informatics, USA, pp. 248-255, July 2003. B. Pattan, "A Brief Exposure to Ultra-Wideband Signaling", Microwave Journal, pp. 104-1 10, Dec. 2003. S. Thiagarajah, B. M. Ali, S. Khatun and M. Ismail, "UWB Fading Characteristics in a Typical Office Environment"', 11'h European Wireless Conference, Nicosia, Cyprus, Vol.2, pp. 696-701, April 2005. D. Cassioli, M. Z. Win and A. F. Molisch, "A Statistical Model for the UWB Indoor Channel", 53d IEEE Vehicular Technology Conference Proceedings, Vol.2, pp. 1159-1163, Spring 2001. S. S Ghassemzadeh, L. J. Greenstein, A. Kaveie, T. Sveinsson and V. Tarokh, "An Empirical Indoor Path Loss Model for Ultra Wideband Channels", Journal of Communications and Networks, Vol. 5, No. 4, pp.303-308. December 2003. D. Parsons, "The Mobile Radio Propagation Channel", Pentech Press, London, 1992. S. Y. Siedel and T. S. Rappaport, "914 MHz Path Loss Prediction Models for Indoor Wireless Communications in Multifloored Buildings", IEEE Trans. Antennas Propagat., Vol. 40, pp. 207-217, Feb. 1992. A. Alvarez. G. Valera, M. Lobeira, R. P. Torres and J.L. Garcia, "Ultra Wideband Channel Model for Indoor Environments", Journal of Communications and Netwvorks, Vol. 5, No. 4, December 2003, pp.309318.