Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
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Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
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Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 6

6J

1 A Novel 5G TDD Cellular System Proposal based on Multipath Division Multiple Access

936

Wei-Han Hsiao, Chia-Chi Huang
Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan, ROC

2 Coexistence of Korea’s DVB-T2 and Japan’s ITS using 700MHz frequency band

943

Ho-Kyung Son, Young-Jun Jung
Radio Technology Research Department, Electronic Telecommunication Research Institute, Daejon, Korea

3 Analytical Description of Chromatic Dispersion Effect on Signal Propagation in the Time Domain

948

Mikhail Meltenisov*, Aleksandr Matukhin**Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg
State University of Telecommunications, Bolshevikov Ave. 22, Saint-Petersburg, Russia

4 Dynamic Analysis of Rotor Blade System

954

Liu YiSchool of Aerospace Engineering, Beijing Institute of Technology, Beijing, China

5 Formulating Closeness Centralities on Workflow-supported Performer-Activity Affiliation Networks

960

Hyunah Kim*, Kwanghoon Pio Kim*
*Dept. of Computer Science, KYONGGI UNIVERSITY, Suwonsi Kyonggido, Republic of Korea

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016936

A Novel 5G TDD Cellular System Proposal
based on Multipath Division Multiple Access
Wei-Han Hsiao, Chia-Chi Huang
Department of Electrical and Computer Engineering, National Chiao Tung University,
Hsinchu, Taiwan, ROC
whsiao.cm97g@g2.nctu.edu.tw, huangcc@faculty.nctu.edu.tw
Abstract—Evolving from the 3G and the 4G communication
systems, the 5G system demands both high system capacity andhigh data rate. A novel time division duplexing (TDD) cellular
system based on multipath division multiple access (MDMA)
with massive antennas in millimeter wave band is proposed inthis paper. The system is built on multipath division multiple
access which is a method to use massive antennas at BS alongwith the Rake receiver and the Pre-Rake transmitter to achieve
a processing gain for suppressing multiple access interference.
The system concept is demonstrated by computer simulations. In
addition, the associated transceiver architecture and a TDD timeslot structure are presented for practical system concerns.
Moreover, it is shown through analysis that the total averagedata throughput equals 3.8 Gbps and the system can achieve abandwidth efficiency of 19 bps/Hz/cell on 200 MHz transmission
bandwidth.
Keyword—5G communication, cellular system, massive
antennas, millimeter wave, TDD

I. INTRODUCTION

R

ecently, many studies have sprung up in the world [1]-[8]for 5G systems. According to the IMT-2020 released in2015 [9], stringent system requirements are specified for
future 5G systems. On the link level and system level, the next
generation 5G mobile radio communication system demands
both the high data rate and high system capacity.
Potential technologies for the future 5G system are in thescope of heterogeneous networks, millimeter wave (mmWave)transmission, and massive multiple-input multiple-output(massive MIMO) antennas [8]. The first scheme basically
evolves from the Long Term Evolution (LTE) and allows
different kinds of cells to function and cooperate in the samegeographical area. The second scheme makes use of theunexplored spectrum in the millimeter wave band since it not———————————————————————
Manuscript received October 24, 2016. This work is a follow-up of theinvited journal to an accepted out-standing conference paper of the 18 thInternational Conference on Advanced Communication Technology(ICACT 2016). This research is supported by the Ministry of Science andTechnology (MOST) of R.O.C.
W. H. Hsiao is with the Department of Electrical and Computer
Engineering, National Chiao Tung University, No.1001, Daxue Rd., East
Dist., Hsinchu City 300, Taiwan (R.O.C.) (Corresponding author, Phone:+886-3-5712121 ext. 54579; e-mail: whsiao.cm97g@g2.nctu.edu.tw).
C. C. Huang is with the Department of Electrical and Computer
Engineering, National Chiao Tung University, No.1001, Daxue Rd., East
Dist.,
HsinchuCity
300,
Taiwan(R.O.C.)(e-mail:huangcc@faculty.nctu.edu.tw).

only avoids frequency spectrum congestion problem below 6
GHz but also provides enough bandwidth for high data ratetransmission. Nevertheless, it leads to the inherent problem ofmuch larger propagation loss in much higher frequency bands.
The third approach considered employs a large amount ofantennas at BS side [10]-[16], usually tens to hundreds ofantennas, that offers substantial degrees of freedom forbaseband signal processing. Combining millimeter wave
transmission with the massive antennas at BS side, this paper
presents a multipath division multiple access (MDMA) [17]time division duplexing (TDD) cellular system that is able toprovide both the high data rate and high system capacity.
Contributions of this article are briefed in the following. A
novel cellular system built upon MDMA for the 5G mobile
communication is proposed. In the uplink (UL), MDMA
distinguishes its users by exploiting their distinct multipathcharacteristics through employing RAKE receivers withmassive antennas at BS. Similarly, the Pre-RAKE precoding
technique with massive antennas is adopted at BS in thedownlink (DL). Moreover, every user terminal (UT) is
equipped with single antenna that greatly alleviates the signal
processing burden. It is shown in Section III that both system
capacity and the aggregated data rate can be boosted up to an
appreciable extent. Thus, the proposed MDMA TDD cellular
system is a promising candidate for future 5G systems.
The paper is organized as follows. Section II gives theradio system architecture, including the transceiverarchitecture, the associated time slot structure, and asimplified analysis. Section III uses computer simulations as
an auxiliary method to illustrate the system concept andprovides a simplified analysis to verify the performance of the
MDMA system. Finally, the paper completes withconclusions in Section IV.
II. SYSTEM ARCHITECTURE
The proposed MDMA cellular system layout is shown in
Fig. 1, which operates on the premise of the following
assumptions.
1)

2)

The system exploits massive antennas operating in themmWave band, e.g., 30GHz, such that the size of eachantenna (e.g., dipole antenna) is very small.
The BS antennas are separated by every other 10
wavelengths (at least) to make their received signals
nearly uncorrelated [18]. For example, if we arrange 100
antennas in a two-dimensional square grid, then the total

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016937

2

respectively. Assume the transmit power E sku (t )  1 forsimplicity, where E is the expectation operator.
The multipath channel between the kth user and the j-th BS
antenna isLhkj (t )  lkj  (t   lkj ) , k  1, 2,...K . j  1, 2,...M ,

(2)

l 1

where αlkj and τlkj represent the complex gain and the delay ofthe l-th path. For normalization, we assume the powerLgain  E  lkj

2

 1.

l 1

Thus, the received signal at the j-th BS antenna from thek-th user readsL

vkj (t )    lkj p(t   lkj  nTb ) sku (n)
l 1

n

(3)

 s (t )  p(t )  hkj (t ) ,ukFig. 1. The proposed MDMA 5G cellular system layout.

where p(t) is the impulse response of the transmit filter
with 

area occupied is about 1 m2, which can be easily
deployed in real environments.
3) Channel bandwidth of 200 MHz is assumed in oursystem. With such wideband transmission, the rich anddistinct multipath components of each individual user
can be resolved, which helps to distinguish all the users.
4) Time division duplexing (TDD) is adopted here, thus weassume channel reciprocity in uplink and downlink
transmissions.
5) UL channel state information (CSI) is known at BS
through channel estimation, which can also be used forprecoding in DL transmission.
6) Power control is executed in the uplink, i.e., the received
power level of each user at BS is nearly equal.
7) A UT is always served by the base station that provides itwith the largest received power level.
8) The proposed cellular system is interference limited, i.e.,the background noise can be ignored as compared with
the interferences.
The equalization of the channel is done in both time andspace domains via the Rake receiver and Pre-Rake [19]transmitter in the uplink and downlink, respectively. This
leads to a huge signal-to-interference plus noise ratio (SINR)
gain for each user when massive antennas are used in the BS.
The resultant spatial processing gain is analogous to a CDMAsystem’s processing gain and is effective in suppressing
intersymbol interference (ISI), multiple access interference(MAI), and cochannel interference (CCI).

p 2 (t) dt  1 and  denotes the linear convolution

operator.
Hence, the output of the k-th user’s Rake receiver at the j-th
BS antenna equals toKukj (t)   vkj (t)   vqj (t)  CCI  n j (t) 


q 1
qk


L

   l'kj p*(t   l 'kj )
l '1

K
 u

u
 sk (t)  p(t)  hkj (t)   sq (t)  p(t)  hqj (t) 
q 1
 

qk


  CCI  n j (t) p(t)  hkj (t) ,(4)
where nj(t) is the additive white Gaussian noise at the j-th BS
antenna and the cochannel interference (CCI) comes fromother cells. Note that the Rake receiver here is represented byp(t)  hkj (t) , where  denotes the complex
conjugation.
Finally, the BS detects the n-th data bit of user k accordingM

to the decision metric  ukj (t ) at t = nTb.
j 1

A.

Uplink Transceiver Architecture
Fig. 2(a) shows the block diagram of the uplink MDMA
transceiver [17]. Consider a multi-user scenario with K
single-antenna users and an M-antenna BS in each cell.
Assume the binary phase shift keying (BPSK) modulation isused. Then the transmit data bit stream of user k can be writtenassku (t )   sku (n) (t  nTb ) , k  1, 2,...K ,(1)nwhere the superscript u, δ(∙), and Tb denote the uplinktransmission, the Dirac delta function, and the bit time,B.

Downlink Transceiver Architecture
Fig. 2(b) shows the corresponding block diagram of thedownlink transceiver. The data bit stream of each user isprecoded by the Pre-Rake transmitter for each BS antenna.
Meanwhile, the required CSI is obtained through uplinkchannel estimation and it is used for Pre-Rake precoding. It is
worth noting that the Pre-Rake precoding moves theequalization effort to the BS transmitter that helps to reducethe complexity of the UT receiver.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

938

(a)

(b)
Fig. 2. MDMA transceiver architecture for (a) uplink and (b) downlink.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016939

The output of the user k’s Pre-Rake transmitter for antennaj is
xkj (t )  skd (t )  p (t )  hkj (t )L(5)   lkj p (t   lkj  nTb )skd (n) ,l 1nwhere the superscript d denotes the downlink transmission.
K

Thus, the transmitted signal at the j-th antenna is  xkj (t ) ,k 1i.e., the sum over all the users’ Pre-Rake transmitter outputs
for the j-th BS antenna.
In the user terminal, the data is detected by sampling thereceived signal at the correct sampling time and making
decisions accordingly.

Fig. 3. The proposed time slot structureM  squ (t )   p(t )  p (t )   hqj (t )  hkj (t )j 1 q  k

C. Simplified Analysis
An analysis of the single cell scenario is considered here
from link level point of view while the multi-cell scenario isdiscussed in Section III from system capacity point of view.
Since the Rake reception and the Pre-Rake transmission
provide the same signal-to noise ratio (SNR) as proved in [19]for single BS antenna case, we only show here the uplink SIR
performance of the system with massive antennas at BS.
Assume user k represents the desired user. Insert (3) into (4)and neglect MAI, CCI and noise terms for now, we get  Lukj (t)      lkj p(   lkj  mTb )sk (m)  l 1 m

(6)
L



*
    l 'kj p (t     l 'kj )  d  .
 l '1Here, we use a bandlimited filter p(t ) since it satisfies not only 





 t 
1
sinc  Tb
 Tb 

p 2 (t) dt  1 but also the Nyquisti i 
criterion, i.e., peff (iTb )   [],, where  [n] is the

Kronecker delta function and peff (t)  p(t)  p (t) .
Thus, sampling (6) at t = nTb results in  Lukj (nTb )      lkj p(   lkj  mTb )sku (m)  l 1 mL    l'kj p(nTb     l 'kj )  d 
 l '1  L
2
     lkj p 2 (   lkj  nTb )sku (n) d   ISI

l

1


 sku (n)  ISI .
(7)
The last equality holds due to wideband transmission, say
200 MHz in our system. That is,  l 1  lkj
L2L  E  lkj  12l 1

for large L. Combine coherently all the Rake receiver’s
outputs and sample at time nTb for user k, one can get thedesired signal as Msku (n) , i.e., the desired signal power
becomes M 2 times of that in the single-antenna case.
Since the interference mainly comes from other users in thesame cell, i.e., MAI can be modeled asM  squ (t )  peff (t )  hqj (t )  hkj (t ) .

(8)

j 1 q  k

Due to the channel’s randomness and normalization among
all users, the interference power from MAI in (8) can becalculated as M  K  1 according to [23, p.5]. Note that weassume the channel has unit power gain. In fact, the result is adirect consequence of the noncoherent addition of theinterference terms over all receive BS antennas.
We can easily calculate the equivalent Eb I 0 of each user
and the result is approximately equal to M / K when M is large.
Since massive antennas are used at BS, the equivalent Eb I 0
could be boosted up to a required amount. In other words, theend-to-end equivalent channel of each user gradually
approaches an ideal channel, an impulse-like channel, when
M is large. Thus, M is the processing gain which can beoffered by the MDMA cellular system. Similarly, an equalamount of processing gain can be achieved for each DL user
through Pre-RAKE precoding.
D. Time Slot Structure
Since the channel coherence time at a carrier frequency of 1
GHz and a vehicle speed of 100 km/hr is roughly 600 μs1 [20],the corresponding coherence time at 30 GHz is about 20 μs,
because it is inversely proportional to the carrier frequency.
Therefore, the system we considered needs to estimate thechannel with a time period of 20 μs. Fig. 3 shows the time slot
structure of the TDD cellular system. A time slot (TS)duration of 10μs amounts to 2000 bits for both UL and DL.
Two DL time slots directly follow two UL time slots.
Together with the data signal, the UL time slots also transmit apilot signal with the same power level as the data signal for the
purpose of channel estimation. Furthermore, a 10 μs
processing time is reserved such that the channel estimation
result in the first UL TS can be used for the first DL TS while
the second UL TS channel estimation result is used for thesecond DL TS, as the red arrows point out in Fig. 3. Although
this pilot-added approach inevitably causes extra interference
for UL data detection, advanced signal processing techniquessuch as iterative cancellation can be applied to reduce theinterference effect. The relevant channel estimation andinterference cancellation techniques, out of the scope of thisarticle, are treated in another paper.

