論文 / 著書情報
Article / Book Information
Title

Dense Multipath Component Characteristics in 11-GHz-Band Indoor
Environments

Authors

Kentaro Saito, Jun-ichi Takada, Minseok Kim

Citation

IEEE Transactions on Antennas and Propagation, vol. 65, no. 9, pp.
4780-4789

Pub. date2017, 9Copyright(c) 2017 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for resale orredistribution to servers or lists, or reuse of any copyrighted componentof this work in other works.

DOI

http://dx.doi.org/10.1109/TAP.2017.2728087

Note

This file is author (final) version.

Powered by T2R2 (Science Tokyo Research Repository)

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

1

Dense Multipath Component Characteristics in
11GHz-band Indoor Environments
Kentaro Saito, Member, IEEE, Jun-ichi Takada, Senior Member, IEEE, and Minseok Kim, Member, IEEE,

Abstract—In the next-generation mobile communication system, the higher frequency bands from C band to V band are
expected to be utilized because it has the potential to improve
network capacity drastically by the available wideband spectrum.
Since the characteristics of reflected and scattered radio waves
from surrounding environments in those bands are thought tobe quite different than at lower frequencies, the clarificationof its influence on the Multiple-Input Multiple-Output (MIMO)
transmission performance is a critical issue. In this paper, we
focused on the characteristics of diffuse scattering in X band,and conducted the MIMO channel measurements in indoor
environments in the 11 GHz band. The frequency, angular,
and polarization domain Dense Multipath Component (DMC)
propagation parameters were jointly estimated by using the
RiMAX-based estimator. The measurement result showed theexistence of significant DMC, which is thought to have originatedfrom the floor, the ceiling as well as the walls. The angular spreadsof the DMC tended to increase, and their decay factor tended
to decrease as the room size decreased. It is also shown that theexistence of DMC significantly affected the eigenvalue structureof the MIMO channel, which defines the MIMO transmission
performance. The result is expected to be utilized for novel
MIMO channel modelling in X band that includes the DMC
contribution.
Index Terms—diffuse scattering, Dense Multipath Component,
MIMO channel modeling

I. I NTRODUCTIONU

SER traffic in the mobile network is increasing drastically owing to the spread of tablet computers andvarious application services. Recently, the next-generation
mobile communication systems such as the fifth generation(5G) system [1] [2] have been widely investigated in orderto accommodate the increasing user traffic. In the 5G system,
the utilization of higher frequency bands from C band to V
band for the mobile communication has become a hot research
topic because it has the potential to improve the network
capacity drastically by using the available wideband spectrums. Furthermore, massive Multiple-Input Multiple-Output(MIMO) technologies are expected to be employed in thosefrequency bands for achieving higher order beamforming andspatial multiplexing data transmission.
The higher frequency bands have not been used for themobile communication. Especially in indoor environments, itis thought that radio waves will be scattered from various
objects in the environment. Diffuse scattering is thought toconsist mainly of the irregular scattering from rough surfaces
Kentaro Saito and Jun-ichi Takada are with the School of Environment and Society, Tokyo Institute of Technology, Tokyo, Japan (email:saitouken@ide.titech.ac.jp).
Minseok Kim is with Department of Electrical and Electronic Engineering,
Niigata University, Niigata, Japan.

of objects [3]. Although it might be expected that diffuse scattering arising from rough surfaces would be more prominent
in X band compared to the Ultra High Frequency (UHF) banddue to X-band’s shorter wavelength, additional attenuation
may also reduce the effect. It is known that diffuse scattering
affects the MIMO transmission performance significantly insome communication scenarios [4] [5] [6]. However, thecontribution of diffuse scattering is not considered in de facto
standard channel models such as the 3GPP Spatial Channel
Model (SCM) [7], ITU-R M.2135 channel model [8], and
WINNER II channel model [9]. Therefore, the clarificationof the influence of diffuse scattering on MIMO channel
characteristics is a critical issue for the higher-frequency-band
MIMO channel modeling.
In previous works on diffuse scattering, scattering characteristics from a rough wall of a building were measured
and the modeling method was introduced to include thosecontributions into ray tracing simulations [10] [11]. Although
the Power Delay Profile (PDP), angular spread, and polarization characteristics of diffuse scattering were also evaluatedthrough ray trace simulations [12] [13] [14], the evaluation
correctness relies on the accuracy of assumed environment
map used in the simulations.
In previous works on the MIMO channel modelling, thecontributions of diffuse scattering were directly estimatedand parametrized from MIMO channel measurements by the
RiMAX algorithm [15] [16] [17]. In the RiMAX algorithm,
coherent waves such as direct waves and regular reflection
waves are modeled deterministically as Specular Multipath
Component (SMC), and the composition of vast amounts
of incoherent plane waves due to diffuse scattering is modeled stochastically as Dense Multipath Component (DMC).
The DMC is characterized by autocorrelations of the signal
component, and only the frequency domain correlation has
been discussed in the RiMAX algorithm [15] [16] [17]. In[18], the DMC frequency domain parameters were estimatedfrom measurement data of a Line-of-Sight (LoS) industrial
environment in the 3 GHz band. The result showed that thepower ratio of the DMC to the total received power ranged
from 23 % to 38 %, and the PDP of the DMC almost followed
the exponential decay distribution that is described in the
RiMAX algorithm. The polarization domain characteristics of
the DMC were also investigated in an industrial hall in the 1.3
GHz band [19], in a large hall in the 1.3 GHz band [20], andin urban environments in the 4.5 GHz band [21]. The results
showed that the DMC had polarization dependencies. The
angular-domain characteristics of the DMC were investigatedin indoor environments in the 5.3 GHz band [22]. The results
showed that the angular power profile of the DMC correlated

