第483回電波研連F分科会
開催日：2004年4月23日

Spatio-Temporal Characterization of Line-of-Sight
and Room-to-Room Propagation in Home
Environments with Ultra Wideband Signal
Katsuyuki Haneda†, Jun-ichi Takada† and Takehiko Kobayashi‡
UWB Technology Institute, National Institute of Information and Communications Technology,
3–4, Hikarino-oka, Yokosuka-city, Kanagawa, 239–0847 JAPAN†Tokyo Institute of Technology, ‡Tokyo Denki University
Email: haneda@ap.ide.titech.ac.jp

I. I NTRODUCTION
Ultra Wideband (UWB) is thought of as one of the candidates to realize future high data rate communications. It is
assumed to be implemented mainly for indoor environments,
but its system performance can suffer from dense multipath
propagation. In order to construct an efficient UWB system,
detailed investigations of the propagation channels are crucial. We have already proposed a frequency domain channel
estimation scheme in a deterministic way which achieves
high resolution with a UWB signal [1], [2]. In this paper,
results of spatio-temporal channel characterization in a typicalhome environment in Japan are presented. First, separation ofdeterministic components and diffuse components [3] were examined. After that, clusterization of deterministic components
was conducted along with the investigations of its intra-cluster
properties and relation to physical phenomena. For the diffuse
components, several qualitative observations are given.
II. C HANNEL E STIMATION S YSTEM
The channel estimation system consists of a data measurement process and derivation of channel parameters. In themeasurement, transfer functions were extracted by a Vector
Network Analyzer (VNA) along with a X-Y scanner and a
X-stage with a single wideband antenna which realizes thesynthesized antenna aperture in both sides of the link [4]. The
structure of the measurement system is depicted in Fig. 1. The
spatially sampled transfer functions are then applied to theMaximum Likelihood based estimator, SAGE. ConventionallyTransmitter wideband
antenna

Receiver wideband
antenna
X-Y Positioner

X stage(DEVICE D3425AV1/O-D)

and Controller(DEVICE DX3165AV1/O-C)

Cable (0.5m)

Vector network analyzer( Agilent 8720ES )Cable (10m)

Abstract— In this paper, spatio-temporal analysis of LOS andNLOS home environments based on the deterministic approachare presented. The detected paths were classified into several
clusters by a heuristic approach. The identified clusters were
determined from the physical structures of the environment,
i.e. room height, specular directions and size of scatterers inboth LOS and NLOS case, which means that the spatial andtemporal channel characteristics are highly correlated. The
NLOS environment was more diffuse than the LOS channel andthe power propagating over the NLOS channel tends to be more
distributed around the specular directions than the LOS case,due to the wall which separates the Tx and Rx. Intra-cluster
properties were derived based on the moment analysis, insteadof deriving a probability density function (PDF) within thecluster. The residual power spectrum after the extraction of 100
deterministic components were identified as diffuse components
and their characteristics were discussed.

Preamp (30 dB)

Port1

Port2

Measurement ofs21

Cable (5m)

GPIB

Positionsetting

GPIB
Laptop PC

Control and data acquisition
Signal Processing

Fig. 1.

UWB channel sounding system [1], [2].

the algorithm is used for wideband channel estimation, but we
modified it for UWB signals [1]. The algorithm is based on thedeterministic approach of propagation channel characterizationand estimates parameters and spectrum of each ray path.
Spherical wave array mode vector was used for the wave
incident model, which seemed to be more realistic in indoor
wave propagation [2]. The SAGE was implemented with theSuccessive Interference Cancellation (SIC) manner, i.e., theestimated components were removed after the determinationof wave parameters. This process was continued until thenumber of waves reached a predefined number or when theextracted spectrum was below the noise level. The double
directional channel measurement enables us to deconvolve
antenna transfer functions in both Tx and Rx sides which leads
to the construction of antenna-independent channel models.
Foremost in the parameters estimation process, we estimatedthe direction-of-arrival (DOA) and time-of-arrival (TOA) simultaneously. After that, direction-of-departure (DOD) estimation was carried out in which TOA was used for the gatingof the transfer function. By using this implementation, thecomputational burden can be significantly reduced [4].

TABLE I
S PECIFICATIONS OF THE E XPERIMENT.

Calibration
IF bandwidthof VNA
SNR at thereceiverRx: 10 × 10 points in horizontal
plane whose element spacing is
48 mm (less than half wavelengthin 3.1 GHz).
Tx: 10 points with 48 mm spacing.
DOA azimuth, elevation angle,
DOD azimuth angle, delay time andspectrum.
UWB monopole antennas [6].
Vertical–Vertical.
Function of VNA andback-to-back.
100 Hz.