Mzk (t )   squ (t )  p(t )  hqj (t )  hkj (t )  p (t )j 1 q  k

1 Recall that the packet duration in GSM is 577 μs, in this time period thechannel can be regarded as quasi-static at a vehicle speed of 100 km/hr in 1
GHz band [20].

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

III. COMPUTER SIMULATION AND SYSTEM CAPACITYEVALUATIONIn the computer simulations which are used to illustrate the
MDMA cellular system concept, we modify the S-V channel
model according to the spatial parameters given in [21]. First,
we generate the number of clusters by the Poisson distribution.
Then, the arrival time of the different clusters is set to beuniformly distributed within the maximum delay spread, e.g.,404.1 ns [22]. Second, we calculate the power of each cluster
using the model of [21]. Third, we generate the inter-arrival
time of each ray within individual cluster according to the
exponential distribution. Finally, we calculate the power of
each ray. In addition, the time resolution of multipath in our
5G system is equal to 5 ns (due to 200 MHz channel
bandwidth), which is the same as the time resolution in theoriginal S-V model. Thus, the path delay is quantized to thenearest integer multiple of 5 ns. Note that a ray will be
dropped when its power is less than a predefined threshold,e.g., 10 dB below the strongest path. A realization of thechannel impulse response is plotted in Fig. 4.
Fig. 5 shows an effective end-to-end channel of a desireduser in a single cell which serves 25 users simultaneously. The
effective end-to-end channel of user k is defined here as theMKdiscrete-time version of  hqj (t )  hkj (t ) . It is apparent
j 1 q 1

that as the number of BS antennas increases, the interferences
get more suppressed relative to the desired signal, which
agrees with the simplified analysis in Section II C when M
grows large. That is, the interferences including MAI and ISI
can be suppressed with a large number of BS antennas.
Moreover, the power of the desired signal is nearly M 2 times
as expected.
Fig. 6 plots the cumulative distribution of the uplinkreceive SIR with 100 BS antennas and different number ofusers in a multi-cell scenario. In the simulations, the cellular
system is constructed with 127 hexagonal cells (each with aradius of 250 m) which include 6 tiers of cochannel cells. For
simplicity, assume uplink channel is known at BS in advance.
Some observations can be made here. First, it can be observedthat the mean of receive SIR is proportional to M / K whichmatches the simplified analysis result in the previous section.
Similarly, the SIR distribution gets improved as the ratio of
M / K increases. On one hand, the mean of the receive SIR isless than M / K by a factor of 2 / 3 (-1.7 dB) which is caused by
all the other cell interference. On the other hand, variations
around the mean of the receive SIR diminish as the number ofusers increases due to the law of large numbers. That is, thereceive SIR converges to its mean as more users are served in
the system. Fig. 7 plots the cumulative distribution of thedownlink receive SIR. Again, it reveals that the mean ofreceive SIR is proportional to M / K as in the downlink.
However, the receive SIR distribution is worse than the uplinkcase since higher tail probability can be observed. This isbecause there is no power control in the downlink, whichcauses more serious cochannel interference problems.
Here, we define f99% to be the other-cell relativeinterference factor for the worst 1% in the SIR cumulative
distribution functions, i.e., the probability that the SIR value
exceeds a predefined SIR threshold is greater than 0.99. In[24], f99% is found to be 0.7 in the uplink with power control,
while it is relatively larger and is about 3.6 in the downlink

940

without power control. By examining the initial portion ofFig. 6 and Fig. 7 closely, we found these values ( f99% = 0.7 or
3.6) are consistent with our simulation results with hexagonalcell layout, such that they are applied here.
Toward the MDMA TDD cellular system, a simple
evaluation of its system capacity is given here. Recall that thesystem considered operates in the carrier frequency of 30
GHz with a channel bandwidth of 200 MHz, and has afrequency reuse factor of one. Also note that K and M are thenumber of users in a cell and the number of BS antennas
respectively. Assume BPSK modulation and ideal power
control in the uplink. In the worst case, the system is in fullloading, i.e., K users are transmitting concurrently. In thissituation, the SIR at each user’s demodulator output is thus
Eb
S
M2
1
M
1





,
I0
I
M (K  1) 1  f99%
K  1 1  f99%
(9)
where Eb and I0 represent the received energy per bit and theinterference power spectrum density. S and I are respectively
M2
the average signal power and interference power.
M (K  1)
comes from the fact that the desired signal of each BS antenna
adds coherently while the interference sums up
noncoherently.
Rearranging (9) leads to
M
1
,
(10)
K 

Eb I 0 1  f99%
which gives an elegant formula to calculate the number ofusers the BS can serve under the required Eb / I0 at each user’s
demodulator output.
We consider first data transmission in the uplink time slots
as described in Section II with half power allotted to data andpilots. Assume a minimum Eb / I0 of 6 dB for data detectionthat provides the acceptable performance [23, pp.8], 300 BS
antennas can serve 22 full loaded users in every cell300
11simultaneously, since  22 , where the4
1  0.7 2
last one-half factor counts for the reduction in SIR due touplink pilots. Now we consider transmission in the downlink
time slots. Since there are no pilots used in the downlink, thetotal number of simultaneous users which can be served turns
300
1
 16 . In a real system, the number
out to be4
1  3.6of simultaneous users can be greatly increased if they are notfull loaded.
Since each user in the cell shares the whole 200 MHz
bandwidth, the proposed TDD cellular system can at least
provide a total average data throughput of 200 Mbps 1
 3.8 Gbps . Thus, the system achieves a2
bandwidth efficiency of 19 bps/Hz/cell.
Note that the system capacity can be further enhanced if
multi-user detection techniques (e.g., successive interference
cancellation or parallel interference cancellation) are used toeliminate intra-cell interference. The system capacity can beincreased since the factor 1 / (1+f99%) in (10) could be
replaced by 1 / f99% if the intra-cell interference can be totallycancelled.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

Fig. 4. A realization of the channel amplitude response.

Fig. 5. An effective end-to-end channel of a desired user in a full loading cellwith 25 active users.

941

Fig. 7. The cumulative distribution of the downlink receive SIR with 100 BS
antennas and different number of active users in a multi-cell scenario.

transmitter to achieve a processing gain to suppress multiple
access interference in a TDD cellular mobile radio system.
The transceiver architecture and the TDD time slot structure
are described in the article. On the other hand, the user
terminal is kept simple because only single antenna is used forevery user. From simulations and simple analysis, it is shownthat both system capacity and aggregated data throughput canbe boosted up to a considerable level for a cellular systemwith a frequency reuse factor of one. The system can serve on
average 19 (  (22  16)  0.5 ) users in each cell with the total
average data throughput of 3.8 Gbps on 200 MHz
transmission bandwidth which achieves a bandwidth
efficiency of 19 bps/Hz/cell. In brief, the proposed MDMA
TDD cellular system can serve as a candidate systemarchitecture for future 5G wireless communication.
REFERENCES[1]

Fig. 6. The cumulative distribution of the uplink receive SIR with 100 BS
antennas and different number of active users in a multi-cell scenario.

IV. CONCLUSION
The next generation (5G) cellular communication system
demands both high system capacity and high data rate. A
novel cellular system architecture built upon MDMA is
proposed for the future 5G cellular mobile communication.
MDMA is a multiple access method to use massive antennas
at BS together with the Rake receiver and the Pre-Rake

METIS website. (2015) [Online]. Available:
https://www.metis2020.com[2] European
Commission.
(2013).
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Non-Orthogonal Waveforms for Asynchronous Signaling. [Online].
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cts/5gnowfactsheet.pdf[3] Alcatel-Lucent. (2011). LTE and beyond, [Online]. Available:
http://web.dit.upm.es/~doct/SI/2011-2012[4] NTT DOCOMO, “Future Radio Access for 5G”, white paper, Jul. 2014
[5] W. Roh. (2013). Performances and Feasibility of mmWave
Beamforming Prototype for 5G Cellular Communications. Presented atIEEE ICC. [Online]. Available: http://www.faculty.poly.edu/~tsr[6] G. Wunder et al., “5GNOW: Challenging the LTE Design Paradigmsof Orthogonality and Synchronicity,” 2013 IEEE 77th VehicularTechnology Conference (VTC), Jun. 2013.
[7] A. Osseiran et al., “The foundation of the mobile and wirelesscommunications system for 2020 and beyond: challenges, enablers andtechnology solutions,” 2013 IEEE 77th Vehicular Technology
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[8] J. G. Andrews et al., “What will 5G be?,” IEEE Journal on Sel. Areasin Communications, vol. 32, no. 6, pp. 1065 –1082, Jun. 2014.
[9] ITU website. (2015) [Online]. Available: http://www.itu.int/en[10] T. L. Marzetta, “Noncooperative cellular wireless with unlimited
numbers of base station antennas,” IEEE Trans. on WirelessCommunications, vol. 9, no. 11, pp. 3590 –3600, Nov. 2010.
[11] A. Pitarokoilis, S. K. Mohammed, and E.G. Larsson, “On the
optimality of single-carrier transmission in large-scale antenna
systems,” IEEE Wireless Communications Letters, vol. 1, no. 4, pp.
276–279, Aug. 2012.
[12] J. Hoydis, S. ten Brink, and M. Debbah, “Comparison of linear

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016precoding schemes for downlink massive MIMO,” 2012 IEEE
International Conference on Communications (ICC), pp. 2135–2139,
Jun. 2012.
[13] F. Rusek et al., “Scaling up MIMO: opportunities and challenges withvery large arrays,” IEEE Signal Processing Magazine, vol. 30, no. 1,
pp. 40–60, Jan. 2013.
[14] J. Zhang et al., “On capacity of large-scale MIMO multiple accesschannels with distributed sets of correlated antennas,” IEEE Journal
on Sel. Areas in Communications, vol. 31, no. 2, pp. 133 –148, Feb.
2013.
[15] J. Hoydis, S. ten Brink, and M. Debbah, “Massive MIMO in the
UL/DL of cellular networks: how many antennas do we need?,” IEEE
Journal on Sel. Areas in Communications, vol. 31, no. 2, pp. 160–171,
Feb. 2013.
[16] E. Larsson et al., “Massive MIMO for next generation wirelesssystems,” IEEE Communications Magazine, vol. 52, no. 2, pp.
186–195, Feb. 2014.
[17] W. H. Hsiao and C. C. Huang, “Multipath division multiple access for
5G cellular system based on massive antennas in millimeter wave
band,” the 18th Intl. Conf. on Advanced Communications Technology(ICACT), pp.741–746, Jan. 2016.
[18] C. Y. Lee, “Mobile radio performance for a two-branch equal-gain
combining receiver with correlated signals at the land site,” IEEE
Trans. on Vehicular Technology, vol. 27, no. 4, pp. 239–243, Nov.
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[19] R. Esmailzadeh and M. Nakagawa, “Pre-RAKE diversity combination
for direct sequence spread spectrum mobile communications systems,”
IEICE Trans. Communications., vol. E76-B, no. 8, pp. 1008–1014,
Aug. 1993.
[20] T. S. Rappaport, Wireless Communications: Principles and Practice,
2nd ed., Prentice Hall, 2002.
[21] M. R. Akdeniz, Y. Liu, S. Sun, S. Rangan, T. S. Rappaport, and E.
Erkip, “Millimeter wave channel modeling and cellular capacity
evaluation,” IEEE Journal on Sel. Areas in Communications, Sep.
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[22] S. Sun and T. S. Rappaport, “Multi-beam antenna combining for 28
GHz cellular link improvement in urban environments,” in Proc. IEEE
Globecom, Dec. 2013.
[23] A. J. Viterbi, CDMA: Principles of Spread Spectrum Communication,
Prentice Hall, 1995.
[24] C. C. Huang, “Computer simulation of a direct sequence spread
spectrum cellular radio architecture,” IEEE Trans. on VehicularTechnology, vol. 41, no. 4, pp. 544–550, Nov. 1992.

942

with National Chiao Tung University, Hsinchu, Taiwan, andcurrently as Professor in the Department of Electrical andComputer Engineering. His research areas are in mobile radio,
wireless communication, and cellular systems.