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

to it of the SMC, as also described in [23]. To deal with angularand polarization characteristics of the DMC quantitatively, theextensions of the RiMAX algorithm have been proposed. In[24] [25], the von Mises distribution [26] was used for representing the angular profile of the DMC. The angular-domain
autocorrelation of the DMC is calculated by convolution of
the DMC angular profile and the array response. In [27], theextension of the DMC polarization domain parameters and theparameter estimation method was proposed.
In the past literature, the discussion on DMC characteristics
is limited to lower frequency bands below 6GHz. Furthermore,
the investigation only focused on frequency domain DMC
parameters, without a quantitative analysis on angular domain
parameters in actual environments. The contribution of ourwork is the clarification of DMC characteristics in X bandthrough 11 GHz band MIMO channel measurements [28].
We have extended the conventional RiMAX algorithm forthe joint estimation of the DMC propagation parameters in
the frequency, angular, and polarization domains to evaluate
the characteristics of the DMC in a quantitative way [29][30]. This paper expands our previous work in [29] [30] by
providing a more comprehensive investigation on the DMC
characteristics in three different indoor environments, particularly in the following aspects. We evaluated the propagation
parameter distributions of the DMC in each indoor environment, and discussed the influences of the environments on
the DMC characteristics. We also clarified to what extent
the DMC contributes to the eigenvalues structure of the
MIMO channel by comparing the measurement data and thedata reconstructed from the estimated propagation parameters.
Finally, we investigated the originating source of the DMC byutilizing the 3-dimensional (3D) virtual array method.
The rest of this paper is structured as follows. The received
signal model and the DMC propagation parameter estimation
method are described in Section II. Then the MIMO channel
measurement setup in the 11 GHz band in indoor environments
is presented in Section III. The measurement results and the
DMC propagation parameters characteristics are presented in
Section IV. Finally, the conclusion and avenues for future work
are described in Section V.
II. R I MAX- BASED DMC P ROPAGATION PARAMETERE STIMATION
A. Signal Model and Parameter Estimation Method
In Fig. 1, the assumed signal model of the MIMO propagation channel [27] [30] is shown. The signal consists ofthe sum of SMC and DMC. The SMC is modeled by thesuperposition of plane waves that are deterministically definedby propagation parameters. The SMC propagation parameter
vector of the i-th path θ s,i consists of the propagation delay
τs,i , the azimuth Angle of Arrival (AAoA) φR,i , the coelevation Angle of Arrival (EAoA) θR,i , the azimuth Angle of Departure (AAoD) φT,i , the co-elevation Angle of
Departure (EAoD) θT,i , and the complex amplitude vector
γ i = [γs,vv,i , γs,vh,i , γs,hv,i , γs,hh,i ]. In the signal model, the
SMC of the i-th path s(θ s,i ) ∈ CNF NR NT ×1 is defined asfollows. Here, NF is the number of subcarriers , NR is the2number of Receiver (Rx) antennas, and NT is the number ofTransmitter (Tx) antennas.
θ s,i =[τs,i , φR,i , θR,i , φT,i , θT,i , γ i ](1)s(θ s,i )=αF (τs,i ) ⊗ αR (φR,i , θR,i ) ⊗ αT (φT,i , θT,i )γ i (2)Here, αF (τs,i ) is the frequency transfer function of the impulse
function δ(τ − τs,i ), and αR and αT are the Rx and theTx array antenna responses, respectively. ⊗ represents the
Kronecker product.
The DMC is modeled by the signal component, which isstochastically determined by the delay-domain DMC propagation parameter θ d,F and the angular-polarization domain DMC
propagation parameter θ d,A as follows:θ d,F =[α0 , α1 , βd , τd ](3)