Display

Rx

Glass

Glass
5.00 m(a) LOS environment (1st floor of the house).

Azimuth angle
180◦90◦270◦ ◦
0III. L INE - OF -S IGHT C HANNEL M EASUREMENT

B. Separation of the Deterministic and Diffuse Components
With the mentioned channel sounding system, we detected
100 waves and regard them as deterministic components
while the residual components were determined as a diffuse
scattering. Although there is no concrete criteria to distinguishthe deterministic and the diffuse components, the detected
power of the deterministic components approached a floor
level when the SIC detection of the SAGE went to 100 waves,then we can recognize the residual components as diffuse. As aresult, the deterministic components consists of 70.24 % of thewhole received power and 29.76 % still remains unextracted
by SAGE. When both components were evaluated in a DOA
azimuth-delay map, Fig. 3 was obtained. The upper figure
shows the deterministic components, while the power spectrumof the diffuse components is shown in the lower figure as

Tx h=1.30m, Rx h=1.00m, Ceiling-floor h=2.47m
8.24 m
Door

Metal pillar

Window

We conducted an experiment in a typical home environment
in Japan. First, we evaluated a line-of-sight environment whose
floor plan is depicted in Fig 2(a). In the measurement, element
spacing of the synthesized array was set to 48 mm, which was
equivalent to 0.49λ in the lowest frequency. The spatial resolution of our system was previously assessed in an anechoic
chamber and 10 deg angle resolution was confirmed with theconsidered antenna aperture size. For the temporal resolution,
0.67 ns (20 cm) different two waves were accurately resolved
with a 7.5 GHz bandwidth, which was the same in thisexperiment [2]. The bandwidth of the subband in the SAGE
algorithm was 4 GHz. Antennas used were UWB monopole
antennas whose fluctuation of group delay were less than
0.1 ns in the considered bandwidth [6]. Specifications of theexperiment are shown in Table I. There was no furniture
equipped in the room other than TV displays and small desks
which were placed near the wall.

Display

Tx

Metal pillar

About 30 dB.

A. Experimental Setup

Display

3.6m

Door

Window

Antennas
Polarization

8.24 m

801.

Door

Rx

Tx

Sliding door

3.6m

Estimated parameters

Tx h=1.30m, Rx h=1.00m, Ceiling-floor h=2.47m

Window

Spatial sampling

3.1 to 10.6 GHz [5].

Door

Bandwidth
Frequency sweeping
pointsAzimuth angle
180◦90◦270◦ ◦
0

Sliding door
5.00 m(b) NLOS environment (2nd floor of the house).
Fig. 2.

Floor plan of the measurement environment.

a contour plot. Note that initial results of this measurement
including path identifications are covered in [4].
C. Clusterization of the Deterministic Components
On the azimuth-delay map, clusterization of the deterministic components were examined so that the whole detected
paths were classified into 5 clusters as shown in Fig. 3(a).
Among them, there are 2 to 3 sub-clusters observed in clusters
A and E, which are expressed in red lines. Note that clusterization was done intuitively by a heuristic approach. From
this figure, it can be concluded that the spatial and temporalcharacteristics are highly correlated in this channel.
In more detail, it was found that each cluster can be
determined by physical structures of the environment, that is,
specular direction, position and size of reflection objects. In

50

Delay time [ns]45

-65 to -70 dB-70 to -75 dB-75 to -80 dB-80 to -85 dB-85 to -90 dB

below -90 dB

40
Cluster C

35

Cluster B

Clusters E3025(a) Cluster A: Reflection fromthe window glass and window
frame.

(b) Cluster B: Reflection fromthe displays.

(c) Cluster C: Reflection fromthe adjacent room throughwooden door.

(d) Cluster D: Ceiling, floor
and door reflection.

Clusters A

20
Cluster D

15

0

50

100

Direct wave

150
200
250
Azimuth angle [deg]

300

350

(a) Deterministic components estimated by SAGE and their clusterization.

-110 dB-105 dB-100 dB-95 dB
50

Delay time [ns]

45
40
35
30
(e) Cluster E: Reflection fromthe window glass and window
frame.

25
20
0

50

100

150

200

250

300

350

Fig. 4.
Cluster identification with respect to physical structures of theenvironment for the LOS channel measurement. Typical paths are also shown.

Azimuth angle [deg](b) Residual spectrum after the extraction of 100 waves.

Fig. 3. Separation of the deterministic components and diffuse components
for LOS channel measurement.