Wei-Han Hsiao was born in Taiwan, R.O.C.
He received the B.S. degree in electrical andcontrol engineering from National Chiao
Tung University (NCTU), Hsinchu, Taiwan,
in 2008. He is currently pursuing the Ph.D.
degree in communications engineering since2010 in NCTU. His current research interests
are on transceiver design and analysis for next generation
mobile communication systems.
Chia-Chi Huang was born in Taiwan,
R.O.C. He received the B.S. degree inelectrical engineering from National
Taiwan University in 1977 and the M.S. and
Ph.D. degrees in electrical engineering fromthe University of California, Berkeley, in1980 and 1984, respectively.
From 1984 to 1988, he was an RF and communication
system engineer with the Corporate Research andDevelopment Center, General Electric Company,
Schenectady, NY, where he worked on mobile radio
communication system design. From 1989 to 1992, he waswith the IBM T.J. Watson Research Center, Yorktown
Heights, NY, as a Research Staff Member, working on indoor
radio communication system design. Since 1992, he has been
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

943

Coexistence of Korea’s DVB-T2 and Japan’s ITS
using 700MHz frequency band
Ho-Kyung Son, Young-Jun Jung
Radio Technology Research Department, Electronic Telecommunication Research Institute, Daejon, Korea
hgson@etri.re.kr, yjchong@etri.re.kr
Abstract—In this paper, we analyze the effects of interference
between Korea’s ultra-high definition TV broadcasting system
and Japan’s intelligent transport system using the 700 MHz
frequency band when considering a practical deployment ofboth systems. We performed Minimum Coupling Loss (MCL)
method to evaluate how much interference from the Korean
UHDTV system is imposed on the Japan ITS system. We also
employ the Advanced Propagation Model (APM) and ITU-R P.
452-15 model to calculate the propagation loss occurring inducts. Our study can be applied to the deployment planning foreach system with an interference impact acceptable to bothparties.
Keyword—DVB-T2, ITS, Interference, Ducting

I. INTRODUCTIONM

any countries converted their analog TV systems into
digital TV (DTV). The South Korean government also
allocated CH 14 through CH 51 (470-698 MHz) for DTV use[1] and recently determined to allocate additionally 30MHz(698-710 MHz and 753-771 MHz) for ultra-high definition
TV (UHDTV) use [2]. Several broadcasters did test
broadcasting with Digital Video Broadcasting-Terrestrial
version 2 (DVB-T2) standard. But recently the Korean
government picked the Advanced Television Systems
Committee (ATSC) 3.0 standard for ultra-high-definition(UHD) television broadcasting set to launch next year [3].
In Japan, digital terrestrial TV broad-casting service isavailable at 460-710 MHz on the UHF band using the ISDB-T
standard and a 9 MHz channel width in the 755.5-764.5 MHz
radio frequency band will be used for Intelligent Transport
System (ITS). It is the realization of safe driving support
systems to reduce the number of traffic accidents. The 700
MHz frequency band is known for its good propagation
characteristics in non-line-of-sight conditions such as behind———————————————————————
Manuscript received October 20, 2016. This work was supported by the
ICT R&D program of MSIP/IITP [B0101-15-222, Development of coretechnologies to improve spectral efficiency for mobile big-bang] and afollow-up of the invited journal to the conference paper of the 18th
International Conference on Advanced Communication Technology(ICACT2016).
Ho-Kyung Son is with radio technology research department, Electronics
and Telecommunications Research Institute, Daejeon, Republic of Korea(Corresponding author, phone: +82-42-860-5981, fax:+82-42-860-5199,
email:hgson@etri.re.kr)
Young-Jun Jong is with radio technology research department,
Electronics and Telecommunications Research Institute, Daejeon, Republicof Korea (email:hjhong@etri.re.kr)

buildings or large vehicles [4]. The 755.5-764.5 MHz
frequency band partially overlaps with Korean UHDTV
frequency band.
Although Korean UHDTV system and Japan ITS system
are located with a sufficient separate distance, the potential
interference would be produced. A few years ago, radio
interference began to occur in the Trunked Radio Service(TRS) frequency band in the southern coastal area of Korea[5] and similar interference has also been observed in themobile communication frequency band [6]. Monitoring such
radio interference found that its main source was the ductingof radio signals produced in the coast of Japan.
The ducting of an RF signal is caused by an atmosphericanomaly known as temperature inversion. A layer of ice coldor very hot air traps the RF signal and guides it to a farther
distance than expected through an anomalously formed duct.
These ducts can extend to hundreds or even thousands ofmiles. Once trapped, the ducted signal may cause interference
to distant wireless systems [7]. As the height and duration of
the duct layer are not constant, it is difficult to predict theeffects of this type of interference. To allow better radio
communication services, we need to be able to predict theeffects of interference and study a method of coexistence
between the two countries.
In this paper, we analyze the effects of interference
between UHDTV services in Korea and ITS service in Japan
at the 700 MHz frequency band. We focus particularly on aninterference scenario of Korea’s UHDTV to Japan’s ITS, as
this type of interference is more serious than the otherinterference scenario. Korean UHDTV is assumed in
DVB-T2 system and the Minimum Coupling Loss (MCL)
method is used for the interference analysis between fixedstations. We also employ the Advanced Propagation Model(APM) and ITU-R P. 452-15 model to calculate thepropagation loss occurring in ducts. To protect againstinterference between Korean and Japanese radio signals, therequired additional attenuation loss according to the
UHDTV’s antenna tilt degree is also calculated.
This paper is organized as follows. In section II,
interference scenarios and the interference analysis method
are described. In addition, the propagation model used arepresented. The simulation parameters and results arepresented in section III. Finally, some concluding remarks
regarding our proposal are provided in section IV.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

II. INTERFERENCE SCENARIO AND METHODOLOGY
A. Interference scenario
Fig. 1 shows a scenario for interference analysis from
Korea’s DVB-T2 system to Japan’s ITS system. We assumed
that Korea’s DVB-T2 site transmits signals using the 759-765
MHz frequency band and Japan ITS system has a 9 MHz
channel bandwidth using 755.5-764.5 MHz frequency band.
The distance between Korea and Japan ranges from about 240
to 300 km, we assumed the separate distance between two
systems is 240 km.
Japan ITS systems are supposed to consist of two types ofsystems: vehicle-to-vehicle communication systems that
support safe driving by inter-vehicular radio communicationsat intersections with poor visibility, and roadside-to-vehiclecommunication systems that support safe driving by sending
information from roadside units of traffic infrastructure tovehicles through radio communications [4]. We considered
only roadside-to-vehicle communication systems that vehicletransmit the signal to roadside units. We evaluated whether
the received interference power at the antenna of Japan’s ITS
receiver satisfies the maximum allowable interference power.

944

where Pt is the transmit power of the interfering system in
the reference bandwidth in dBm, Gt is the gain of theinterferer antenna in the direction of the receiver in dBi, G r is
the gain of the victim receiver antenna in the direction of theinterferer in dBi, PL(d ) is a basic transmission loss forseparation distance d between the interferer and receiver indB.
The receiver thermal noise power is given by

PN  10 log10 (kTB)  NF

(3)

where k is Boltzmann’s constant (W/K/Hz), T is theambient temperature (K), B is the channel bandwidth (Hz),and NF is the receiver noise figure (dB). For
k  1.3804  10 23 , T=290 and NF=6dB, we have
PN  108 dBm / MHz .
In an interference-limited environment, equation (1) can beapproximated as follow

PN  PI  PI

(4)

The tolerable interference signal power can be determinedusing the following equation

PI ,t arg et  PS  SINRmin(5)

The required additional attenuation loss ( La ), in dB , ofthe interfering system on the victim system can be determinedusing the following equation [8]:La  PI  PI ,t arg et(6)

PI ,T arg et is the tolerable, or target, interference power at
the receiver.

Fig. 1. Interference scenario between Korea’s DVB-T2 and Japan’s ITS

B. Methodology for an interference analysis
The possibility of frequency spectrum sharing depends onwhether the received signal-to-interference plus noise ratio at
the antenna of victim receiver satisfies the minimum
signal-to-interference plus noise needed to process (vice justdetect) a signal.

PS  ( PN  PI )  SINRmin(1)

where PS is the received wanted signal power fromvehicle’s transmitting signal, PN is the thermal noise powerof the receiver and PI is the received interfered signal powerfrom Korean DVB-T2 transmitting signal.
The received interfered signal power from Korean
DVB-T2 transmitting signal is expressed by [8]PI  Pt  Gt  Gr  PL(d )(2)

C. Propagation model for path loss calculation
The basic transmission loss is the most important factor to
predict the interference level and determine the additional
required attenuation loss. For a calculation of this basic
transmission loss occurring in a duct, we used ITU-R
P.452-15 [9] and hybrid propagation model called an
Advanced Propagation Model (APM).
ITU-R P.452-15 is a prediction method for the evaluationof interference between stations on the surface of the Earth at
frequencies from about 0.1 GHz to 50 GHz, accounting forboth clear-air and hydrometeor scattering interference
mechanisms. The models within Recommendation ITU-R
P.452 are designed to calculate propagation losses notexceeded for time percentages over the range 0.001
 p  50%. This assumption does not imply the maximum
loss will be at p = 50%. The method includes acomplementary set of propagation models which ensure thatthe predictions embrace all the significant interference
propagation mechanisms that can arise. Methods foranalyzing the radio-meteorological and topographicalfeatures of the path are provided so that predictions can be

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

prepared for any practical interference path falling within thescope of the procedure up to a distance limit of 10000 km [9].
APM is much faster than split-step parabolic equation (PE)method, yet it requires far less memory and can be used inwider applications. APM considers four regions shown in Fig.
2. At ranges less than 2.5km and for all elevation angles above
5, APM uses a flat earth (FE) model region. For beyond the
FE region, where the grazing angles of the reflected rays fromthe transmitter are above a small limiting value, the ray optics(RO) model is used. The PE model is used for ranges beyond
the RO region, but only for altitudes below a maximum PE
altitude as determined by the maximum allowed 1024-point
Fast-Fourier transform (FFT). For ranges beyond the RO
region and heights above the PE region, an extended optics(XO) method is allowed, which can operate at the maximum
PE altitude [10].

Fig. 2. Advanced Propagation Model(APM)

945

TABLE IIITS SYSTEM PARAMETERS FOR SIMULATION

Parameter

Value

Center frequency

760MHzChannel bandwidth

9MHz

Vehicles radiated power

20dBm

Road-side antenna height

7m

Road-side antenna gain

13dBi

Road-side noise figure

6dB

Minimum SNR

13dB

Vehicle antenna height

3m

Vehicle antenna gain

0dBi

DVB-T2’s channel bandwidth to be 6 MHz and thetransmitting power to be 5 kW. Hwang-Ryeong Mountain waschosen as Korean DVB-T2 site, which located near Japan.
The vertical pattern of the DVB-T2 antenna [11] was shownin Fig. 3. We consider the channel bandwidth of ITS system tobe 9 MHz and the vehicles transmitting power to be 200 mW.
We also assumed 7m as the Road-side antenna height and
13dB minimum SNR as the protection ratio. Also we consider
the Road-side noise figure is 6dB. A free space propagation
model is used to calculate the path loss between ITS’s vehicle
and ITS’s roadside.
An ITU-R P.452-15 propagation model and APM are used
to calculate the path loss between Korea’s DVB-T2 andJapan’s ITS and Table III shows the path loss using theP.452-15 model. Table IV shows the modified refractivity
index of Pohang provided by the WMO station. The values
represent a surface-based duct and elevated ducts in June.

III. SIMULATION RESULTS
A. Simulation parameter
We considered a scenario of the interference between
Korea’s DVB-T2 and Japan’s ITS, where DVB-T2 and ITS
are deployed at the shores of Korea and Japan. We simulated
only the interference effect from Korea’s DVB-T2 station to
Japan’s ITS road station. Table I and II present theoperational system parameters for the DVB-T2 and ITS
systems used for simulation, respectively. We consider the
TABLE I
DVB-T2 SYSTEM PARAMETERS FOR SIMULATION

Parameter

Fig. 3. DVB-T2’s antenna vertical pattern
TABLE III

Value

Center frequency

762MHzChannel bandwidth

6MHz

Transmitting power

67dBm

Antenna gain

10dBi

Antenna height

450m

Distance

240km

Modified refractivity

WMO data

tilting

0~9

Location35 15 80
129 08 22

Antenna pattern horizontal
verticalomni
ITU-R F. 1336

PATH LOSS USING ITU-R P.452-15

Time rate(%)
1
10
50

Path loss(dB)
144.2dB
172.8dB
179.6dB

TABLE IV
MODIFIED REFRACTIVITY-WMO DATAHeight(m)
0
16
47
72
1933
2055
3054

Modified refractivity
337
339
343
334
559
551
661

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

946

vehicle unit of ITS. The target signal-to-interference plus
noise ratio of 13dB may be satisfied depending on theseparate distance. For 10% time rate and 50% time rate of
ITU-R P.452-15 model, it satisfied the target
signal-to-interference plus noise ratio in all segments.
However, for a 1% time rate of ITU-R P. 452-15 model and
APM, the target signal-to-interference plus noise ratio
appears to be satisfied at separate distance below 150 m.
The additional attenuation loss required for the receiving
antenna height of ITS’s roadside is shown in Fig. 7.

Fig. 4. Path loss between Korean DVB-T2 and Japan ITS using APM

Fig. 6. Received signal-to- interference plus noise ratio according to the time
rate at the roadside of ITS

Fig. 5. Path loss versus receiving antenna height

B. Simulation result
The propagation loss using APM for variation of the
distance between Korea’s DVB-T2 and Japan’s ITS is shownin Fig.4. The propagation loss appears to increase as the
distance is increased and the propagation loss by ducting may
be smaller than the free space loss.
Fig. 5 shows the propagation loss for variation of receiving
antenna height when the distance between two countries is
240 km. The propagation loss appears to have an abnormal
value near the receiving antenna height of 50m and the
propagation loss below the height of 75m is smaller than thefree space loss 137.5dB. This result indicates that the
propagation loss depends on the antenna height and the
propagation loss also appears to vary according to the ducting
layer height.
Fig. 6 shows the received wanted signal strength andinterfering signal strength ratio at the victim receiver,road-side antenna of ITS, for variation of the time rate andpropagation model. Interfering signal strength is calculatedusing equation (2) and the parameters in Table I and Table II.
After calculating the interference power level and the wanted
signal strength at the victim receiver, we can determine
whether the interference occurs. The received
signal-to-interference plus noise ratio appears to be
dependent on the separate distance between roadside unit andFig. 7. Additional attenuation loss required for receiving antenna heights

Fig. 8. Additional attenuation loss required for tilt degree of transmitting
antenna

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

These are calculated using equation (6) and the parameterslisted in Table I and Table II. When the heights of thereceiving antenna of ITS’s roadside is 5 m, an additional
attenuation loss of above 8 dB is required to satisfy the target
interference level with ITS’s cell radius of 500m. As theseparate distance between roadside and vehicle of ITS, cellradius, is extended, a required additional attenuation loss tosatisfy the target interference level is increased. In addition,
when the heights of the receiving antenna of ITS’s roadside is
7 m, an additional attenuation loss of 11 dB is required. From
the result, we confirm that the target interference level may besatisfied depending on the antenna height and ITS’s cellradius.
Fig. 8 shows the additional attenuation loss required forvariation of the tilting angle of a transmitting Korean
DVB-T2 site antenna, which was simulated based on thevertical pattern of the DVB-T2 antenna in [9] and based on
the APM. Also the required additional attenuation loss iscompared when the tilting angle of the DVB-T2 site antenna
is 0, 3, 6 and 9. This result indicates that Korean DVB-T2
site system produces a higher interference on Japan’s ITS
system when the tilting angle of the transmitting antenna is 3
than when it is 9.
The additional attenuation loss required, as shown in Fig. 7
and Fig. 8, can be used as a guideline for allowing the
deployed Japan ITS system and Korean DVB-T2 to avoid anunacceptable amount of interference between systems.