θ d,A =[µR , µT , κR , κT , γd,vv , γd,vh , γd,hv , γd,hh , γα ] (4)In [15] [16], the PDP of the DMC is modeled as the following
exponential decay distribution by using the delay-domain
DMC parameters.
(τ < τd )α0(5)
fF (τ ) = α1 /2(τ = τd )−Bd (τ −τd )α1 e(τ > τd )In [23] [24] [25], the angular power spectrum of the DMC ismodeled by the von Mises distribution [26]. In the literature,
the Rx-side angular power spectrum is defined as follows.
fR (φ) =1eκR cos(φ−µR )2πI0 (κR )(6)Here, I0 (·) is the modified Bessel function of the first kindand the order is zero, µR is the center AAoA of the DMC,
and κR is a parameter which represents the angular spread of
the von Mises distribution. The angular spread decreases andthe distribution approaches to the normal distribution as κR
increases. The angular spread σR is approximated by σR =−0.5
κRin the case of large κR .
The frequency domain correlation matrix RF (θ d,F ) ∈CNF ×NF and the angular-polarization domain correlation matrix RA (θ d,A ) ∈ CNR NT ×NR NT are calculated from thepower density distribution defined in eq. (5) and (6). Finally,
the DMC correlation matrix R(θ d,F , θ d,A ) is calculated asfollows:
R(θ d,F ,θ d,A ) = RF (θ d,F ) ⊗ RA (θ d,A )(7)

The estimation procedure is explained as follows. First,
the SMC parameter θ s,i is estimated from the measured data
x by the SAGE algorithm [31]. Next, the delay, angular andpolarization domain propagation parameters of the DMC are
next estimated by the RiMAX-based algorithm. The detailed
procedure is explained in [30].
B. Stochastic MIMO Channel Generation Method
In this section, the MIMO channel generation method basedon the estimated propagation parameters is explained. Because
the contribution of the radio propagation channel and the array
antenna characteristics to the MIMO channel are separately

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

3

SMC model (plane wave assumption)
Rx
Tx(φT ,iθ T ,i )(φR ,i , θ R ,i )τ s.i(C) Antenna array(dual-pol. 12-element circular array)

Signal model
SMC(path#i)s(θs ,i ) = α F (τ s ,i ) ⊗ α R (φR ,i , θ R ,i ) ⊗ α T (φT ,i , θ T ,i ) γ i
DMC:

Propagation Parametersθ s ,i = [τ s ,i , φT ,i ,θ T ,i , φR ,i , θ R ,i , γ i ]θ d,F = [α 0 , α1 , β d ,τ d ]θ d,A = [ µ R , µ T , κ R , κ T ,γ d, vv , γ d, vh , γ d,hv , γ d,hh , γ α ]f A (φR ) =1eκ R cos(φR − µ R )4π 2 I 0 (κ R )f F (τ ) = α1 exp(− β d (τ − τ d ))Fig. 2. Photos of 11-GHz-band channel sounder: ((A) Receiver, (B) Transmitter , and (C) Antenna Array).

Fig. 1. The SMC and the DMC model of MIMO propagation channel.

modeled in the Geometry-based channel model explained in
II-A, the MIMO channel reconstruction from the estimatedpropagation parameters is necessary to validate how accurately
the MIMO channel characteristics are modeled. Generally, it
is possible to synthesize MIMO channels even if we assume
array antennas other than the one used in the measurement.
The MIMO channel is synthesized from the propagation parameters θ s,i , θ d,F , and θ d,A and the array antenna responses
α0R and α0T which are assumed for the channel generation.
In [15] [16], the generated MIMO channel x0 ∈NF NR NT ×1
C
consists of the SMC of the i-th path s0 (θ s,i )and the DMC d0 (θ d,F , θ d,A ).
x0 =

I
X

s0 (θ s,i ) + d0 (θ d,F , θ d,A )(B) Transmitter(A) Receiver

(8)

i=1

TABLE IM EASUREMENT PARAMETERS .
Channel sounder parameters
Center frequency

11 GHzBandwidth

400 MHz

Transmit power (per antenna)

10 mW

Transmit signal

Multitone signal

Number of Tone

2048

Tx/Rx antenna array

12-element circular array(12 V-pol. and 12 H-pol.)Tx antenna height

1.7 m

Rx antenna height

1.7 m (moving measurement)
1.7 m, 1.714 m, 1.728 m, 1.742 m(3D measurement)