Fig. 4, typical paths belonging to the clusters are shown on
the floor plan. The shadowed regions correspond to the angle
ranges of the incoming signals. Cluster A contains 3 subclusters, which represent the window reflected waves, window/ display reflection and window / sliding door double bounces,respectively. Differences of the delay time within each subclusters arose mainly from whether the path experiences
ceiling / floor reflection or not. We can then see that the delay
spread within the sub-clusters depends on the height of theroom. The higher the ceiling is, the larger the delay spread.
It also depends on the relative positions of Tx and Rx within
the room. Note that we cannot distinguish ceiling and floor
reflection correctly due to the horizontal plane scanning of thesynthetic array in the Rx side. Therefore, the DOA elevation
was not considered in the clusterization procedure, otherwise
the sub-clusters in cluster A might have been classified intomuch smaller clusters.

Cluster B has the largest number of paths among the 5clusters. The range of the azimuth angle of arrival corresponds
to the positions and size of the displays and small desks, putnear the wall. Several specular diffractions from edges of thedisplays were detected.
Cluster C is composed of incoming signals from the Tx
direction, but have longer delays. They seem to experience
penetrations to and from the adjacent room. Since only two
scattering points can be specified for one ray path, there is
uncertainty concerning the identification of these waves andthe typical paths are represented by dashed lines.
Cluster D includes several types of ray paths, i.e. ceilingreflections, floor reflections and bounces from the wooden door
behind the transmitter antenna. They have the same incident
azimuth angles and closer delays, which are difficult to divide
into different clusters.
Cluster E consists of scattering from the walls, glass andmetal frames of the slide door. As observed in cluster B, thesize of the scatterer, sliding doors in this case, made the patharrival angles wider within the cluster. Here, two sub-clusters
were identified.
In Table II, intra-cluster properties with respect to the firstand second-order moments are shown. Ideally the spatial andTABLE II

4

I NTRA - CLUSTER P ROPERTIES OF THE D ETERMINISTICC OMPONENTS D ERIVED FROM THE LOS C HANNEL .

2

Angular [deg]MeanSpread85.74
17.77
231.92
8.96
269.631.37
270.141.90
305.92
4.50

Delay [ns]MeanSpread
27.26
1.57
22.31
3.22
30.61
1.86
20.00
1.36
22.21
1.38

Power loss [dB]

Cluster(Num. paths)
A (18)B (33)C (4)
D (22)
E (18)

Measured characterisitcs

Mean pathgain [dB]−85.21−83.93−90.36−83.76−85.21Fitting by L = −1.8 − 0.3f

0
-2
-4
-6
-8

temporal anaysis must not be carried out individually since
they are highly correlated. However, the number of detected
paths are too small to derive the spatio-temporal probability
density functions (PDFs) within the clusters. Therefore, in thispaper, the angle and delay moments were evaluated separately.
Angular spread of cluster A indicates the largest value
since they contain three kinds of specular paths, as mentioned
earlier. Larger angular spreads in clusters B and E confirmed
the observation above that the clusters correspond to large or
distributed small scattering objects. The mean received powerof clusters B and D are almost the same, but the mean delay
time and delay spread of cluster B are larger than those ofcluster D. This is because cluster B has larger number of strong
waves of double reflections than cluster D. The delay spreadof cluster B shows the largest value among the 5 clusters.
D. Considerations on the Diffuse Components
The response from diffuse components (Fig. 3(b)) showsthat the distribution of power on the azimuth-delay map
has the same tendency with the distribution of deterministic
components on its azimuth-delay map. This means that our
deterministic approach could not extract power below −95
dB of the path gain sufficiently. Thus it seems that the diffuse components can be characterized from the deterministic
components, but longer delayed paths whose power levels arearound the floor levels (−95 dB) and appear only in the diffuse
response should additionally be considered.
IV. N ON -L INE - OF -S IGHT C HANNEL M EASUREMENT
A. Measurement Environment
Next, we conducted an experiment in a non-line-of-sightenvironment (Fig 2(b)). The previous LOS measurement wasat the first floor of the house and this NLOS measurement
was at the second floor, so that the size of the room wasalmost the same. The distance between Rx and Tx was set
to 5 m, the same as the LOS measurement so that evaluationof penetration loss at the wall was possible. Specifications of
the experiment and definitions of coordinates were the sameas the experiment of the LOS channel. There was no furniture
equipped in the room.
B. Comparison with the LOS Channel Measurement Result
Figure 5 shows the power loss of wall penetration, derived
from the spectrum of shortest paths in the LOS and NLOS
environments. The result is well characterized by a linear
fitting in the log-scale,
L [dB] = −0.3f [GHz] − 1.8

(1)

3

4

5

6

7

8

9

10

11

Frequency [GHz]Fig. 5.