IV. CONCLUSION
In this paper, we analyses the effect of interference
between UHDTV services in Korea and ITS service in Japan
at the 700 MHz frequency band when encountering a ducting
phenomenon. The MCL method is used for an interference
analysis, focusing on an interference scenario of Korea’s
UHDTV to Japan’s ITS. The ITU-R P.452-15 model and
APM were used to calculate the propagation loss in the ducts,
and the modified refractivity indexes of a surface-based duct
and an elevated duct as provided by a WMO station wereapplied. The propagation loss by ducting may be smaller than
the free space loss.
The received signal-to-interference plus noise ratio and therequired additional attenuation loss at Japan’s ITS roadsideoccurring from Korea’s DVB-T2 were simulated for antennaheight and antenna’s tilting angles. We confirmed that thestrength of the received interfering signal depends on theantenna height, and that the target interference level may besatisfied by adjusting the antenna height. Also we confirmedthat the received interfering signal strength depends on thetransmitting antenna’s tilting angles and that the target
interference level may be satisfied by adjusting the tilting
angle of antenna and Japan’s ITS cell radius.
This paper can be used to evaluate the interference effects
and find a spectrum-sharing method between Korean
DVB-T2 and Japan ITS. It can also be useful for deployment
planning by each system, resulting in an interference impactthat is acceptable to both parties.

947

MSIP/IITP,
Republicof
Korea.
[B0101-15-222,
Development of core technologies to improve spectral
efficiency for mobile big-bang].
REFERENCES
[1]
[2]
[3]

Frequency policy in Korea, KICS, 5 June 2009http://www.ddaily.co.kr/news/article.html?no=132874
http://english.yonhapnews.co.kr/business/2016/07/26/0504000000A
EN20160726002700320.html[4] ITU-R 5A/198-E, “WORKING DOCUMENT TOWARDS APRELIMINARY DRAFT REVISION OF REPORT ITU-R M.2228:
Advanced intelligent transport systems (ITS) radiocommunications”20
Nov. 2012[5] H. S. Lee, “An Analysis of Radio Interference in 800MHz Bandbetween Korea and Japan,” The Journal of Korea Information andCommunication Society, 8(1998), pp. 1952-1962.
[6] N. H. Jeong, “The Analysis of Radio Interference between Korea andChina/Japan using Split-step DMFT Algorithm,” The Journal of
Korea Institute of Electromagnetic Engineering and Science, 2(2002),
pp. 196-207[7] P. Gerstoft, D. F. Gingras, L. T. Rogars, and W. S. Hodgkiss,“Estimation of Radio Refractivity Structure Using Matched-Field
Array Processing,” IEEE Trans. Antennas Propagation, 48(2000).
[8] ERC Report 101, “A comparison of the minimum coupling loss
method, enhanced minimum coupling loss method, and theMonte-Carlo simulation,” May, 1999.
[9] ITU-R Rec. P.452-15, “Prediction procedure for the evaluation ofinterference between stations on the surface of the Earth at frequencies
above about 0.1GHz,”2013[10] H. V. Hitney, “Hybrid Ray Optics and Parabolic Equation Methods for
Radar Propagation Modeling,” IEE International Conference Radar
92, 12-13, Oct. 1992.
[11] ITU-R Rec. F.1336-4, “Reference radiation patterns of omnidirectional,
sectoral and other antennas for the fixed and mobile service for use insharing studies in the frequency range from 400 MHz to about 70
GHz,”2014
Ho-Kyung Son received the B.S and M.S degrees inElectronic Engineering from Kyungpook National
University, Daegu, Korea, in 1997 and 1999, and thePh.D degree in the same university, Korea in 2013.
From March 1999 to March 2000, she was a
researcher of LG Information and Communication.
Since May 2000, she has been with the Electronics
and Telecommunications Research Institute (ETRI),
Daejeon, Korea, where she is a principal researcher ofspectrum engineering research section of the Radio Technology Research
Department. Her research interests include radio resource management,interference analysis between wireless communications, and radio
propagation.
Young-Jun Chung received the B.S. degree from the
Jeju University, Jeju Island, Korea, in 1992, and the
M.S. degree in electronics engineering from Sogang
University, Seoul, Korea in 1994, and Ph.D degree inElectronic Engineering from Chungnam National
University, Daejeon, Korea in 2005.
Since 1994, he has been with ETRI, Daejeon, Korea,
where he is a leader of spectrum engineering researchand section principle member of the research staff ofthe Radio Technology Department. His research interests include RF circuit,RF systems, and spectrum engineering.

ACKNOWLEDGMENT
This work was supported by the ICT R&D program of
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

948

Analytical Description of Chromatic Dispersion
Effect on Signal Propagation in the Time
DomainMikhail Meltenisov*, Aleksandr Matukhin**Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Bolshevikov Ave. 22, Saint-Petersburg, Russia
meltenisov@gmail.com, matukhin@list.ru
Abstract —The investigation of chromatic dispersion effect onpulse propagation is of interest in high-speed opticaltransmission systems. But the chromatic dispersion effect hasn't
an acceptable analytical description in the time domain. The
analytical model of the dispersion effect in the time domainusing a quadratic function approximation of nonlinear part ofthe propagation constant and the Fresnel integrals is proposedin this paper. It is shown that the obtained model is universal
and it has a tunable accuracy. A simple method of estimating thememory of an optical channel is proposed. The analytical modelof signal propagation in an optical channel by means ofsequential generation of pairs of echo-signals is described in thearticle.
Keyword—Analytical model, approximation, dispersion,Frensel integrals, propagation constant, time domain,echo-signal, memory of channel

II. DISPERSION THEORYIn the Dispersion Theory [1], pulse propagation can be
written in the form

A
  jβ  f  Az(2)

where A  z, f  is a signal spectrum at a distance z, andβ  f  is the propagation constant which has a frequency

I. INTRODUCTIONA

T the present time, high-speed optical transmission
systems are in an active development and theinvestigation of the chromatic dispersion effect is one of mostinterest of issues. The propagation of pulses through an
optical fiber, which is a dispersive medium, is well exploredin [1]–[7].

A
A jβ2  2 A β3  3 A
 β1


0
z
t
2 t 2 6 t 3

Besides, a direct solution may be difficult, whereas ananalytical equation is impossible to get using the fast Fouriertransform. But the analytical equation may be of use in somecases, e.g. signal processing in the time domain.
In this paper, a simple and universal analytical dispersion
model in the time domain will be found.

dependenceβ f  

2πf
n f c

(3)Here, c is the velocity of light in vacuum and n is a
refractive index which defined by the Sellmeier equation [8].
All nonlinear effects and the attenuation have been
(1) excluded in equation (2).
The solution of the equation iswhere A  z, t  is the slowly varying pulse envelope andA  z, f   A  0, f  e jβ f  z(4)

β m are a parameters described in [1] .
The differential equation (1) should be solved if a pulse
form in the time domain is required to find. And, this equation
should be solved individually for of all kinds of pulses.
———————————————————————
Manuscript received October 28, 2016. This work is a follow-up of theinvited journal to the conference paper of the 18th International Conference
on Advanced Communication Technology (ICACT2016).
M. A. Meltenisov is with the Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Saint Petersburg, Russia (corresponding
author, e-mail: meltenisov@gmail.com).
A. Yu. Matukhin is with the Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Saint Petersburg, Russia (e-mail:matukhin@list.ru).

It completely described the effect of the chromatic
dispersion on a signal.
Therefore, the transfer function of the dispersive medium isHfe jβ f  z(5)III. DISPERSION IN THE TIME DOMAINAs seen from Figure 1, the propagation constant has asubstantial linear part. Therefore, we can represent it as

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

β  f   βln  f   βnl  f 

(6)

949

Actually, βnl is not a quadratic function. This function has
some cubic part. Accordingly, there is a deviation, as shown

where β ln is a linear part and βnl is a nonlinear part of the
propagation constant.
Since the entire optical spectrum is not of interest an
operating band of signal will be considered further, i.e.

in Fig. 3. But βnl can not be approximated with a cubic
function because it will obstruct further calculations.

f   f l ; f h  where, f h is the higher frequency, and f l is

the lower frequency.
A. Linear Part of the Propagation Constant
The influence of β ln causes signal delay in the time
domain. β ln can be represented in form

βln  f   kf  p(7)

And parameters k and p are

β  fh   β  fl k 
fh  fl p  β  fl  fh  β  fh  flfh  fl(8)

Fig. 1. Frequency dependence of the propagation constant.

If the nonlinear part is omitted, a pulse envelope in the time
domain will be obtained in the formkz A  z, t   A  0, t   e  jpz
2π 


(9)

B. Nonlinear Part of the Propagation Constant
The nonlinear part can be obtained as

βnl  f   β  f   βln  f (10)

As seen from Fig. 2, it is look like quadratic function.
Therefore, it can be represented asβnl  f   af 2  bf  cFig. 2. A nonlinear part of the propagation constant in the 100 GHz
operating band.

(11)

where a, b and c are
1a  4 β  f c   βln  f c  2
 fh  fl 
 fh  fl b  4 β  f c   βln  f c  2
 fh  fl 


fh fl
c  4 β  f c   βln  f c  2

 fh  fl Here, f c is a central frequency of the band.

(12)

Fig. 3. An absolute error of the quadratic function approximation of
nonlinear part of the propagation constant.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

To minimize the deviation, the entire operating band can bedivided into two. And then, the deviation function can beapproximated with a quadratic function in each of sub-bands

 m f 2  n1 f  k1 , f   f l ; f c βnl  f    1 2

m2 f  n2 f  k2 , f   f c ; f h 

(13)

cn 

e

950

  bzL  πn 2 j cz 
 4 azL2

2 L  az 
bzL  πn bzL  πn  C  az f h   C  az f l 
2 azL 
2 azL 

 

(19)

bzL  πn bzL  πn   jS  az f h   jS  az f l 

2

azL
2 azL  




and

Here, C(x) and S(x) are the Fresnel integrals.
For second approximation of nonlinear part of the(14) propagation constant from (15) and for next approximations,
βnl  f   βnl  f   βnl  f (19) should be calculated in each of sub-bands (with constantParameters m1 , n1 , k1 , m2 , and k2 can be found with L), and the results should be summarized then.
A signal spectrum at a distance z is(12), where f h , f l and f c is a higher, lower and the centralA z, jω  A  0, jω H  jωfrequency of the each of the ranges, respectively.
A new approximation is2  a  m1  f   b  n1  f   c  k1  , f   f l ; f c βnl  f   2 a  m2  f   b  n2  f   c  k2  , f   f c ; f h or, with (17) and (7) is(15)
NDeviations in each of the ranges will be similar to thedeviation of the first approximation. Therefore, a deviation in
the entire band can be represented by four quadratic functions
in four intervals.
It was found that improving of the approximation can becontinued in a similar way until the error value becomes
sufficient.
C. Representation of Chromatic Dispersion in the Time
DomainThe transfer function of the dispersive medium can be
represented as a product of two partsHf e jβln  f  ze jβnl  f  z(16)First part is a linear part which described in the time
domain by (9). Second part is a nonlinear part which causes apulse distortion. Let us expand it into a Fourier series to
describe it in the time domainH  f   e jβln  f  z   cn e

j

πfn
L

n 

where

1 h  jβnl  f  z  j πLfneedf
2 L flA  z, jω    cn e  jpz A  0, jω  e

 kz n  jω  
 2π 2 L 

To describe a signal in the time domain the inverse Fouriertransformation [11] should be used.
Finally, in the time domainNn kz A  z, t    cn e  jpz A  0, t  
2 L 2π n  N(22)

where A is a pulse envelope at a distance z and at a time
moment t. L is a half of a signal bandwidth. cn are given by(19), and p and k are given by (8).
Equation (22) describes any signal at a distance z which is
distorted by the chromatic dispersion.

IV. PROPAGATION PROCESS IN TERMS OF THE
ECHO-SIGNALS MODEL

Model of Chromatic Dispersion Effect (22) allows
representing a signal propagation process in terms of(17) echo-signals.
The echo-signals model [11] describes a signal at a
distance z as the sum of initial signal and N pairs of lagged andanticipatory echo-signals.
(18) A z , t  a A 0, t   0  N

 a A  0, t  nτ 
n 1

n

(23)

N

  a n A  0, t  nτ and L is a half of the entire band.

Because βnl  f  is a quadratic function cn can be
expressed in terms of the Fresnel integrals [9](21)n  N

f

cn 

(20)

n 1

Here,

A  0, t  is initial signal, A  0, t  nτ  areanticipatoryecho-signals,

A  0, t  nτ 

arelaggedCopyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016A  2dz, t   a0 2  2a1a1  A  0, t 

echo-signals and are some coefficients.
Expression (22) can be represented in the same form

 2a0a1 A  0, t  τ   2a1a0 A  0, t  τ kz A  z , t   c0e  jpz A  0, t 

2π 


and t  jpzcorresponds to a n ,(27)

 a12 A  0, t  2τ   a12 A  0, t  2τ Nn kz   cn e  jpz A  0, t 
2 L 2π n 1Nn kz   c n e  jpz A  0, t 
2 L 2π n 1Here, cn e

951

(24)

Considering that a1 = a 1 , signal at the distance z can be
described by expression

K
A  Ndz , t     c0 k a0 N 2 k a12 k  A  0, t  k 0NK
N  2 k n
    cnk a0  a12 k  n  A  0, t  n  (28)n 1  k 01
corresponds to τ
2L

N
K

N  2 k n
    cnk a0  a12 k  n  A  0, t  n 
n 1  k 0

kz
corresponds to t in (23).
2π

A. Memory of Optical Channel
The memory of channel allows estimating the number ofpulses, which influence one another.
Interval between centers of two echo-signals is τ 

1.
2L

Also, τ is interval between centers of two pulses. Therefore,
the number of echo-signals equals the number of pulses,
which influence one another. The number of echo-signals isdefined by coefficients cn , which can be called the impulse
response of an optical channel if nonlinear effects are
negligible. Therefore, the memory of an optical channel canbe found by estimating values of cn .
Example of the impulse response of an optical channel isshown in Fig. 4. The coefficients until certain n do not have adecreasing character and then begin decreasing rapidly.
Plotting of similar graphs allows simple finding thememory of an optical channel.