Data analysis parameters

0Here, s (θ s,i ) is deterministically generated from the SMC
propagation parameters according to eq. (2). Because the DMC
is modeled stochastically, d0 (θ d,F , θ d,A ) is stochastically generated from the i.i.d. channel matrix G ∈ CNF NR NT ×1 and thecorrelation matrix R0 (θ d,F , θ d,A ).
d0 (θ d,F , θ d,A ) = LG

Maximum path number

10, 20, 40, 80, or 120

Maximum iteration number(both SAGE and RiMAX)

20

Path discarding threshold ξ

20 dB

(9)

Here, L is the matrix that satisfies LLH = R0 (θ d,F , θ d,A ).
III. 11 GH Z - BAND I NDOOR MIMO C HANNELMEASUREMENT

We conducted MIMO channel measurements in the 11 GHzband in several indoor environments to clarify the frequency,
angular and polarization characteristics of the DMC in X
band. The specifications of the 11-GHz-band MIMO channelsounder that we have developed [32] is shown in Table I.
Photos of Tx, Rx, and the antenna array are also shown in
Fig. 2. The Tx/Rx antenna arrays were 12-element circulararrays with dual-polarized patch antennas. Therefore, 24-by24 MIMO channel matrices with 400 MHz bandwidth wereobtained in the measurement. The Tx/Rx antenna heights were
set to 1.7 m.

The measurements were conducted in 3 indoor environments: a room, a hall, and a museum. The floor layouts andphotos of each environment are shown in Fig. 3, and 4. In theroom and in the hall, there was no furniture except for a few
chairs and tables. In the museum, there were several tables,
chairs and interior structures such as pillars. The sizes of thoseareas were about 18 m ×10 m ×3 m, 30 m ×10 m ×3 m, and
30 m ×20 m ×6.5 m, respectively. We conducted two kindsof measurements. The first kind of measurement is a moving
measurement, where the Rx was set at fixed points in the areas,
and then the Tx was moved along the measurement courses
at a constant speed of approximately 0.25 m/s. The channels
were measured at every 1.4 sec. during the measurements.
The Rx points and measurement courses are shown in Fig. 3.
The numbers of the Rx points were 2 in the room and in themuseum, and 3 in the hall. The number of the courses wereJOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

4

Point210 m
photo viewpoint

Tx

θRx

θTx

Measurement Course1

2m

9.75m

2.25 m
3m

18 m

Rx

3m

Measurement Course2

1m

Point1(A) Room2 m 2.5 m
3.2 m

9.75m

30 m
20 m

Point (b)Point3

Measurement Course2

Tx
θTx 3 m 1m

Measurement Course1

θRx
Rx

Point (a)photo viewpoint

Point29.75m

Point1(Panorama photo)(B) Hallcharacteristics by assuming four measurement data as a 96by-24 MIMO channel matrix.
In the data analysis, the SMC parameters were first estimated by the SAGE algorithm [31]. Then the frequency,
angular, and polarization domains DMC propagation parameters were estimated by the RiMAX-based estimator that was
explained in Section II. In the data analysis, the maximum
specular path numbers were changed from 10, 20, 40, 80,
and 120. The path discarding threshold ξ was 20 dB. Paths
whose powers were lower than the highest path power byξ were discarded. The RSC, which is the residual signal
component after subtracting the SMC from the measured data,
was calculated from the estimation results. The signal ratio of
the RSC to the whole measured data, and the delay-angularcharacteristics of the RSC were discussed. Next, the delay,angular, and polarization domain DMC propagation parameters were estimated. Although the DMC characteristics aredefined by the correlation matrix R as shown in eq. (7), there
is an ambiguity in terms of the normalization of RF (θ d,F )and RA (θ d,A ). For example, mRF (θ d,F ) ⊗ RA (θ d,A ) =RF (θ d,F ) ⊗ mRA (θ d,A ). In this paper, we normalized the
DMC power gain γd = [γd,vv , γd,vh , γd,hv , γd,hh , γα ] forpresenting the results.
γd,vv + γd,vh + γd,hv + γd,hh + γα = 1

The DMC propagation parameter distributions in each environment are then shown. Finally, 4-by-4 MIMO channel matrices
were generated from the estimated propagation parameters,
and the eigenvalue structures of the generated channels werecompared with the measured data.

photo viewpoint

18 m
Measurement Course2

θTx

Measurement Course1

1m

Tx
6.9 m

θRx

Point2

Point1(10)Rx(C) Museum

IV. 11 GH Z - BAND C HANNEL M EASUREMENT R ESULT
A. Residual Signal Component Characteristics
Fig. 5 shows the residual power ratios of all cases ofmaximum path numbers. The residual power ratio pRSC is
defined by measured data x and RSC r as follows.
r =x−

