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Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
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Dr. Ting Peng, Chang'an University, China
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Copyright ⓒ 2014 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

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume 2, Issue 6

1Gain and Bandwidth Improvements for Distributed Raman Amplifier in UW-WDM Communication Systems

329

Fathy M. Mustafa *, Ashraf A. M. Khalaf **, F. A. Elgeldawy ***Bani-suef University, Egypt,** ElMinia University, Egypt

2

Optimum Stealthy Aircraft Detection Using a Multistatic Radar

337

Hassan El-Kamchouchy, Khaled Saada, Alaa El-Din Sayed Hafez
Alexandria University, Alexandria, Egypt

3

Dynamic Physical Impairment-Aware Routing and Wavelength Assignment in 10/40/100 Gbps Mixed Line Rate
Optical Networks

343

Filippos BALASIS, Xin WANG, Sugang XU, and Yoshiaki TANAKA
Waseda University, Japan

4

Intelligent Packaging and Intelligent Medicine Box for Medication Management towards the Internet-of-Things352

Zhibo Pang*, Junzhe Tian**, Qiang Chen***ABB AB, Västerås, Sweden, ** Royal Institute of Technology (KTH), Stockholm, Sweden

5

Introducing Dual Regularization Parameters into Regularized Channel Inversion (RCI)-Based Vector Perturbation for
Modulo Loss Reduction
Yafei Hou, Satoshi Tsukamoto, Kazuto Yano, Masahiro Uno, Kiyoshi Kobayashi
ATR Wave Engineering Laboratories, Kyoto, Japan

361

ICACT
ICACT Transactions
Transactions on
on Advanced
Advanced Communications
Communications Technology(TACT)Technology (TACT)Vol.
Vol.2,
2,Issue
Issue6,
6,November
November2013
2013

329
329

Gain and Bandwidth Improvements forDistributed Raman Amplifier in UW-WDM
Communication Systems
Fathy M. Mustafa *, Ashraf A. M. Khalaf **, F. A. Elgeldawy *** Electronics and Communications Engineering Department, Bani-suef University, Egypt** Electronics and Communications Engineering Department, ElMinia University, Egypt
fmmg80@yahoo.com, ashkhalaf@yahoo.com, f.elgeldawi@yahoo.com

Abstract—Fiber Raman amplifiers in ultra wideband
wavelength division multiplexing (UW-WDM) systems haverecently received much more attention because of their greatly
extended bandwidth and distributed amplification with the
installed fiber as gain medium. It has been shown that the
bandwidth of the amplifier can be further increased and gain
spectrum can be tailored by using pumping with multiple
wavelengths. In this paper, the distributed multi-pumping
Raman amplifier has been studied and analyzed by testing two
designed model of amplifier to obtain the gain of maximum
flatness and bandwidth. Also we have investigated the effects ofmany parameters on the gain and bandwidth of Raman
amplifier such as: pumping wavelength, offset wavelength, therelative refractive index difference and the number andlocation of the cascaded units used in the amplifier model
design. The models is analyzed where six and eight Raman
pumping of special pumping power and pumping wavelengths
are lunched in the forward direction where each model isanalyzed by two different way. The gain is computed over the
spectral optical wavelengths (1.45µm ≤ λ signal ≤ 1.65µm). The
differential gain of each unit of the amplifier is obtained
according to the straight line-exponential model of a small
maximum constant gain of 7.4×10-14 m/W over an optical
wavelength interval of 16 nm

Keyword—Distributed Raman amplifier, Raman gain,
Raman Bandwidth, Ultra wideband-wavelength divisionmultiplexing (UW-WDM ).