Attenuation due to the wall penetration.

where L is the loss and f denotes the frequency.
In Fig. 6, two kinds of comparisons regarding the powerof the extracted waves were examined. Path gain from wave
#1 to #100 are shown in Fig. 6(a) and the percentage of thesum of extracted power are presented in Fig. 6(b). From thesefigures, it is obsereved that the detected path levels from NLOS
environment were below the levels of LOS paths, mainly due
to the wall penetration rather than the size of the rooms, sinceboth measurements were conducted in rooms of the same
size. Another important finding from the figures is that the
propagation environment of NLOS channel is more diffuse
than the LOS channel, as seen in the percentage of the sumof extracted power. In Fig. 6(b), the values from the NLOS
channel is always smaller than those from the LOS channel.
C. Clusterization of the Deterministic Components
According to the clusterization by a heuristic approach,the detected deterministic components were classified into 10
clusters on the azimuth-delay map as shown in Fig. 7(a).
Contrary to the LOS channel case, the number of observed
clusters were much larger. What is significant in the NLOS
case is that the transmitted power propagates to the receiverin a more distributed or diffuse manner. For example, cluster
D consists of the shortest delay path which is equivalent to
the direct path, associated with several waves whose DOAs
and TOAs were around the shortest delay path. This was dueto the walls, more specifically, reinforcings inside the wall.
It seems to cause diffraction and produce waves around theshortest delay paths. This kind of phenomenon could not be
observed in the LOS channel measurement.
It was observed that the propagating power concentrated
on the specular directions in the LOS case, while they were
scattered around the specular directions in the NLOS case.
Therefore, the uncertainty of path identification increases in
the NLOS case, especially for longer delay paths like cluster
C and J. Another significant reason for this uncertainty is that
only azimuthal angles were estimated in the DOD measurements and the use of a linear array resulted in an ambiguityof the angles.
Nevertheless, it can be concluded that the clusters can be
attributed to the physical structures of the room even in the-70 to -75 dB-85 to -90 dB

-65
LOS

-70

Delay time [ns]Path gain [dB]

Cluster C

45

-75
-80
-85
-90
-95
40

60

80

Cluster B

35

Cluster F

30
Cluster H
Cluster E

25

Cluster I

Cluster G
Cluster A

100

Cluster J

40

20

-100
20

-80 to -85 dB
below -95 dB

50

NLOS

0

-75 to -80 dB-90 to -95 dB

Cluster D

15

Wave #

0(a) Extracted power of each wave in the LOS and NLOS environment.

50

100

150
200
250
Azimuth angle [deg]

300

350

(a) Deterministic components estimated by the SAGE and theirclusterization.

LOS

90

-105 dB-100 dB

NLOS

80

50

70

45

60

Delay time [ns]Sum of extracted power [%]

100

50
40
30
20

-110 dB

40
35
30
25

10
0
0

20

40

60

80

100

Wave #20150(b) Percentage of the sum of extracted power.

50

100

150
200
250
Azimuth angle [deg]

300

350

(b) Residual spectrum after the extraction of 100 waves.
Fig. 6. Comparison of the LOS and NLOS measurement results in terms of
power of each wave and extracted percentage of the power.
Fig. 7. Separation of the deterministic components and diffuse components
for NLOS channel measurement.

NLOS case as shown in Fig. 8. Clusters A, B arose fromone side of the wall, while clusters G and H come from the
opposite side of the wall. Clusters A and B, G and H canbe distinguished by considering whether they experience areflection from the window of Tx side or not. It is interesting topoint out that although the walls were symmetric with respect
to the Tx-Rx line, the number of detected paths and theirpower levels were slightly different. Cluster D was associatedwith the waves around the shortest delay paths, as explained
above. Ceiling or floor reflections appeared as cluster E. The
longer delay paths whose DOAs were the same as the shortest
delay path correspond to the reflections from the back of theTx, i.e. window reflections (cluster F). Clusters I and J werefrom the back of the Rx. Cluster I contains many specularpaths and can be divided into 3 sub-clusters, while cluster J is
composed of the paths which experienced multiple bounces.
Note that the longest delay path (azimuth 90.8 deg, delay 48.2ns) is assumed to experience two reflections. After penetrating

the wall, it reached the window at 270 ◦ direction and thenreflected at the center wall, which separates the room. Intracluster properties are shown in Table III.

D. Considerations on the Diffuse Components
Residual spectrum after the extraction of 100 waves in the
NLOS measurement is depicted in Fig.7(b). The floor level ofthe spectrum was almost the same as the LOS channel case,around −100 dB with respect to the path gain, but the areaof the floor level is larger than that of the LOS map. We cansee that the response of the diffuse components has a relation
with the power distribution of deterministic components, assuggested in the LOS case. We can still see strong responses
remaining in the azimuth angle of 250 ◦ to 300◦ region.