I
X

s(θ s,i )

(11)

|r|2
|x|2

(12)

i=1

Fig. 3. The floor layouts of measurement areas: ((A) Room, (B) Hall, and(C) Museum)).

pRSC =

2 in all areas. The Tx/Rx array directions are also shown,and the AAoA and the AAoD were defined from the front
directions of the instruments in a counter-clockwise direction.
The statistical distributions of the propagation parameters ineach environment were estimated from the measurements. The
second kind of measurement is a 3D measurement by using thevirtual array method. In this measurement, the Rx was fixedat Point 1 and the Tx was fixed at Point (a) in the hall. To
investigate the elevation domain propagation characteristics,
we conducted the measurements by changing the Rx antennaheight from 1.7 m, 1.714 m, 1.728 m, to 1.742 m. The step
length of the height approximately corresponded to the halfwave length, which is 13.6 mm in the 11 GHz band. We
evaluated the SMC and Residual Signal Component (RSC)The figure shows the mean of the residual power ratios of
all moving measurement snapshots in each area. Although
the residual power ratios monotonically decreased as the
maximum path number is increased, the slopes of the graphs
became gradually smaller. The residual power ratios remained
more than 30% in all cases. The RSC is thought to consist of
DMC mainly, and the result showed the difficulty in modelingthe DMC accurately by the superposition of a finite numberof plane waves. The residual power ratio was highest in theroom, followed by the hall, and then the museum. The reason
is thought to be that the scattering of waves by the walls,
floor, and ceiling was more significant in the small rooms. In
this paper, we set the maximum path number to 80 for thefollowing detailed evaluations. The residual power ratios were0.47, 0.35, and 0.31, respectively.

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

5(A) Room(B) Hall(C) Museum

Fig. 4. Photos of measurement areas: ((A) Room, (B) Hall, and (C) Museum)).

1
Room
Hall

0.8

Residual Power Ratio

Museum

0.6

0.4

0.2

0

0

20

40

60

80

100

120

Number of paths

Fig. 5. Comparison of residual power ratio for various maximum pathnumbers in several environments

Next, we present the 3D measurement result. In Fig. 6, a
panorama photo from the Rx-side view at Point 1 in the hall
is shown. The delay-AAoA domain SMC estimation result of
the 3D-measurement when the Tx was at Point (a) in Fig.
3 is also shown. In the SMC result, the paths formed someclusters. The first cluster was present approximately at 70 ns of
the propagation delay and -80◦ of the AAoA. They are thought
to be direct propagation paths from the Tx. There was also acluster approximately at 120 ns of the propagation delay and80◦ of the AAoA. It is thought to consist of the reflection
paths from the left-side wall in the map. Other clusters thatwere reflected from the walls were also observed.
In Fig. 7, the PDP of the 3D measurement data, the SMC
reconstructed from the SMC propagation parameters, and the
RSC at Point (a) are shown. The result shows that although
the part of the signal component whose propagation delay wasapproximately less than 200 ns was mainly estimated as the
SMC, the other part remained as the RSC. The RSC remained
significantly also in the early reflection region. The RSC PDP
followed the exponential decay distribution, which is describedin [15] [16]. The RSC peak level was approximately 10 dBlower than the SMC peak.
Fig. 8 shows the RSC power spectrum of both the delay-

AAoA and delay-EAoA domains. As described in [22] [23],the RSC was distributed around the SMC that was shown in
Fig. 6. Especially, the angular spread was wide in both azimuthand elevation domains in the early reflection region where
the propagation delay was less than 200 ns. It indicates that
the RSC consisted of the single bounce irregular scatteringfrom the floor, ceiling, and walls in the region. In the multiple
reflection region where the propagation delay was more than
200 ns, the RSC was mainly observed approximately at 80◦
and -80◦ of the AAoA and 90◦ of the EAoA. It shows that
the RSC consisted of multiple reflection waves between theleft-side wall and the right-side wall of the map. Because
the RSC level was much higher in the early reflection region
than the multiple reflection region, the DMC modeling in
the early reflection region is thought to be important. The
appropriate DMC modeling is also expected to improve the
SMC estimation accuracy in the multiple reflection region
because it is not detected correctly by the ML-based estimator
due to the lower power compared with the peak power of the
DMC.
B. DMC parameter distributions
In Fig. 9, the transitions of τD , µR , and µT when the Rx
was fixed at point 1 and the Tx was moved along Course 1 in
the hall are shown. τD , µR , and µT represent the peak positionof the DMC in the delay and angular domains. In the figure,the LoS directions and the LoS propagation delays which aretheoretically calculated are also shown. The result shows thatthe transitions of τD , µR , and µT were close to the valuesof LoS propagation. This result also shows that the scatteringfrom the floor and the ceiling between the Tx and the Rx were
one of the significant sources of diffuse scattering.
In Fig. 10, the Cumulative Distribution Functions (CDFs)
of α1 in all measurement areas are shown. α1 represents thepeak power of the DMC. To mitigate the influence of pathloss
variations due to the distance differences between the Tx andthe Rx, α1 was normalized by the highest path gain of thepaths, which almost corresponded to the LoS propagation pathgain. The medians of α1 ranged from -12 dB to -9 dB, andit was significantly higher than the value of -20 dB in an
outdoor environment in the same frequency band [33]. The
result showed that the contributions of the DMC to the MIMO
channel characteristics are not negligible in X band.