O

I. INTRODUCTIONPTICAL amplifiers have played a critical role in thetelecommunication revolution that has begun two
decades ago. Raman amplification has enabled adramatic increase in the distance and capacity of light wave
systems [1]-[3].
Manuscript received January 5, 2013. This work is sponsered by theDept. of Electric Eng., Minia University, it is a part of Ph.D study of the 1st
author under supervision of the 2nd and 3rd authors.
F. M. Mustafa is a research assistant in electronics andcommunications department at Bani-suef university, Egypt. He joined the
PhD program in Minia university in 2011 (fmmg80@yahoo.com ).
A. A.M. Khalaf is with the electronics and communications
engineering department, ElMinia university, Egypt . He was a Ph.D
student in Graduate School of Natural Science and Technology, Kanazawa
university, Japan from 1996 to 2000. He is a corresponding author of thispaper (Phone: +20 86 2355261,
Fax: +20 86 2346674,
ashkhalaf@yahoo.com )F. A. El-Geldawy is a professor with electrical engineering
department, faculty of engineering, ElMinia, Egypt (e-mail:f.elgeldawi@yahoo.com).

There are mainly three reasons for the interest in Raman
amplifier. First its capability to provide distributed
amplification second is the possibility to provide gain at any
wavelength by selecting appropriate pump wavelengths, andthe third is the fact that the amplification bandwidth may bebroadened simply by adding more pump wavelengths. An
important feature of the Raman amplification process is that
amplification is achievable at any wavelength by choosing
the pump wavelength in accordance with the signal
wavelength [4].
The term distributed amplification refers to the methodof cancellation of the intrinsic fiber loss. The loss in
distributed amplifiers is counter balanced at every point
along the transmission fiber in an ideal distributed amplifier[4].
In the mid-nineties, high-power pump lasers became
available and in the years following, several system
experiments demonstrated the benefits of distributed Raman
amplification including repeater-less undersea experiments,
high-capacity terrestrial as well as submarine systems
transmission experiments, shorter span single-channel
systems including 320 Gbit/s pseudo linear transmissions,and in soliton systems [4].
distributed Raman amplifiers improved noise
performance because of amplification at any wavelength
controlled simply by selecting the appropriate pumpwavelength, extended bandwidth achieved by using multiple
pumps when compared to amplification using erbium-doped
fiber amplifiers (EDFAs), and finally control of the spectral
shape of the gain and the noise figure, which may be
adjusted by combining and controlling the wavelength andpower among multiple pumps [4], [6].
Raman amplifiers pumped at multiple wavelengths draw
significant attention in high-speed long-haul WDM
transmission, for example, because of their wideband flatgain profile (100nm with 12 channel-WDM pumping) andsuperior signal-to-noise ratio (SNR) performance. However,they require numbers of high power pump lasers to achieve
high-gain and high bandwidth which makes it very
expensive at the initial deployment stage where the WDM
bandwidth is not in full use. While modular band-by-band
and high upgrade like EDFA-based WDM systems reduces
system introduction cost very much, in which either C or Lband EDFAs can be added later when a new bandwidth
becomes needed. However, such modular addition of
amplifiers is not possible for a DRA in which a transmission
fiber is shared as common-gain medium. Neglecting

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ICACTTransactions
Transactionson
onAdvanced
AdvancedCommunications
CommunicationsTechnology
Technology(TACT)ICACT(TACT) Vol. 2, Issue 6, November 2013

nonlinear pump interaction or saturation WDM-pumped
Raman amplifier gain can be approximated as the linear
superposition of Raman gains induced by each pump laser[7].
Currently, RFAs are the only silica-fiber based
technology that can extend the amplification bandwidth to
the S band while providing performance and reliability
comparable with those of EDFAs. However, the noise figure
remains high compared to that of the C and L bands [8].
In this paper, the distributed multi-pumping Raman
amplifier has been studied and analysed by using N
cascaded Raman amplifier units, N pumping signals are
injected in a parallel processing at different pumping powers
and wavelengths. The designed model of amplifier are
considered to obtain the gain of maximum flatness andwider bandwidth. Also we have investigated the effects ofmany parameters on the gain and bandwidth of Raman
amplifier such as: pumping wavelength, offset wavelength,
the relative refractive index difference and the number andlocation of the cascaded units used in the amplifier model
design. The gain is computed over the spectral optical
wavelengths (1.45µm ≤ λ signal ≤ 1.65µm). The differential
gain of each unit of the amplifier is obtained according to
the straight line-exponential model of a maximum gain
constant of 7.4×10-14 m/W over an optical wavelengthinterval of 16 nm. Computer simulations are carried out
using the Matlab software package.