N  n 2  is thenumber of summand at nth echo-signal and cnk are
Here, N is number of the sections, K 

coefficients at the summands which calculated as follows

cnk 

N!
k ! k  n ! N  2k  n !

(29)

K
N 2 k 2 k 
a1  corresponds c0 and  c0 k a0
 k 0KN 2 k 2 k 
a1  corresponds c1  c1 from (24).
  c0 k a0
 k 0
Besides,

As can be seen from the above, the propagation process inan optical channel can be represented as sequential generationof pairs of echo-signals after each section with length dz. It
B. Propagation Process
could be useful for investigation of nonlinear propagation in
Let us consider signal at distance dz at which there are twoan optical channel because the model (28) is not requires
echo-signals only
performing the Fourier transform in distinction from thesplit-step Fourier method [1]. All of operations are performed(25) in time domain.
A  dz, t   a0 A  0, t   a1 A  0, t  τ   a1 A  0, t  τ 
The entire line with length z can be divided to N sections
with lengths dz as shown in Fig. 5.
Each section transforms the signal the same way as in (25):

the input is A n  1 dz, t , the output isA  ndz, t   a0 A  n  1 dz , t  a1 A  n  1 dz , t  τ (26)

 a1 A  n  1 dz , t  τ 
One section can be represented as FIR filter as shown in
Fig. 6. Each section generates a new pair of echo-signals:
Fig. 4. The impulse response of an optical channel with 100 GHz operating
band at the distance 100 km.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

952

Fig. 5. The line with length z which is divided to N sections with lengths dz.

Fig. 8. Sinc pulse with width 30 ps at the distance 155 km. The constantdelay, which equaled 0.76 ms, has been compensated.
Fig. 6. Section dz as FIR filter.

V. EXPERIMENTAL RESULTS
An accuracy of the obtained analytical model is defined by
the number of the Fourier series coefficients N and by the
number of the approximations as in (15). The first parameteris substantial in any conditions. The second is substantial onlyat considerable distances ≥ 1000 km and at signal bandwidths≥ 100 GHz. Dependencies of the parameter N are

N  z   z  km F  GHz  N  F   
100

(30)
2

(31)

Some types of pulses calculated with the model are shownin Fig. 7, 8 and 9.

Fig. 9. Square pulse with width 30 ps at the distance 50 km. The constantdelay, which equaled 0.24 ms, has been compensated.

VI. CONCLUSION

Fig. 7. Gaussian pulse with width 30 ps at the distance 155 km. The constantdelay, which equaled 0.76 ms, has been compensated.

In this paper, an analytical model of dispersion effect in thetime domain has been described. This model allows describe
the chromatic dispersion effect on any signal, which
propagates through optical fiber. The model has a tunableaccuracy, so it is applicable in different areas such as signal
propagation
modeling,
algorithms
ofdispersion
compensation, etc.
It has been shown that the propagation process in an opticalchannel can be represented as sequential generation of pairsof echo-signals. The model is an analytical and described intime domain. The applications of this model are an opticalsignal propagation modeling which includes the nonlinear
propagation and algorithms of dispersion compensation.
Furthermore, the method of estimating the memory of an
optical channel is proposed in this article. It requires only theestimation of values of coefficients at each echo-signal.
A problem of the proposed models is a high computational
complexity in the case of wide signal bandwidths andconsiderable distances. However, the main task of this modelis the using it in an analytical equations. In this case, thecomputational complexity is not substantial.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

953

REFERENCES[1]

G. P. Agrawal, Nonlinear Fiber Optics (5th ed.), New York:
Academic, 2013.
[2] S. J. Orfanidis. (2016, August 1). Electromagnetic Waves andAntennas [Online]. Available:
http://www.ece.rutgers.edu/~orfanidi/ewa[3] E. Iannone, F. Matera, A. Mecozzi, and M. Settembre, Nonlinear
Optical Communication Networks, New York: Wiley, 1998.
[4] S. Elahmadi, M. D. Srinath, D. Rajan, and R. Haberman, Capacity andModeling of Nonlinear Fiber Optic Communications as aFrequency-Selective Fading Channel, in Proc. ICNC'12, 2012.
[5] K. N. Modi, Modeling, Detection and Signal Design for Multichannel
Fiber Optic Communications, Univ. of Virginia, 2009.
[6] R. Gaudino, and E. Viterbo, “Pulse shape optimization indispersion-limited direct detection optical fiber links”, IEEE
Communications Lett., vol.7, pp. 552–554, Nov. 2003.
[7] K. Kashiwagi, H. Ishizu, and T. Kurokawa, “Fiber Transmission
Characteristics of Parabolic Pulses Generated by Optical Pulse
Synthesizer,” Japanese Journal of Applied Physics, vol. 50, Sep. 2011.
[8] M. J. Adams, An Introduction to Optical Waveguides, New York:
Wiley, 1981.
[9] Y. Luke, Mathematical Functions and Their Approximations, New
York: Academic Press, 1975.
[10] N. Morrison, Introduction to Fourier Analysis, New York: Wiley,
1994.
[11] N. N. Baeva, I. K. Bobrovskaya, V. A. Breskin., Yu. A. Yakub,
Fundamentals of multi-channel communication. Textbook for HighSchools, Moskow: Svyaz, 1975, (in Russian)Mikhail A. Meltenisov was born in Russia in
1989.
He
graduatedtheBonch-Bruevich
Saint-Petersburg
State
UniversityofTelecommunications, Russia as the engineer at
specialty "multichannel transmission systems" and as
the master at area "infocommunication technologies
and telecommunication systems" in 2011 and 2013,
respectively. In 2013, he took a postgraduate courseonspecialty"systems,
networks
andtelecommunications devices" in the same university.
He is working in St.Petersburg University of Telecommunication as
Research Engineer since 2015. His studies focused on adaptive signal
processing in fiber optic transmission systems.
Dr. Aleksandr Matukhin was born in 1973 in
USSR.
He
graduated
Bonch-Bruevich
Saint-Petersburg
State
UniversityofTelecommunications, Russia as the engineer at
specialty "multichannel transmission systems" in
1996. He received Ph.D from St.Petersburg
University of Telecommunication in 2004.
He worked in St.Petersburg University of
Telecommunication as assistant, senior lecturer,
associate professor and chief Department ofMultichannel Transmission Systems since 1997. Now he is working in
St.Petersburg University of Telecommunication as Associate Professor
Department of Communications Networks. His studies focused on adaptive
signal processing in multichannel transmission systems.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

954

Dynamic Analysis of Rotor Blade System
Liu YiSchool of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
bjliuyi_2015@163.com

Abstract—To make the rotation of the rotor blade moreaccurate, the elastic deformation in the system is considered inthis paper. Firstly, the number and direction of the generalized
coordinates are determined by the freedom degree of the blade
and the finite element method and Lagrange equation are used
to build the dynamical equation of the rotor blade system. Then,
we obtain the position vector, the mass matrix, the derivative ofMass Matrix on time and other coefficient matrix according to
the principle of flexible multibody dynamics. Finally, theviolation correction method is used to get the numerical solutionof the dynamical equation in the simulation. Additionally, theangular displacement of the hub around the main axis, theangular displacement of the blade end and elastic deformation in
the y-direction and z-direction are analyzed to prove thecorrectness of the model.

Keyword—rotor blade, elastic deformation, finite element,
flexible multibody dynamics.

I. INTRODUCTIONT

HE study of flexible multibody dynamics began in thelate 1950s during which the US launched the first
artificial satellite. In the past 20 years, due to its very close
relationship with the engineering field, such as aerospace,aviation, machinery, vehicles and robots, even with the sports,
flexible multibody dynamics have caused great concern andbecome an active study field of theoretical and appliedmechanics. Flexible multibody dynamics researches the
dynamics behavior of the systems consisted of deformableobjects and rigid body during wide-range spatial movement.
Flexible multibody dynamics focus on ―flexible‖, and study
the interaction between the objects’ deformation and overallrigidity movement. The character that deformation movement
and rigidity movement occur and couple simultaneously is thecore feature of flexible multibody dynamics.
A novel mathematical formulation capable of treating the
problems of maneuvering and control of flexible multibody
structures is developed [1]. The authors focused on an
approach to the study of the dynamics and control of large
flexible space structures which comprised of subassemblies, asubject of considerable contemporary interest [2]. A flexible
multibody dynamics formulation to analyze the vibration of
hard disk drives is presented [3-4]. The inverse-dynamic

model of mobile multibody systems articulated with joints
and wheels is built and an easily implementable algorithm
which is based on Newton–Euler recursive dynamics is
proposed [5]. This authors presented several iterativealgorithms regarding the dynamic analysis of multibody
systems [6].
However, there is few literatures investigating the dynamicof the rotor blade using the flexible multibody dynamics.
Moreover, the research on helicopter fall behind that on
Fixed-wing aircraft, the reason is mainly that the awareness ofthe helicopter rotor aerodynamic is not sufficient. Based on
this, the dynamical equation of the rotor blade system is built
using the finite element method and the Lagrange equation.
Additionally, the mass matrix and other coefficient matrix are
obtained. After that, the simulation is used to verify thecorrectness of the dynamical model.
II. SYSTEM MODEL
Helicopter rotor is composed of hub and several blades.
The hub mounted on the rotor shaft, while the blade which
likes the slender wing connects to the hub. The hub and blade
rotated together with the helicopter engine rotation, and theinteraction between the blade and the surrounding air cancreate the thrust along the rotor shaft. Additionally, the blade
contains flap, lag and torsion, and all these motions coupled
together to formed a complex structure-dynamics problem.
The helicopter rotor not only rotates around the engine
shaft, but also has Elastic deformations due to its flexible.
According to the motion characteristics of the rotor blade inspace, we can determine the number of generalized
coordinates, and build the finite element model of the rotor
blade in Fig. 1.
x3
p

y2
Y(y1)

y3



x2

O2(o3)

R0



O(o1)



x1
z2(z3)
XZ
z1———————————————————————
Manuscript received October 19, 2016. This work is a follow-up of theinvited journal to the accepted conference paper of the 18th International
Conference on Advanced Communication Technology (ICACT2016)
Liu Yi is with School of Aerospace Engineering, Beijing Institute ofTechnology,
Beijing,
China(corresponding
author’sphone:+86-15011440975; e-mail: bjliuyi_2015@163.com).

Fig. 1. The finite element model of the rotor blade.

In Fig. 1, OXYZ is the inertial coordinate frame, whileo1x1y1z1, o2x2y2z2 and o3x3y3z3 are the moving coordinates.
The o1x1y1z1 frame rotates around OY axis with vector R0,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016

and the rotation angle is β; the o2x2y2z2 frame rotates aroundo1z1 axis with vector R0 , and the rotation angle is β; theo3x3y3z3 frame whose origin coincides with that of theo1x1y1z1 frame rotates around o2z2 axis with vector R0 , andthe rotation angle is θ.
III. DYNAMICAL EQUATION AND COEFFICIENT
MATRIX OF THE ROTOR BLADE
A. Dynamical Equation
The dynamical equation of flexible multibody systems isgiven as follows: 
1  T M 
M q M q q
q  Kq  QF
2
q

(1)

In helicopter rotor system, the blade is hinge connected tothe hub, and flap around the hub. The hub rotates around theengine shaft. Due to the damping effect in these motions, thedamping coefficient matrix and the constraint equation are
introduced, and dynamical equation of the helicopter rotor
blade can be written as follows:

M q  D q  Kq  CqT   QF  QV

(2)

where M is the mass matrix, QF is the generalized externalforce vector, q is the generalized coordinate vector, q is thegeneralized speed vector, D is the damping coefficient matrix,
K is the stiffness coefficient matrix, Cq is the constraint
Jacobian matrix, λ is the Lagrange multipliers vector, andCqT  is the generalized constraint force.
T
Submitting Q   M q  1 q ( M ) q into Equation (2),V2qthe dynamical equation can be rewritten as:


.






1  T M 
M q  ( D  M ) q  Kq  CqT   QF  q (
)q
2
q

(3)

.