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

6

Propagation delay (ns)400

Reflection fromthe left-side wall

300

LoS Paths

Reflection fromthe upper-side wall

Reflection from thewall behind the Rx

200
100
120

180

60

0

-120

-60

-180

azimuth AoA (deg.)(B) Specular Multipath Component (SMC)

Tx(point(a))Rx(A) Panorama photo from the Rx-side viewFig. 6. 3D Measurement results at Point (a) in the hall ((A)Panorama photo from the Rx-side view, (B) Specular multipath component.

parameter are presented. The Cross-Polarization Ratio (XPR)
γxpr , and the Co-Polarization Ratio (CPR) γcpr of the DMC
were calculated from the DMC power gain γ d as follows:-70

Measured Data

Measured Data
SMC

Power delay profile (dBm)

-80

RSC

The peakLevel of RSC

γd,vv + γαγd,vh + γαγd,vv + γαγcpr =
γd,hh + γαγxpr =

-90

RSC
-100

SMC

-110

-120

0

100

200

300

400

500

Propagation delay (ns)Fig. 7. The Power delay profile of 3D measurement at Point (a).

In Fig. 11, the CDFs of κ of both the Rx and the Tx sidein all measurement areas are shown. κ represents the angularspread of the DMC. The medians of κ ranged from 0.4 to
0.8 in all the areas, and they corresponded to the range from80◦ to 90◦ of the angular spreads. Although the DMC angular
distribution was not uniform as described in [15] [16], the total
spreads were quite large in indoor environments. Regarding α1and κ, the CDFs matched the normal distributions well. For
practical purposes, we also showed the fitted lines of normal
distributions in the figures. n(µ, σ) represents the normal
distribution whose mean is µ and the standard deviation isσ.
In Table II, we summarized the estimated DMC propagation
parameters. The means and the standard deviations of each

(13)
(14)

We also showed the XPR and CPR of the SMC for thecomparison. The XPR and CPR of the SMC were calculated by summing the path gains of each polarization setting[|γs,vv,i |2 , |γs,vh,i |2 , |γs,hv,i |2 , |γs,hh,i |2 ] for all the paths. The
mean DMC XPRs ranged from 5.2 dB to 7.7 dB, whichwere 4 or 5 dB lower than the SMC XPRs. The DMC XPR
tends to be several dBs lower than the SMC also in thelower frequency measurements [19] [20] [21]. The mean DMC
CPRs were approximately 1 dB, which were almost the sameas the SMC CPRs. The DMC had significant polarization
dependencies. The reason why the DMC XPR was high isthought to be that the DMC mainly originated from diffuse
scattering on the floor, the ceiling and walls in the area,
and the polarization planes of the scattered waves were notchanged significantly, because the scattered surfaces were
almost horizontal or vertical.
Bd ranged from 18.2 MHz to 25.5 MHz, which werehigher than the measurement results in the 3GHz band [18].
However, because the frequency characteristics also depend on
the measurement environments, such as the floor layout andthe wall materials, a careful comparison will be necessary. As
shown in Fig. 5, the residual power ratios were highest in theroom, followed by the hall, and then the museum. With respect
to the differences of the DMC propagation parameters in eachmeasurement area, the mean α1 was highest in the room. In

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

7

80
μ

150

μ
τ

0

40

d

LoS direction-50
20

LoS direction
LoS propagation delay

-150
0

5

10

15

20

0

Tx Travel Length (m)(A) Delay-AAoA domain power spectrum

Multiple reflection region

60

50

-100

Early reflection regionTdtauτ (ns)
(ns)

μ ,μ

(deg.)

μRR, μTT (deg.)

100

R

Fig. 9. The transitions of τD , µR , and µT in course 1 in the hall.