Where, "i" is the number of cascaded units, λr is Raman
pumping wavelength and λo≥1.35um. The symbol , is theRaman shift that indicates the position of each ith amplifier
unit.
. ,

,

Figure 1 shows an N-Raman amplifier in a cascade formof special pumping powers Pr1, Pr2, Pr3, Pr4,…, PrN andcorresponding pumping wavelengths λr1, λr2, λr3, λr4,……., λrN.

The gain, g, of multi-pump Raman amplifier
versus wavelength, λ

,

.

.
,

10 , cm

10 ,

,

4

With 1 cm-1 = 30 GHz [11], where λo,i indicates the offset
wavelength and λr,i indicates the pumping wavelength ofeach amplifier. These wavelengths are then used to indicate
δo,i for each amplifier.
,

,

,

5

,

Where,
7.4 10/ is the differential Ramangain constant (of pure SiO2 at λ = 1.34 µm), and
,

.

,

.

10 ,

6

.

10 ,

7

,

,

.

,

е

,

,

.

,

,

,

8

∆λ = λ2 – λ1=16 nm (fixed value for all units)
10 ,

0.044

1.34

The map of δ-g or λ –g is as shown in Figure 1, where δis the Raman shift and g is the Raman differential gain
coefficient; both were cast based on [9-13] as:λ3

9By changing the position δo,i, the total bandwidth and theflatness of the amplifier are changed.
We are interested in obtaining a large bandwidth with awider flat gain by changing δo,i or λo,i. In this case, we canuse either of two cases: δo=δr (i.e. λo=λr) or δo>δr (i.e.
λo>λr), where λr is Raman pump wavelength and λo is theoffset wavelength.
Raman differential gain constant, g, and the effective corearea, A, are defined as [9]:Figure 1.

λλ

440

,

With

1

λ

0

And

II. MATHEMATICAL MODEL

δ

330
330

1

1

10

80

,
10

Where ∆ is the refractive index difference, n1 is therefractive index of the core, and n2 is the refractive index of
the clad.

The general equations representing the Raman gain in thethree regions are , , , and , respectively [13]. Where,
i denotes the order of amplifier unit in cascade.

2

,

11

And
,

,

440

2
Copyright ⓒⓒ 20132013 GiRI(Global
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ICACT Transactions
Transactions on
on Advanced
Advanced Communications
Communications Technology
Technology(TACT)ICACT(TACT) Vol.
Vol. 2,2, Issue
Issue 6,
6, November
November 20
20 13
13

0.21

12
√∆
Where, Ws and Wr are the mode field radii of two lightwaves coupled with each other with W=Ws at λ= λs and
W=Wr at λ= λr .
Neglecting the cross coupling among the signal
channels, one has the differential equation governing thesignal propagation for N-channels Raman pumping [9]:,13Where, i = 1,2,3,……..N, M is the number of pumps, Si issignal power and PRj is the jth unit pumping power.
Assume the R.H.S of equation (13) equals gti , as:,14

in m-1 represents the
The total (overall) gain coefficient,thtotal gain coefficient of the i signal due to the N-pumping
is a function of a set ofcascaded units. It is clear thatvariables such as: signal wavelength; fiber core radius;
Raman wavelength; relative refractive index difference; andcan be written
Raman pumping power. The overall gain,in the form:,

Where, the total differential gain,

15

, is:

,

16

And by Defining a total gain coefficient per watt (m/w) ,, as:,Then, we have three gain coefficients:
functions of the propagation distance.
III.