Where M is the partial derivative of mass matrix on time,M is the derivative of mass matrix on generalizedqcoordinate.
B. Position Vector
The displacement of the arbitrary point on beam element in
the inertial coordinate frame can be given as(4)
Rp  R0  A1 A2 A3 (u0  Nq f )where Rp  [ x0

 cos A1   0  sin 

y0

955

cos 
from o2x2y2z2 to OXYZ, A   sin 
3

 0

 sin cos 
000  is the
1 rotation matrix from o3x3y3z3 to OXYZ,  is the rotation
angle around OY axis,  is the rotation angle around oz1 axis,

 is the rotation angle around o2z2 axis, u is the vector ofpoint p after distortion in moving coordinates o3x3y3z3, u0 is
the vector of point p without distortion in moving coordinates
o3x3y3z3, and is the position vector of point p by distortion inmoving coordinates o3x3y3z3.
The flight conditions of helicopter is different withdifferent  ,  and  . The state of analysis in this paper is asshown in formula (4). At this time, the helicopter fly forward,
backward, or fly by hover. When   0 , the helicopter is in astatic state; when  is a fixed value and   0 , the helicopter
is in the air hover state; when β and φ are both constant, thehelicopter in the air flight state.
According to the finite element principle [7-8], thedisplacement of any point on the element can be represented
using the interpolation method. That is, the displacements ofthe element nodes are used to represent the elastic
deformation at any point on the element.
Setting u0 as the position vector before beam deformation,
N as the shape function of beam element, and qf as
displacement vector of beam element, the elastic deformation
at any point on the element can be given as follows:(5)
N f  Nq f
where
 n1 0N f   0 n3
 0 0

0
0
n3

0 0
0 0
0 n7

0
n4
0

n2
0
0

0
n5
0

0
0
n5

0
n6 
0 

0 0
0 0
0 n8

is

the shape function of beam element, and n1  1  x , n2  x ,
l
l
2
3
2
3
2
3
3x 2 x
3x 2 x2xx
n3  1  2  3 , n4  x 
 2 , n5  2  3 ,
l
l
l
l
l
l
2
3
3x
x
n6  
 2 , n7  n4 , n8  n6 , x is the abscissa of any
llpoint in moving coordinates o3x3y3z3, l is the length of thebeam element.
Space beam element has two nodes (i node and j node),each node has six degrees of freedom along the threecoordinate axes and three axes around the rotation, thus thenode displacement vector can be expressed as
q f  qi1 qi 2

qi 3

qi 4

qi 5

qi 6

q j1 q j1 q j1 q j1 q j1 q j1 

z0 ] is the position at the start time,

0 sin  
1
0  is the rotation matrix from o1x1y1z
0 cos  cos   sin  0 
to OXYZ, A   sin  cos  0  is the rotation matrix
2


0
1 
 0

C. Mass Matrix
Calculating the derivative on time in equation (4), we canget the speed of point p as follows:








R p  R0  A1  A2 A3U  A1 A2  A3U


(6)



 A1 A2 A3  U  A1 A2 A3 N q f

Due to the constant u0, we can get u 0  0 and u  N q f .
Base on this, the equation (6) can be reorganized as:

Copyright ⓒ 2016 GiRI (Global IT Research Institute)TICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 6, November 2016
 R0 
 
 

 
R p  [ I A1 A2 A3U A1 A2 A3U A1 A2 A3U A1 A2 A3 N ] 
 
 

q 
 f 

(7)

where R p is the velocity vector at any point on the beam,q f is the derivative of beam element’s displacement on time,  sin   cos  0 
A3   cos   sin  0  is the derivative of A3 on θ,
 0
0
0 
  sin  0 cos  
A1   00
0  is the derivative of A1 on  ,  cos  0  sin    sin   cos  0
A2    cos   sin  0 is the derivative of A2 on  ,
 0
0
0 


q   R0






T



q f  is the generalized velocity




  

vector.
The kinetic energy of the flexible body is given as:



1
1 T
T
(8)
T    Rp Rp dV  q M q
2 V
2where  and V are the density and volume of the beam
element respectively.
Submitting Equation (7) into Equation (8), we can kinetic
energy can be rewritten as:1
 R pT R p dV
2 V
(9)
I

A A AU  1 2 3 
1 T  q  A1 A2 A3U [ I A1 A2 A3U A1 A2 A3U A1 A2 A3U A1 A2 A3 N ] q dV
2 V  A1 A2 A3U 
A A A N 
 1 2 3 

T




1 T
q Mq
2

From Equation (8), the mass matrix of the rotor blade M
can be obtained.
D. The derivative of Mass Matrix on time
The derivative of mass matrix on time is given as follows:M q M 
(10)
M

q
q t
q
Since β, θ, and φ all contains one generalized coordinate, qf
has 12 generalized coordinates, and R0 has three generalizedcoordinates, q can be written as q   X 0 Y0





qi 4





Z0


qi 5



qi 6









  

qi1 qi 2

q j1 q j 2

q j3



qi 3


q j4

(11)


q j5


q j6 


956

The generalized coordinate is vector, thus the above
equation is the partial derivative of the mass matrix on eachgeneralized coordinate. Based on this, the derivative of mass
matrix on time can be rewritten as




 

m
m
m
m
m
11
12
13
14
15 




 

m22 m23 m24 m25 

(12)
18

M   M qi  
m33 m34 m35 i 1 qi 
m44 m45  m55 
where m11
 q
 i



M 

qi 






18

m 
m11   11 qi
i 1 qi

m12
qi

m13
qi

m14
qi

m22
qi

m23
qi

m24
qi

m33
qi

m34
qi
m44
qi

m15 qi m25 
qi 
,m35 
qi 
m45 
qi 
m55 
qi 





18

m 
m12  , 
qi m13  m31   13 qi ,i 1 qii 1 qi





18

m 
m14  , 
qi m15  m51   15 qi ,i 1 qii 1 qi



18

m 
m22  , 
qi m23  m32   23 qi ,i 1 qii 1 qi,

18

m12  m21  
18

m14  m41  
18

m22  


18

m 
m24  , 
qi m25  m52   25 qi ,i 1 qii 1 qi18

m24  m42  1818
m33  , 
m 
m 
qi m34   34 qi , m35   35 qi ,i 1 qii 1 qii 1 qi18

m33  


18
18

m 
m 
m44  , 
qi m45   45 qi , m55   55 qi .
i 1 qii 1 qii 1 qi
18

m44  E. Partial Derivatives of Kinetic Energy on Generalized
Coordinates
Based on the expression of kinetic energy in equation (9),the partial derivative of kinetic energy on the generalized
coordinate can be expressed as 1  T M  
q
 q
2
q1 
T



(13)
T 1 M qq   ...... 
q 2
q
 T

 1 q M q 
 2 q 18
F. Constraint Equation and Jacobian Matrix
The Lagrange multipliers method is used to build theconstraint equation in this paper. The general form of theconstraint equation is C (q, t )  0 . The distance between the

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blade root and the rotation center is constant, thus theconstraint equation of the rotation center is given as:(14)
C (q, t )  R0  A1 A2 A3u i  l0

The initial values are as follows: the rotor shaft rotates withthe speed of 36.63rad/s, the elastic deformation is 0, the initialfiwhere R0 is the position vector of the rotation center, u f is
the elastic deformation, l0 is the distance between the blade
root and the rotation center.
According the relationship of the location change between
each object, the constraint equation with variational form canbe obtained as Cqq  0 . Then, the constraint Jacobian matrix
can be derived as：c11 Cq  c
 m1 






c1n 
 
 

cmn 

(15)

where c  ci , m is the number of the constraint equations,ijqi
n is the number of generalized coordinate. In multibody
system, the element Cq is the the partial derivative of the i-th
constraint equation on the j-th generalized coordinate.
G. Stiffness Matrix
The virtual work of the cell in the object can be written as:W     T  dV
V

(16)

957





acceleration can be derived by q0  M 0 QF 0 , where M0 is theinitial mass matrix, QF 0 is the initial generalized external
force.
In the simulation, the viscous damping is used to simulate
the air friction within Rotating surface of the rotor blade. The
TABLE I
MAIN PARAMETERS
Parameter

Value

Deadweight
Radius of the rotor
Number of the blades
TorsionMoment of Inertia on blade
Stiffness coefficient of torsion

M=1975kg
l=5.5mn=4
Fz=35000N/m
Iz=6726kg/m

Damping coefficients of the rotor shaft

D =1600

Radius of the hub

r=0.5mk  18000 N / mmass matrix, partial derivative of mass matrix on time, partial
derivative of mass matrix on generalized coordinate, stiffness
matrix, damping matrix are input to matlab, and the violation
correction method is used to get the numerical solution of the
dynamical equation. The Iterative step is 10-4, and theiterative time is 2s.

where σ and ε are the stress and strain. For the linear
isotropic materials, we can get   ET  , where ET is theelastic quantity matrix. The strain can be gotby   Du f  DNq f , where D is the partial differential
operators of the strain-displacement.
Submitting the expression ofεinto equation (17), thevirtual work can be rewritten as:(17)
W  qTf K ff q f
From the equation (17), the beam element stiffness matrix
can be derived as:T

K ff   ( DN ) E DNdV

(18)

H. Damping Matrix
The aerodynamics model is two-dimensional steady modelin this paper. The introduction of damping coefficient has twofunctions. One is to balance the torque of the helicopter
engineer to make the rotor shaft uniform rotation. The other is
supporting a big attenuation coefficient to the flap of the blade,increase the iterative rate of the flap and reduce thecomputation time.
The damping coefficients of the rotor shaft and flapping
ream are D and D respectively, then the damping matrix

Fig. 2. The Curve ofof the system can be written as D  diag (0 D D 0) ,where diag () denotes the diagonal matrix.
IV. SIMULATIONIn this section, the simulation is used to verify the model
built in the section above. The MATLAB software is used tosolve the dynamical equation. The rotor type in the simulation
is OA212-207, and the main parameters are shown in Table 1.

Fig. 3. The Curve of 

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Fig. 4. The Curve of 

958

Fig. 7. Angular displacement with in the z-direction

Fig. 2 shows the angular displacement of the hub aroundthe main axis. The angular displacement of the hub is astraight line, and the rotational speed descends slowly withsmall oscillation. Fig. 3 shows the angular displacement of the
hub around dynamic axis and Fig. 4 shows the angulardisplacement of the impeller around dynamic axis. It can beseen that the two angular displacements are quadratic. At theend of time t = 2s, the angular displacement of the hub aroundthe dynamic axis and the angular displacement of the impeller
around dynamic axis are both agreement with the expected
results, which indicates that the coordinate transformation
matrix theory in this paper is correct.

Fig.8. Deformation with in z-direction

Fig. 5. Angular displacement with in the y-direction

Fig. 5 and Fig. 7 show the angular displacement of theblade end with in the y-direction and z-direction respectively,
and Fig. 6 and Fig. 8 show the deformation of the blade end
with in y-direction and z-direction respectively. From thesefigures, we can see that the a certain elastic deformation of theblade emerges due to its elasticity during the rotation of therotor blade, and the elastic deformation can make the blade
end deviates from a predetermined trajectory which willaffect the positioning accuracy. Thus, the elastic deformation
should be considered to make the rotational of the blade moreaccurate in practice. The vertical vibration cycle of the blade
end in the plane which is perpendicular to the rotation plane is
one seventeenth of that of the rotor shaft, and has lower
frequency than that of the blade end in the rotation plane.
These verify that the theoretical formula in this paper iscorrect.
V. CONCLUSION

Fig. 6. Deformation with in y-direction

The dynamical equation of the helicopter rotor blade
system is built based on the finite element method and the
Lagrange equation. Also, the mass matrix and its derivativeon time are obtained based on the flexible multibody
dynamics. In the simulation, the violation correction method
is used to get the numerical solution of the dynamical equation.
Simultaneously, the angular displacement of the hub aroundthe main axis, the angular displacement of the blade end andelastic deformation in the y-direction and z-direction are got

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and analyzed to verify that the model in this paper is correct.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

[7]
[8]

Yen, Gary G., and Moon K. Kwak, ―Dynamic modeling of flexible
multibody structures,‖ IEEE Transactions on Aerospace andElectronic Systems, 35.1 (1999): 148-156.
Grewal, A., and V. J. Modi, ―Multibody dynamics and robust control
of flexible spacecraft,‖ IEEE Transactions on Aerospace andElectronic Systems, 36.2 (2000): 491-500.
Gao, Feng, Fook Fah Yap, and Ying Yan,‖Modeling of hard disk
drives for vibration analysis using a flexible multibody dynamics
formulation.‖ IEEE Transactions on Magnetics, 41.2 (2005): 744-749.
Liu, Mengjun, Fook Fah Yap, and Hendri Harmoko, ―A model for a
hard disk drive for vibration and shock analysis,‖ IEEE Transactions
on Magnetics, 44.12 (2008): 4764-4768.
Boyer, Frédéric, and Shaukat Ali, ―Recursive inverse dynamics ofmobile multibody systems with joints and wheels,‖ IEEE Transactions
on Robotics, 27.2 (2011): 215-228.
Sun, Wei, Xiao-Guang Fan, and Tong Wang, ―Efficient Iteration
methods for Constrained Multibody Dynamics & Symposia,‖ 11th
IEEE International Conference on Control & Automation (ICCA),
2014.
Brezzi, Franco, and Michel Fortin. Mixed and hybrid finite element
methods. Vol. 15. Springer Science & Business Media, 2012.
Zienkiewicz, Olgierd Cecil, and Robert Leroy Taylor. The finite
element method: solid mechanics. Vol. 2. Butterworth-heinemann,
2000.

Liu Yi , born on 1993-01-03 in Beijing,
is a senior student in Beijing Institute ofTechnology, Beijing, China. The major isEngineering Mechanics.
His current research interests mainly
include multibody dynamics, mechanism
modeling and system identification modeling.
And he has published two conference papers.