1n(−11.2, 3.2)

0.8

CDF

0.6

0.4

0.2

n(−9.1, 1.9)
Roomn(−11.2, 3.7)

Hall
Museum(B) Delay-EAoA domain power spectrum

0
-20

-15

-10

-5

0

normalized(dB)
Normalizedalpha1α1 (dB)

Fig. 8. The RSC power spectrum of 3D measurement ((a) Delay-AAoA
domain, (b) Delay-EAoA domain).

addition, the mean Bd and the mean κ were smallest in theroom. These results showed that the influence of irregular
scattering was more significant in the small room, which
increased the DMC angular spread and decreased the DMC
PDP decay factor. Those tendencies followed in the order ofthe hall and the museum. From the comparative results, it isthought that room size is one of the key factors that determine
the characteristics of the DMC.
C. Channel Reconstruction and Eigenvalue Characteristics
The characteristics of MIMO channel matrices that werereconstructed from the propagation parameters are investigated
for the validation of the estimation results. We assumed thesame antenna responses as the measurement to clarify the
influence of the DMC existence on the accuracy of the channel
reconstruction. The matrices were reconstructed from theestimated SMC and DMC propagation parameters according
to eq. (8) and eq. (9). Fig. 12 shows the received powerof all MIMO channels when the Tx was at Point (b) and
the Rx was at Point 1 in the hall. The received powers
1 The power is normalized by the highest path gain of the paths

Fig. 10. α1 CDF of each measurement area.

were calculated by summing the received powers of all delaybins. The figure shows the received power of the measured
data, the data reconstructed only from the SMC, and thedata reconstructed from both the SMC and the DMC. With
respect to the radiated region of the co-polarized condition,
the received powers were reconstructed fairly well by bothmethods. However, the received powers were significantly
underestimated in the shadowing region in the case that thedata were reconstructed only from the SMC. This is because
the contribution of the DMC in the region was relatively higher
than in the radiated region. The result shows that the received
powers are not correctly reconstructed in the shadowing region
without considering the DMC contribution.
Next, eigenvalues characteristics of the reconstructed
MIMO channel matrices are investigated. The channel generation method is shown in Fig. 13. We selected 4-by-4
MIMO channel matrices from the whole channel data at
each snapshot with a central focus on the LoS direction.
In the spatially-separated MIMO (SS-MIMO) condition, only
the vertical polarized propagation channels were selected. In
the dual-polarized MIMO (DP-MIMO) condition, 2 verticallypolarized propagation channels and 2 horizontally polarizedJOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

8

1

0.8

n(0.4, 0.2)

CDF

0.6

n(0.8, 0.4)

0.4

n(0.7, 0.3)
Room

0.2

Hall
Museum

0

0

1

0.5

2

1.5

κ

Kappa

Fig. 11. κ CDF of each measurement area.
TABLE IIDMC PROPAGATION PARAMETERS SUMMARY.
Room
mean
s.d.

Hall
mean
s.d.

Museum
mean
s.d.

-9.1
18.2
0.4
7.7
1.5

-11.2
21.8
0.7
6.8
1.0

3.2
3.2
0.3
0.7
0.9

-11.2
25.5
0.8
5.2
1.1

3.7
3.6
0.4
0.8
1.5

SMC polarization characteristics (for references)
XPR (dB)
11.4
1.6
10.1
1.3
CPR (dB)
1.1
1.4
1.0
1.9

10.7
1.4

1.4
1.7

DMC parameterα1 1 (dB)
Bd (MHz)
κR,T
XPR (dB)
CPR (dB)

1.9
1.8
0.2
0.6
0.3

propagation channels were selected. Fig. 14 and 15 show 4
eigenvalues CDFs of each MIMO condition. The figures show
the mean eigenvalues of all subcarriers, and the eigenvalues
are normalized by the medians of the first eigenvalues of themeasured MIMO channels. In the SS-MIMO condition, thefirst eigenvalues were prominent owing to the strong LoS
signal component, and they had good agreement with themeasurement in both reconstruction methods. However, thesecond and the following eigenvalues were significantly underestimated in the case that the channel were reconstructed only
from the SMC. The median eigenvalue reconstruction errors
were -0.7 dB, -3.2 dB, -4.6 dB, and -7.8 dB, respectively.
The errors were less than 1.4dB when the DMC contribution
was considered, and this shows the validity of our DMC
analysis results. In the DP-MIMO condition, although the firsttwo prominent eigenvalues were fairly reconstructed in bothreconstruction methods, the consideration of the DMC wasalso indispensable to improve the reconstruction accuracies ofthe following eigenvalues. The result showed that the DMC
consideration is necessary to prevent the underestimation of
the MIMO transmission performance.
V. C ONCLUSION
In this paper, 11-GHz-band MIMO channel measurement
results in LoS indoor environments was presented to clarify