17

,

and

, are

MODEL EXAMPLES AND CASESIn this paper, we investigated two model examples ofcascaded Raman amplifier, these examples are the 6th orderand 8th order Raman amplifier, respectively. Both of theseexamples design are obtained by using the following
equation:
λ1

λ0.044λ10 , µm

TABLE 1.MODEL 1: NUMBER OF AMPLIFIERS = 6

18

331
331

Λr

λo

λ1

λ2

Pr(W)

1.4

1.432

1.528294799

1.544294799

0.20

1.42
1.44

1.452
1.472

1.548294799
1.568294799

1.564294799
1.584294799

0.15
0.15

1.467
1.48

1.499
1.512

1.595294799
1.608294799

1.611294799
1.624294799

0.20
0.15

1.5

1.52

1.616294799

1.632294799

0.15

TABLE 2.MODEL 2: NUMBER OF AMPLIFIERS = 8
λr

λo

λ1

λ2

Pp(W)

1.4

1.4443

1.540830404

1.556830404

0.14

1.405

1.448

1.545830404

1.561830404

0.12

1.425

1.471

1.568830404

1.584830404

0.14

1.43

1.477

1.574830404

1.590830404

0.10

1.45

1.499

1.596830404

1.612830404

0.14

1.455

1.505

1.602830404

1.618830404

0.12

1.475

1.528

1.625830404

1.641830404

0.11

1.48

1.534

1.631830404

1.647830404

0.13Firstly, we choose the offset wavelength of the first unit of
the cascaded units consisting the overall Raman amplifier;
we choose λo from the wave length range (from 1.45µm –to- 1.65µm). Then, we calculate λ 1 using Eq.(18). After
that, we assume the identical Raman units of individual
bandwidth of λ2 – λ1=16 nm. This is done for each amplifier
unit.
Finally, we obtain the two design examples , which are the
6th order and 8th order Raman amplifier as shown in TABLE
1 and TABLE 2, respectively. Note that λ1 – λo=0.096294798, and λ2 – λ1=16 nm.
We consider two cases for the pumping wavelength λ r ,these two cases are considered for each model example.
A. Case A : λ r ≠ λo
B. Case B : λ r = λoFor each case we execute a simulation program to find anddemonstrate the figures of the three gain coefficients of the,and.
Raman amplifier:
There are many parameters affecting the gain coefficientssuch as: effective core area, relative refractive index
difference, pumping wavelengths and pumping powers. So
these parameters must be taken into account for any design.
IV. SIMULATION RESULTS AND DISCUSSIONSThe bandwidth for distributed multi-pumping Raman
amplifier is investigated with aim of obtaining maximum
flat-gain amplifier with as wider bandwidth as possible.
Bandwidth; ∆λr, can be evidently broadened by means of

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increasing the number of pumps (amplifier units) and by
adjusting the position of these cascaded units.
As we said in Sec. III, we have investigated the
distributed multi-pumping Raman amplifier by two model
examples that are 6th order and 8th order amplifier. Eachmodel example have been tested under two cases. That are :λ r ≠ λo case and λ r = λo.
A. Results of Model 1:,
For The 6th order Raman, the three gain coefficients, are obtained through simulation results andanddepicted in the corresponding figures.
A. Model 1: Case A : λ r ≠ λo
In this case we put the pumping wavelengths of theamplifier units not equal to the offset wavelengths.
The design structure of the 6th order Raman amplifier isobtained as shown in TABLE 1.
The following results will be demonstrated as:1) Differential gain:
Figure 2 displays the differential Raman gain versus
wavelength, λ, at different values of the relative refractive
index difference (∆). If the relative index difference
increases, Raman gain will increase.

Figure 3. Raman gain variation with different values of.

Figure 3 depicts the relation between Raman gain andpumping wavelength. This figure is plotted at different
values of relative index difference, where pumping
wavelengths for optical signals in the range from 1.4 to 1.55
µm, this range is suitable for Raman amplifier to avoid noiseand losses.

Figure 4. The gain per unit watt at different values ofwavelength λFigure 2. Differential gain versus the wavelength λ at

different and λ r ≠ λo
We note that Raman gain starts to increase from the firstpumping wavelength to reach its peak value at 1.54µm, thenit decreases exponentially tending to zero at 1.65µm. Since
the optical amplifiers and optical signals are operated in therange of 1.45µm to 1.65µm. In this case we obtained, theoverall bandwidth of the cascaded amplifier =110nm, (from
λ1t =1.5µm to λ2t =1.61µm).