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1960

Formulating Closeness Centralities on
Workflow-supported Performer-Activity Affiliation
Networks
Hyunah Kim*, Kwanghoon Pio Kim*
*Dept. of Computer Science, KYONGGI UNIVERSITY, Suwonsi Kyonggido, Republic of Korea
hyuna2486@naver.com, kwang@kgu.ac.kr

Abstract—This paper focuses on a special type of enterprisesocial networks, which is called ‘workflow-supported activityperformer affiliation network,’ and particularly formulates ametric of closeness centrality to numerically analyze the degreeof clerical familiarities among performers who are involvedin a workflow-supported activity-performer affiliation network.
A workflow model specifies enactment sequences of the associated activities and their affiliated relationships with roles,
performers, invoked-applications, and relevant data. These affiliated relationships can be revived into valuable organizational
knowledge supporting business intelligence as well as managerial
decision-making activities. In this paper, we particularly focus
on formulating the affiliated relationships between activities andperformers in a workflow model to numerically measure thecloseness centralities of performers as well as the closenesscentralities of activities. We also devise a series of algorithms for
implementing the formulated closeness centrality equations, anddescribe the ultimate implications of these closeness centrality
formulations in workflow-supported organizations.
Keywords-workflow-supported affiliation network, ICN-based
workflow model, organizational closeness centrality, businessprocess intelligence

I. I NTRODUCTION
In recent, the workflow literature starts being interested in
re-positioning the traditional workflow systems into the toolsof business and organizational knowledge and intelligence.
It begins from the strong belief that social relationships andcollaborative behaviors among workflow-performers obviously
affect the overall performance and being crowned with great
successes in the real businesses and the working productivity as well. The typical pioneering outcomes of thosere-positioning works ought to be [1][2][3], in which theauthors formalize mechanisms and their related algorithms———————————————————Manuscript received September 27, 2016. This work is a follow up of
the accepted conference paper as an outstanding paper in the 15th IEEE
International Conference on Advanced Communications Technology. This
research was supported by Basic Science Research Program through the
National Research Foundation of Korea(NRF) funded by the Ministry of
Education, Science and Technology (Grant No. 2012006971).
Hyunah Kim is with the department of computer science, Kyonggi University, 154-42 Kwangkyosan-ro Youngtong-gu Suwon-si Gyeonggi-do, 16227,
Republic of Korea (e-mail: hyuna2486@naver.com)
Kwanghoon Pio Kim is with the collaboration technology research laboratory in the department of computer science, Kyonggi University, 154-42
Kwangkyosan-ro Youngtong-gu Suwon-si Gyeonggi-do, 16227, Republic ofKorea (correponding author, phone: +82-31-249-9679, fax: +82-31-249-8949,
e-mail: kwang@kgu.ac.kr)

Fig. 1. Four Types of the Performer-Centered Affiliation Relationships inan Information Control Net of Workflow Modelto discover workflow-supported social networking knowledge
from workflow models and their enactment event logs. In
general, the workflow model is formally defined by using
the information control net (ICN)[4] methodology. In defining
a workflow model, we have to specify four types of theperformer-centered affiliation relationships[5] by associatingeach individual performer with all the essential entity-types
such as activity, role, application, and relevant data. The Fig.
1 illustrates these performer-centered affiliation relationships
in a specific ICN-based workflow model. We are particularlyinterested in the performer-activity affiliation relationships
in a workflow model, where the performers (or actors) arelinked in activities through joint participations; reversely, theactivities are connected to performers through joint involvements; the authors’ research group has modeled a collection ofthese participations and involvements as “workflow-supported
performer-activity affiliation network model[6][7].”In this paper, we focus on quantitatively measuring thedegree of performers’ familiarity by adopting the concept ofcloseness centralities into the workflow-supported performerCopyright ⓒ 2016 GiRI (Global IT Research Institute)

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activity affiliation network model. We assume that a workflowsupported performer-activity affiliation network is formed by
two key groups of the entity types such as a set of performers
and a collection of activities in a corresponding information
control net of workflow model. That is, we are basically concerned about numerically formalizing the closeness centralities
among the performers involved in fulfilling the ICN-based
workflow model. Basically, the affilation network is comingfrom the formal properties[8]—two-mode and non-dyadic
networks—of affiliation relationships. Since the workflowsupported performer-acitivity affiliation network is a two-mode
network, the complete measurements should be done by giving
centrality indices for both performers and activities. Generally,
there are four centrality analysis techniques, such as degree,
closeness, betweenness, and eigenvector centralities, and weparticularly measure the closeness centralities of performers
and the closeness centralities of activities in this paper.
By analyzing the closeness centralities on a workflowsupported performer-activity affiliation network formed froma set of activity-performer affiliated relationships in an ICNbased workflow model, it is eventually possible to visualize
and numerically express how much the performers and theactivities are interrelated and collaboratively closed in enacting the corresponding workflow model. As a consequence,
the main purpose of this paper is to theoretically develop
formulations and their algorithms for calculating the closeness centralities for activities and performers in a ‘workflowsupported performer-activity affiliation network. In the next
section, we simply describe the basic concept of the workflowsupported performer-activity affiliation network through theformal definition and the graphical representation as well. And,in the consecutive section we try to formulate the closenesscentrality equations and their implementation algorithms with
an operational example. Finally, we finalize the paper withdescribing a couple of related works in the last section.
II. W ORKFLOW-S UPPORTED P ERFORMER -ACTIVITYA FFILIATION N ETWORK M ODELIn order to represent the workflow-supported performeractivity affiliation knowledge, [6] has recently defined a graphical (Bipartite Graph) and formal representation model, whichis dubbed workflow-supported performer-activity affiliation
network model, which is abbreviated to APANM, and it
has two types of nodes—a set of performers and a set of
activities—and a set of relationships between those nodal
types. Thus a workflow-supported performer-activity affiliation
network model is a two-mode network model with aiming toaccomplish the following dual objectives:• to uncover the relational structures of workflowperformers through their joint involvement in activities,
and• to reveal the relational structures of workflow-activities
through their joint participation of common performers.
Additionally, those relational structures can be weighed tomeasure the extent of their strengths by assigning a value toeach of relations between nodal types. Therefore, there are
two types of performer-activity affiliation networks—binary

2
961
961

performer-activity affiliation network and valued performeractivity affiliation network. In the binary performer-activity
affiliation network, its value (0 or 1) implies a binary
relationship of involvement (or participation), while values
in the valued performer-activity affiliation network may
represent various implications according to their application
domains; typical examples of values might be stochastic(or probabilistic) values, strengths, and frequencies. The
formal knowledge representation of workflow-supported
performer-activity affiliation network model is defined in thefollowing [Definition 1][6].
[Definition 1] Workflow-Supported Performer-Activity
Affiliation Network Model. A workflow-supported
performer-activity affiliation network model is formallydefined as Λ = (σ, ψ, S), over a set C of performers (actors),a set A of activities, a set V of weight-values, a set
Ep ⊆ (C × A) of edges (pairs of performers and activities),and a set Ea ⊆ (A × C) of edges (pairs of activities andperformers), where, ℘(A) represents a power set of theactivity set, A:• S is a finite set of work-sharing actors or groups of someexternal performer-activity affiliation network models;• σ = σp ∪ σv /* Involvement Knowledge */where, σp : C −→ ℘(A) is a single-valued mapping function from a performer to its set of involved activities;
σv : Ep −→ V is a single-valued mapping function froman edge (∈ Ep ) to its weight-value;• ψ = ψa ∪ ψv /* Participation Knowledge */where, ψa : A −→ ℘(C) is a single-valued mapping
function from an activity to a set of participatedperformers; and ψv : Ea −→ V is a single-valued
function from an edge (∈ Ea ) to its weight-value;
Furthermore, the affiliation knowledge representation can begraphically depicted by an affiliation graph. So, a workflowsupported performer-activity affiliation network’s graphicalmodel consists of two types of graphical nodes—a set of performers (shaped in hexagon) and a set of workflow activities(shaped in circle)—and a set of non-directed edges between
two nodal types, which means that a workflow affiliation network is a non-directed graph. That is, in a workflow-supported
performer-activity affiliation graph, non-directed lines connect
performers aligned on one side of the diagram to the workflow
activities aligned on the other side. Importantly, a performeractivity affiliation graph does not permit lines among theperformers nor among the workflow activities. Therefore, aperformer-activity affiliation graph with g performers and hworkflow activities can be transformed into a matrix with 2dimension of g × h.
III. C LOSENESS C ENTRALITY M EASUREMENTF ORMULATIONS
In general, an affiliation networking graph[9] is a bipartite
graph, as described in the previous section, in which nondirected lines connect performers aligned on one side of the
diagram to the workflow activities aligned on the other side.
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Based upon the performer-activity affiliation networking graphand its affiliation matrix, it is possible to analyze a variety ofknowledge analytics issues[9], such as mean rates analysis[8],density measurements[8], and centrality measurements[9],raised from the social networking literature. In this paper, ourfocus concentrates upon the centrality measurements of theworkflow-supported affiliation network model. More precisely
speaking, we try to propose an algorithmic formalism foranalyzing organizational centrality measurements, particularly
closeness centrality measurements, of a workflow-supported
performer-activity affiliation network.
A. Definition of Affiliation Matrix
Eventually, it is necessary for the performer-activity affiliation network model to be analyzed in a mathematical representation. A workflow-supported performer-activity affiliation
network model is graphically represented by a bipartite graph,and at the same time it is mathematically represented by anaffiliation matrix. The affiliation matrix can be realized byeither an involvement matrix or a participation matrix. Thatis, a performer-activity affiliation network model is mathematically transformed into an activity-performer affiliation
matrix that records the presence and absence of g performers
at h workflow activities; thus its dimensions are g rows andh columns, respectively. If a certain performer φi attends aworkflow activity αj , then the entry in the ith and j th cell inthe matrix equals to 1; otherwise the entry is 0. Denoting abinary activity-performer affiliation matrix as Z, its xi,j values
meet these conditions:!

if performer, φi , is affiliated with activity, αjotherwise(1)• The row total, also called row marginals, (D r ), of aperformer-activity affiliation matrix Z sum to the numberof workflow activities that each performer will attend,which implies the involvement relations between activities and performers in a specific workflow model.
⎤g⎡h$Dr = ⎣
xi,j ⎦(2)

xi,j =

1
0

j=1

•

i=1

The column marginals, (Dc ), indicate the number of performers who will attend each workflow activity’s enactment, which implies the participation relations between
performers and activities in a corresponding workflow
model.
(h
' g
$
(3)
Dc =
xi,j
i=1

j=1

Also, assuming an affiliation networking graph has g performers and h activities, then its bipartite affiliation matrix
has dimensions (g + h) × (g + h). Consequently, using
the involvement affiliation matrix (Zp ) and the participation
affiliation matrix (Za ) forms an affiliation bipartite matrix,
XP,A , which can be schematically represented as the following
equations, (4) and (5).

XP,A =

3
962
962

)

*

(4)

XA = Za · Zp ;

(5)

0
Za

XP = Zp · Za

Zp0

B. Closeness Centrality Formulations[8] gives a series of well-described equations that can beapplied to calculating the closeness centralities based upon
the bipartite matrix of a workflow-supported performer-activity
affiliation network model. Before we develop an algorithm of
the closeness centrality measurements in the next subsection,
we need to restate those closeness centrality equations, andconsider the relationship between the closeness centrality ofa performer and the closeness centrality of the activities towhich the performer belongs, and the relationship between thecloseness centrality of an activity and the closeness centralityof its performers.
Basically, the meaning of closeness centrality index in asocial network[1][3] implies the average geodesic distance thata node is from all other nodes in the graph. In other words,
it is to calculate the ‘farness’ of a node from other nodes in
the graph. As described in the previous section, the performeractivity affiliation network is a special type of social network,
and it is represented by a bipartite graph with relationships(or connections) between performers and activities. Thus,
calculating the geodesic distances in a bipartite graph begins
with a function of the distances from the activities to theperformers which each of them belongs. The distance froma node i representing a performer to any node j (either
performer or activity) is d(i, j) = 1 + min{d(k, j)}k , forevery activity node k adjacent to i. Given this properties, thecloseness centrality of a performer in the bipartite graph can be
expressed with a function of the distances from the performer’s
activities, k:g+h$d(i, j) =g+h$[1 + min{d(k, j)}k ] , i ̸= j

(6)

j=1

j=1

1) Closeness Centrality of Performers: Based on the distance function of (6), the following expressions are the index
and the standardized index of the closeness centrality of aperformer with a function of the minimum geodesic distancesfrom its activities to other actors and to other activities, respectively. Note that every activity na is adjacent to performer ni .
• The Index of Closeness Centrality of Performers
⎤−1
⎡
g+h
$
d(i, j)⎦ (i ̸= j)(7)
OCC (ni ) = ⎣
j=1

⎡

•

OCC (ni ) = ⎣1 +

g+h
$
j=1

⎤−1

min{d(na , nj )}a ⎦(i ̸= j) (8)The Normalized Index of Closeness Centrality of Performers
SOCC(ni ) = (g + h − 1) · [OCC (ni )](9)Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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S
OCC
(ni ) =

'

1+

+g+h

j=1 min{d(na , nj )}a

g+h−1

(−1

(i ̸= j)(10)2) Closeness Centrality of Activities: By revising the distance function of (6), it is also necessary to make the expressions for the index and the standardized index of the closenesscentrality of an activity with a function of the minimum
geodesic distances from its performers to other activities andto other performers. Note that every performer mp is adjacent
to activity mj .
• The Index of Closeness Centrality of Activities
⎤−1
⎡
g+h
$
min{d(mp , mj )}p ⎦ (i ̸= j)
OCC (mi ) = ⎣1 +
j=1

(11)
• The Normalized Index of Closeness Centrality of Activities
(−1
'
+g+h
j=1 min{d(mp , mj )}p
S
OCC (mi ) = 1 +(i ̸= j)g+h−1
(12)Summarily speaking, the equations (9) and (12) are fornormalizing the index of closeness centrality by multiplyingby (g + h − 1). Suppose that a performer is close to all others,
which means that its adjacent activity has a direct tie to every
performer in the bipartite graph. Thus the computed index
values will be vary according to their graph sizes. In order tocontrol the size of the graph, it is necessary for the individual
index to be normalized so as to allow meaningful comparisonsof performers across different graphs. This explanation can be
identically applied to the normalized index for activities.
C. Algorithms of the Geodesic Distances
Based upon those closeness centrality equations, we develop
a series of algorithms for calculating the closeness centralitiesof all the performers as well as all the activities associated witha workflows-supported performer-activity affiliation network.
The following subsections concisely describe the details of
the algorithms and their explanations. Note that we won’t put
all the algorithms that are needed to calculate the closenesscentralities.
1) Algorithm of the geodesic distances for performers: By
extensively applying the equations of (8) and (10), we can calculate the closeness centralities of performers for a workflowsupported performer-activity affiliation network. The essential
part of those equations must be the functions of calculating
the geodesic distance from a performer node, ni , to anotherperformer node, nj , and the geodesic distance, which implies
the shorted path, from a performer node, ni , to an activity
node, mj , respectively. In this subsection, we devise an algorithm with recursive functions, to algorithmically implement
the essential equations. Assume that the algorithm operates on
a given performer-activity affiliation adjacency matrix, XP,A ,representing the corresponding workflow-supported performeractivity affiliation network, and its functional procedure nameis ‘PcGeodesicDistance()’ using two recursive functions,