TABLE III
I NTRA - CLUSTER P ROPERTIES OF THE D ETERMINISTICC OMPONENTS D ERIVED FROM THE NLOS C HANNEL .
Cluster(Num. paths)
A (20)B (16)
C (3)
D (8)
E (9)
F (10)
G (11)H (6)I (11)J (4)Angular [deg]MeanSpread
56.16
6.28
64.98
2.44
69.15
0.64
89.39
3.96
89.58
3.87
90.12
1.81
123.922.99
112.74
5.04
269.305.79
277.54
9.84

Delay [ns]MeanSpread
22.07
1.28
28.95
0.98
39.25
2.52
16.84
0.21
19.50
0.24
26.52
0.83
21.29
1.12
28.61
1.11
31.49
1.14
40.87
0.82

Mean pathgain [dB]−87.86−91.13−100.70−78.49−88.04−89.21−86.12−91.56−89.69−93.45(a) Cluster A: Reflection fromthe wall.

(b) Cluster B: Two bouncesfrom walls and window.

(c) Cluster C: Long delayed
paths from specific directions;exact paths cannot be specified.

(d) Cluster D: Shortest delay
paths including the direct path.

(e) Cluster E: Ceiling or floor
reflected paths.

(f) Cluster F: Window reflection.

R EFERENCES(g) Cluster G: Reflection fromthe wall.

(h) Cluster H: Two bouncesfrom walls and window.

[1] K. Haneda and J. Takada, “An application of SAGE algorithm for UWB
propagation channel estimation,” in Proc. IEEE Conf. on Ultra Wideband
Systems and Technologies 2003 (UWBST2003), Reston, VA, USA, Nov.
2003.
[2] K. Haneda, J. Takada and T. Kobayashi, “Experimental evaluation of a
SAGE algorithm for UWB channel sounding in an anechoic chamber,”
accepted for 2004 International Workshop on Ultra Wideband Systems
Joint with Conference on Ultra Wideband Systems and Technologies(UWBST&IWUWBS2004), Kyoto, Japan, May 2004.
[3] A. Richter and R. Thomä, “Parametric modelling and estimation of
distributed diffuse scattering components of radio channels,” in COST273
Temporary Documents, TD(03)198, Prague, Czech Republic, Sept. 2003.
[4] K. Haneda, J. Takada and T. Kobayashi, “Spatio-temporal channel
characterization in a home environment with ultra wideband signal,”
submitted to Intl. Conf. on Wireless Personal Multimedia Communications
2004 (WPMC04), Padova, Italy, Sept. 2004.
[5] Federal Communications Commission, “Revision of part 15 of thecommission’s rules regarding ultra-wideband transmission systems,” inFinal Report and Order, FCC 02–48, Apr. 2002.
[6] T. Taniguchi and T. Kobayashi, “An Omni-directional and low-VSWR
antenna for the FCC-approved UWB frequency band,” in Proc. 2003
IEEE AP-S Int. Symp. (AP-S ’03), pp. 460–463, Ohio, USA, June 2003.

(i) Cluster I: Reflection fromthe wall behind the Rx.

(j) Cluster J: Multipule reflections from the windows andwalls.

V. C ONCLUSION
In this paper, spatio-temporal analysis of LOS and NLOS
chanels based on the clusterization approach were presented.
It was found that the clusters were determined by the physicalstructure of the environment, i.e. room height, specular directions and size of scatterers in both LOS and NLOS case, whichmeans that the spatial and temporal channel characteristics
were highly correlated. The NLOS environment was morediffuse than the LOS channel and the power propagating over
the NLOS channel tends to be more distributed around thespecular directions than the LOS case, due to the wall which
separates the Tx and Rx. Intra-cluster properties were derived
based on the moment analysis, instead of deriving a probability
density function (PDF) within the clusters. The residual power
spectrum after the extraction of 100 deterministic components
were identified as diffuse components and their characteristics
were discussed.
ACKNOWLEDGMENT
The authors would like to thank members of the NICTUWB Consortium, Akira Akeyama, Kiyomichi Araki, Fumio
Ohkubo, Osamu Sasaki, Honggang Zhang, Iwao Nishiyama,
Takahiro Miyamoto, Makoto Yoshikawa, and Yuko Rikuta for
their help in the experiment and research.

Fig. 8.
Cluster identification with respect to physical structures of theenvironment for the NLOS channel measurement. Typical paths are also
shown.