Figure 4 shows the gain coefficient per unit watt, at different
values of relative refractive index difference.
We note that gain coefficient/unit watt starts to increase
from the first pumping wavelength to reach its peak value at
1.56µm, then it decreases exponentially tended to zero at
1.65µm. In this case we obtained, a total bandwidth (overall
bandwidth) =110nm, (from λ1t =1.5µm to λ2t =1.61µm).
Depending on these results, we noticed that there are many
parameters affecting the gain coefficient per unit watt suchas: effective core area, relative refractive index difference,
pumping wavelengths and pumping powers. So theseparameters must be taken into account for any design.
2) The total gain coefficient:Figure 5 displays the variation of the total gain coefficientwith wavelength. In this case, a bandwidth of 110 nm is
obtained. Similarly, we note the peak value of the gain is at
1.54µm, and the gain tended to zero at 1.65µm.
It is found that, when pumping powers increase the total
gain increases, so Raman amplifier is preferred to be usedwith high pumping powers.

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Figure 5. Variation of total gain coefficient with wavelength.

B. Model 1: Case B : λ r = λoIn this case we put the pumping wavelengths equal to theoffset wavelengths of the corresponding units.
The following results will be demonstrated as:

333
333

Figure 7. Gain coefficient per unit watt against wavelength.

3) The total gain coefficient:Figure 8 displays the variation of the total gain coefficientwith wavelength. In this case, a bandwidth of 120nm is
obtained. Similarly, we note the total gain coefficient has its
peak value at 1.54µm, and its zero value at 1.65µm.

1) Differential gain:
Figure 6 displays the differential Raman gain, g, withwavelength, λ, at different values of the relative refractive
index difference.

Figure 8. Variation of total gain coefficient with wavelength.

Figure 6. Differential gain versus the wavelength λ at

different and λ r = λo
If relative index difference increases, Raman gains
increases.
We note that Raman gain starts to increase from the firstpumping wavelength to reach to peak value at 1.54µm, thenthe gain decreases exponentially tended to zero at 1.65µm.
In this case we obtained, an overall bandwidth =120nm, (λ1t=1.49µm and λ2t =1.61µm).

B. Results of Model 2:,
For The 8th order Raman, the three gain coefficients :and, are obtained through simulation results anddepicted in the corresponding figures.
A. Results of Model 2: Case A : λ r ≠ λo
In this case we put the pumping wavelengths of theamplifier units not equal the offset wavelengths.
The design structure of the 8th order Raman amplifier isobtained as shown in TABLE 2.
1) Differential gain:

2) The gain coefficient per unit watt:
The gain coefficient/unit watt, against wavelength is shownin Fig. 7 at different values of relative refractive index
difference.
We note that the gain coefficient/unit watt starts to increase
from the first pumping wavelength to reach its peak value at
1.56µm, then the gain decreases exponentially tended tozero at 1.65µm.

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In this case we obtained, total (overall) bandwidth=120nm, where λ1t =1.5µm and λ2t =1.62µm.
3) The total gain coefficient:Figure 11 displays the variation of the total gain coefficientwith wavelength. In this case, a bandwidth of 120 nm is
obtained. Similarly, we note the total gain coefficient starts
to increase from the first pumping wavelength to reach topeak value at 1.56 µm. The gain decreases exponentially
until it reaches to zero at 1.65µm. Gain in this case isaffected by pumping powers, effective core area and therelative index difference.

Figure 9. Differential gain versus the wavelength λ at

different and λ r ≠ λo
Figure 9 displays the differential Raman gain, g m/w,
with wavelength, λ, at different values of the relativerefractive index difference (∆). If the relative index
difference increases, Raman gain will increase.
We note that Raman gain starts to increase from the firstpumping wavelength to reach to peak value at 1.56µm, thenthe gain it starts to decrease exponentially tending to zero at
1.65µm. Because of optical amplifiers and optical signals
are operated in range 1.45µm to 1.65µm. In this case we
obtained, total bandwidth =120nm, where λ1t =1.5µm , and
λ2t =1.62µm.
2) The gain coefficient per unit watt:
Figure 10 shows the gain coefficient/unit watt, ∑ gi / Ai, m¯¹
W¯¹ at different values of relative refractive index
difference.