4
963
963

‘gDistance()’ and ‘hDistance()’, which are calculating thegeodesic distances from a specified performer (ni ) to all theperformers and to all the activities, respectively. The outputof the algorithm is the geodesic distance of a performer, ni ,to either a performer or an activity, nj , and it is saved on
the performer-centered geodesic distance matrix, GP,A , as avalue of the cell, GP,A [ni , nj ]. The time complexity of thealgorithm is O(N), where N = g+h−1, and g is the number of
performers and h is the number of activities in a corresponding
workflow-supported performer-activity affiliation network.
The Geodesic Distances Algorithm for Performers:
01: Given Global A Binary Affiliation Bipartite Matrix, X P,A [g +h, g + h];
02: Given Global A Set of Performers, P;
03: Given Global A Set of Activities, A;
04: Procedure Name: PcGeodesicDistance
05: Input A Performer (From), ni ;06:
Either a Performer or an Activity (To), nj ;07: Output A Performer-Centered Geodesic Distance Measure,P,A[ni , nj ];G
08: Local An Activity Distance Vector, Gk [1..h], initialized by maximum;
09: Local A Performer Distance Vector, Hk [1..g], initialized by maximum;
10: Begin Procedure11:
If ( nj ∈ P ∧ ni ̸= nj )12:
For ( ∀mk ∈ Ak adjacent to ni )13:
Gk [mk ] ← gDistance(ni , mk , nj );
14:
roF
,
h

15:

16:
17:
11:
11:
20:
21:
22:
23:
24:
25:
26:
27:
28:
29:
30:

GP,A [ni , nj ] ← 1 + minimum Gk [i];i=1Else If ( nj ∈ A )For ( ∀mk ∈ Ak adjacent to ni )If ( mk = nj )Gk [mk ] ← 0; break;Else If ( mk ̸= nj )Ps ← all performers who are adjacent to mk ;Ps ← Ps − ni ;For ( ∀ns ∈ Ps )Hk [ns ] ← hDistance(mk , ns , nj );roF,-gGk [mk ] ← 1 + minimum Hk [i]roF,

GP,A [ni , nj ] ← 1 + minimum Gk [i]Return GP,A[ni , nj ];

-h

;

i=1

Initialize Hk [1..g] by maximum;;

i=1

31: End Procedure2) Algorithm of the Geodesic Distance for Activities: We
develop an algorithm for implementing the above equationsof (11) and (12) by revising the algorithm developed in theprevious subsection. By using the algorithm we are able tocalculate the closeness centralities from a activities’ point ofview. Likewise, the essential part of those equations must be
the functions of calculating the geodesic distance from an activity node, mi , to another activity node, mj , and the geodesic
distance from an activity node, mi , to a performer node, nj ,respectively. Assume that the algorithm also operates on agiven performer-activity affiliation adjacency matrix, XP,A ,representing the corresponding workflow-supported performeractivity affiliation network, and its functional procedure nameis ‘AcGeodesicDistance()’ using two recursive functions,’gDistance()’ and ‘hDistance()’, too. The output of thealgorithm is the geodesic distance measure of an activity, mi ,to either a performer or an activity, mj , and it is saved on theactivity-centered geodesic distance matrix, GP,A , as a value of
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Technology(TACT)(TACT)Vol.
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the cell, GA,P [mi , mj ]. The time complexity of the algorithmis O(N), where N = g + h − 1, and g is the number of
performers and h is the number of activities in a corresponding
workflow-supported performer-activity affiliation network.

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in developing a workflow-supported organizational intelligent
system supporting to measure the individual levels as well as
the group levels of the closeness centralities in a workflowsupported organization.

The Geodesic Distances Algorithm for Activities:
01: Given Global A Binary Affiliation Bipartite Matrix, X P,A [g +h, g + h];
02: Given Global A Set of Performers, P;
03: Given Global A Set of Activities, A;
04: Procedure Name: AcGeodesicDistance
05: Input An Activity (From), mi ;06:
Either an Activity or a Performer (To), mj ;07: Output An Activity-Centered Geodesic Distance Measure,A,P[mi , mj ];G
08: Local A Performer Distance Vector, Hk [1..g], initialized by maximum;
09: Local An Activity Distance Vector, Gk [1..h], initialized by maximum;
10: Begin Procedure
11:
12:
13:
14:
15:
16:
17:
18:
19:
20:
21:
22:
23:
24:
25:
26:
27:
28:
29:
30:

If ( mj ∈ A ∧ mi ̸= mj )For ( ∀nk ∈ Pk adjacent to mi )Hk [nk ] ← hDistance(mi , nk , mj );roF,-gGA,P [mi , mj ] ← 1 + minimum Hk [i];i=1Else If ( mj ∈ P )
For ( ∀nk ∈ Pk adjacent to mi )If ( nk = mj )Hk [nk ] ← 0; break;Else If ( nk ̸= mj )As ← all activities that are adjacent to nk ;As ← A s − m i ;For ( ∀ms ∈ As adjacent to nk )Gk [ms ] ← gDistance(nk , ms , mj );roF,hHk [nk ] ← 1 + minimum Gk [i]Initialize Gk [1..h] by maximum;

roF,

GA,P [mi , mj ] ← 1 + minimum Hk [i]Return GA,P [mi , mj ];

-g

;

i=1

;

i=1

31: End Procedure

D. Implications of the Closeness Centralities
In this paper, we are particularly interested in adopting
the concept of closeness centrality to measure the degreeof familiarity among performers in a workflow-supportedorgranization. The semantic significance of closeness distancein terms of the familiarity metric refers to how quickly aperformer can interact with others via intermediary activities
where the performers are jointly participating to. In consequence of those consecutive calculations of all the performers,
we can draw the answers to the following question frommeasuring the closeness centralities on a workflow-supported
performer-activity affiliation network:• The degree of familiarity: How quickly can a performer
interact with others via very few intermediary activities
in enacting workflow procedures?
Conclusively, the answer to the question is able to convey
a very valuable and meaningful insight to the corresponding
workflow-supported organization. We assure that the primary
rationale of the closeness centrality ought to be on the question and the answer. We strongly believe that a series oftheoretical formulations on the closeness centralities and theirimplementable algorithms devised in this paper can be used

IV. R ELATED W ORKS
Recently, technology-supported social networks and organizational behavioral analytics issues have been raised in the IT
literature. Naturally, the workflow literature has just started
transitioning into and focusing on social and collaborative
work analyses in workflow-supported organizations, because
workflow management systems are “human systems,” where
workflow procedures must be designed, deployed, and understood within their social and organizational contexts. It isquite natural for the concept of enterprise social networks(workflow-supported affiliation networks) to be raised andissued from these human-centered organizational contexts. It
is important to remind that the human-centered affiliation
relationships reveal how each of the individuals is associatedwith the essential entity-types of the organizational resources
like activity, role, application, and relevant data. Particularly,
in this paper we focus on the Performer-Activity affiliation
networking knowledge[7] and fomulate their equations forcalculating the closeness centralities among the performers.
K. P. Kim [7] firstly issued the workflow-supported
performer-activity affiliation network as a special type of organizational social network knowledge acquired from deploying
workflow technologies. In the paper, the author theoreticallyderived a series of concepts and algorithms not only for
representing and discovering those knowledge but also for
analyzing the discovered knowledge. Battsetseg, et al. [10]proposed a theoretical formalism to analyze a workflowsupported performer-activity affiliation network by measuring the organizational closeness centralities of performers aswell as the organizational closeness centralities of activities.
Note that we try to extend the proposed theoretical formalisms through this paper. H. Kim, et al. [11] formalized
the workflow-supported performer-role affiliation network. In
the paper, the authors formally defined the workflow-supported
performer-role affiliation networking knowledge through a series of theoretical formalisms and practical implementation formodeling, discovering, and visualizing workflow performerrole affiliation networking knowledge. H. A. Reijers, et al. [12]pioneered the human-centered resource management issue ina workflow-suppoted organization, which can be interpreted
by a conceptual species of the workflow-supported affiliation networks. Through this research, they showed that thehigh degree of geographical closenesses among workflowperformers be leaded to the posivitve effect on workflowsupported organizational performance by conducting a casestudy of distributed teamworks on a workflow process model.
Conclusively, we would say that these pioneering works,
until now, concerning about the human-centered affiliation
knowledge are the outputs in the stage of initiative researchworks, which is the discovery phase. The next stage ought tobe the analysis phase. The paper of [10] was just a halffinishedstep forward to the analysis phase shifting from the discovery
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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AdvancedCommunications
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Technology(TACT)(TACT) Vol.
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ICACT
5, Issue 6,phase. In particulay, P. Busch and his colleague in [13][14]raised the logical necessity of the conceptual triangulation of
workflow management, social network analysis, and knowledge management, which ought to be one of the circumstantial
evidences of the theoretical importance of this paper.
V. C ONCLUSION
In this paper, we have formulated a series of closeness centrality measurement equations and proposed their related algorithms and descriptions for analyzing a workflow-supported
performer-activity affiliation network representing involvement and participation behaviors between workflow-based
people and workflow-based activities. We have introduced thebasic concept of workflow-supported performer-activity affiliation network and its implications as a meaningful mechanismof organizational knowledge and intelligence. Particularly, we
restate the mathematical equations for the closeness centrality
measurements, and develop an functional algorithm for implementing those closeness centrality equations. As a future work,
we have a plan to implement those concept and algorithms formeasuring the closeness centralities as a fundamental functionof the organizational knowledge and intelligent management
system.
ACKNOWLEDGMENT
This research was supported by Basic Science Research
Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science andTechnology (Grant No. 2012006971).

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[12] H. A. Reijers, M. Song, and B. Jeong, “Analysis of a collaborative
workflow process with distributed actors,” Information Systems Frontiers, vol. 11, no. 3, pp. 307–322, 2009.
[13] E. Kim and P. Busch, “Workflow interpretation via social networks,”
Lecture Notes on Artificial Intelligence, vol. 9806, pp. 241–250, 2016.
[14] P. Busch, “Business process management, social network analysis andknowledge management: A triangulation of sorts?” in Proceedings ofthe the 21st Australasian Conference on Informaiton Systems, Brisbane,
Austrailia, Dec. 2010.

Hyunah Kim Dr. Kim is an adjunctive professor
and a faculty member of the collaboration technology research laboratory in the department of computer science at Kyonggi University, South Korea.
She received her B.S. degree in computer sciencefrom Korea Nazarene University in 2001. Also,
she received her M.S. and Ph.D. degrees in computer science from Kyonggi University in 2003 and2009, respectively. She has been on the operational
committees of several domestic and international
conferences including KSII, AP-IST, ICONI, andICACT. Her research interests include workflow systems, SCORM-based
e-Learning process models, BPM, BPI, ACM, workflow-supported social
networks discovery and analysis, and process-aware Internet of Things.

R EFERENCES[1] W. M. P. van der Aalst, H. A. Reijers, and M. Song, “Discoveringsocial networks from event logs,” Computer Supported Cooperative
Work, vol. 14, no. 6, pp. 549–593, 2005.
[2] J. Won, “A framework: Organizational network discovery on workflows,”
Ph.D. dissertation, Kyonggi University, 2008.
[3] J. Song, M. Kim, H. Kim, and K. Kim, “A framework: Workflow-basedsocial network discovery and analysis,” in Proceedings of the IEEE 13th
International Conference on Computational Science and Engineering,
Hongkong, China, Dec. 2010, pp. 421–426.
[4] C. A. Ellis and G. J. Nutt, “Office information systems and computerscience,” ACM Computing Surveys, vol. 12, no. 1, pp. 27–60, 1980.
[5] K. Kim, “Actor-oriented workflow model,” in Proceedings of the 2nd international symposium on Cooperative Database Systems for Advanced
Applications, WOLLONGONG, AUSTRALIA, Mar. 1999, pp. 150–164.
[6] H. Kim, H. Ahn, and K. P. Kim, “A workflow affiliation network
discovery algorithm,” ICIC Express Letters, vol. 6, no. 3, pp. 765–770,
2012.
[7] K. P. Kim, “Discovering activity-performer affiliation knowledge on icnbased workflow model,” Journal of Information Science and Engineering, vol. 29, pp. 79–97, 2013.
[8] D. Knoke and S. Yang, SOCIAL NETWORK ANALYSIS - 2nd Edition,
Series: Quantitative Applications in the Social Sciences.
SAGE
Publications, 2008.
[9] K. Faust, “Centrality in affiliation networks,” Journal of Social Networks,
vol. 19, pp. 157–191, Aug. 1997.
[10] A. Battsetseg, H. Ahn, M. Park, H. Kim, W. Yoon, and K. P. Kim,
“Organizational closeness centrality analysis on workflow-supported
activity-performer affiliation networks,” in Proceedings of the IEEE 13th
International Conference on Advanced Communications Technology,
Phoenix Park, Pyeongchang, South Korea, Feb. 2013.
[11] H. Kim, H. Ahn, and K. P. Kim, “Modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge,” KSII
Transactions on Internet and Information Systems, vol. 8, no. 2, pp.
134–151, February 2014.

Kwanghoon Pio Kim Dr. Kim is a full professorof computer science department and the founder andsupervisor of the collaboration technology researchlaboratory at Kyonggi University, South Korea. He
received B.S. degree in computer science from Kyonggi University in 1984. And he received M.S. degree in computer science from Chungang University
in 1986. He also received his M.S. and Ph.D. degrees
from the computer science department at Universityof Colorado Boulder, in 1994 and 1998, respectively.
He had worked as researcher and developer at Aztek
Engineering, American Educational Products Inc., and IBM in USA, as well
as at Electronics and Telecommunications Research Institute (ETRI) in South
Korea. In present, he is a vice-chair of the BPM Korea Forum. He has beenin charge of a country-chair (Korea) and ERC vice-chair of the Workflow
Management Coalition. He has also been on the editorial board of the journalof KSII, and the committee member of the several conferences and workshops.
His research interests include groupware, workflow systems, BPM, adaptive case management (ACM), CSCW, collaboration theory, Grid/P2P/Cloud
distributed workflow systems, process warehousing and mining, workflowsupported social networks discovery and analysis, process-aware information
systems, data intensive workflows, and process-aware Internet of Things.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)