the propagation characteristics of the DMC in X band. We
jointly estimated the frequency, angular and polarization domain DMC propagation parameters by the extended RiMAX
based estimator to deal with the DMC contribution to the
MIMO channel quantitatively. In the measurements, the residual power ratios when we assume only the SMC were almost
more than 30% in all areas. Through the 3D measurement byusing the virtual array method, we showed that the originating
source of the RSC in the early reflection region is the irregular
scattering from the floor, ceiling, and walls in the environment.
The RSC was significant in the early reflection region, andits peak level was approximately 10 dB lower than the SMC
peak. Although the frequency domain distributions of the
DMC almost followed the RiMAX DMC model, the DMC had
angular and polarization dependencies, which are not present
in the RiMAX DMC model. The median DMC angular spreads
were from 80◦ to 90◦ , and the DMC XPR was from 5.2 dB
to 7.7 dB. The results also show that the angular spreads
tended to increase and the decay factor tended to decrease as
the room size decreased owing to more significant scattering
effects from the environment.
For the validation of the modeling, the MIMO channel
matrices were reconstructed from the estimated propagation
parameters and compared with the measurement data. The
comparative result showed that the receiving power was underestimated in the shadowing region if the DMC was not taken
into account. The second and the following eigenvalues of the
4-by-4 MIMO channel matrices were also underestimated evenin the radiated region. However, we found that the eigenvalues
structure was reconstructed fairly well by considering theDMC contribution mainly focused on the RSC characteristics
in the early reflection region, and the median reconstruction
errors of eigenvalues were less than 2 dB.
Creating a novel higher frequency band MIMO channel
model that includes the DMC contribution will be a future
work.
ACKNOWLEDGMENT
This work was partly supported by ”The research and development for expansion of radio spectrum resources” of The
Ministry of Internal Affairs and Communications (MIC), JSPS
KAKENHI Grant Number 15H04003, and JSPS KAKENHI
Grant Number 16K18102.
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10

Kentaro Saito Kentaro Saito was born in Kanagawa,Japan, in 1977. He received his B.S. and Ph.D.
degrees from the University of Tokyo, Japan, in 2002
and 2008, respectively. He joined NTT DOCOMO,
Kanagawa, Japan, in 2002. Since then, he has been
engaged in the research and development of mobile
communication systems, and radio propagation. He
joined Tokyo Institute of Technology, Japan in 2015.
Since then, he has been engaged in research on radio
propagation for mobile communication systems. Dr.
Saito is a member of IEICE and IEEE.

Jun-ichi Takada (S89-M93-SM11) received the
B.E., M.E., and D.E. degrees in electrical and electronic engineering from Tokyo Institute of Technology, Tokyo, Japan, in 1987, 1989, and 1992,
respectively. From 1992 to 1994, he was a Research
Associate with Chiba University, Chiba, Japan. From1994 to 2006, he was an Associate Professor with
Tokyo Institute of Technology, where he has beena Professor since 2006. From 2003 to 2007, he
was also a Researcher with the National Instituteof Information and Communications Technology,
Kyoto, Japan. From 2007 to 2010, he was a Co-Chair of the Special Interest
Group on Body Communications within EU COST Action 2100. His researchinterests include radio-wave propagation and channel modeling for mobile
and short range wireless systems, regulatory issues of spectrum sharing, andICT applications for international development. Dr. Takada was appointed an
IEICE Fellow in 2012. He was the recipient of the Achievement Award of
IEICE in 2008.

Minseok Kim (S02-M05) was born in Seoul, Korea. He received the B.S. degree in electrical engineering from Hanyang University, Seoul, Korea,
and the M.E. and D.E. degrees in electrical andcomputer engineering from Yokohama National University (YNU), Yokohama, Japan, in 1999, 2002,
and 2005, respectively. In 2007, he was an AssistantProfessor with the Tokyo Institute of Technology,
Tokyo, Japan, from which he was on leave at theGeorgia Institute of Technology, Atlanta, GA, USA,as a Visiting Scholar in 2010. In 2014, he joined
the Graduate School of Engineering, Niigata University, Niigata, Japan,
as an Associate Professor. His research interests include radio propagation
channel measurement and modeling, body area network, antenna array signal
processing, DSP implementation on FPGAs, cognitive and software definedradios. Dr. Kim is a member of IEICE. He was the recipient of the Young
Researchers Encouragement Award of IEEE VTS Japan in 2003.