Figure 11. Variation of total gain coefficient withwavelength.
It is found that, when pumping powers increase the total
gain increases, so Raman amplifiers is preferred to be usedwith high pumping powers.
B. Model 2 :Case B: λ r = λoIn this case we put the pumping wavelengths equal to theoffset wavelengths of the corresponding units.
1) Differential gain:
Figure 12 displays the differential Raman gain, g, withwavelength, λ, at different values of the relative refractive
index difference. If relative index difference increases,
Raman gains increases.

Figure 10. The gain per unit watt at different values of .
We note that gain coefficient/unit watt starts to increase
from the first pumping wavelength to reach to peak value at
1.56µm, then the gain decreases exponentially tended tozero at 1.65µm. Depending on these results, we can say that
there are many parameters affect the gain coefficient perunit watt such as: effective core area, relative refractive
index difference, pumping wavelengths and pumping
powers. So these parameters must be taken into account forany design.

Figure 12. Differential gain versus the wavelength λ at
different and λ r = λo

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We note that Raman gain starts to increase from the firstpumping wavelength to reach the peak value at 1.56µm,
then it decreases exponentially tended to zero at 1.65µm.
Because of optical amplifiers and optical signals areoperated in the range 1.45µm to 1.65µm.
In this case we obtained, total bandwidth =120nm, where λ1t=1.5µm (for all amplifier units) and λ2t =1.62µm (for all
amplifier units).
2) The gain coefficient per unit watt:
The gain coefficient/unit watt, ∑ gi / Ai, m¯¹ W¯¹ against
wavelength is shown in Fig. 13 at different values of relativerefractive index difference.

335
335

Figure 14. Variation of total gain coefficient with wavelength.
TABLE 3.COMPARISON BETWEEN TWO MODEL
Model 1
number of amplifier
N=6
Case A

Model 1
number of amplifier
N=6
Case B

gmax

∆℅

gmax

∆℅

3.6239 × 10-

0.8
0.6
0.4

3.7553 × 10-

0.8
0.6
0.4

13

2.7179 × 1013

1.8119 × 10

BW
nm
110

13

2.8165 × 1013

-

BW
nm
120

1.8776 × 10-

1313

Model 2
number of amplifier
N=8
Case A

Model 2
number of amplifier
N=8
Case B

gmax

∆℅

gmax

∆℅

5.0734 × 10-

0.8
0.6
0.4

5.3468 × 10-

0.8
0.6
0.4

13

3.8050 × 1013

2.5367 × 10
13

-

BW
nm
120

13

4.0101 × 1013

BW
nm
120

2.6734 × 1013

Figure 13. Gain coefficient per unit watt against wavelength.
We note that the peak value at 1.56µm, and the zero value
at 1.65µm.
Depending on these results, we can say that there are many
parameters affect the gain coefficient per unit watt such as:
effective core area, relative refractive index difference,
pumping wavelengths and pumping powers.
3)

The total gain coefficient:Figure 14 displays the variation of the total gain coefficientwith wavelength. In this case, a bandwidth of 120nm is
obtained. By similar we note the total gain coefficient isstart to increase from the first pumping wavelength to reachto peak value at 1.56µm, the gain is start to decrease
exponentially tended to zero at 1.65µm. Gain in this case isaffected by pumping powers, effective core area and relativeindex difference. It is found that, when pumping powers
increase the total gain increases, so Raman amplifiers ispreferred to be used with high pumping powers.

From TABLE 3, we note that the gain of each unit of theamplifiers in case B is better than the gain in case A also,
the bandwidth in case B is larger than in case A for model 1
but for model 2 the bandwidth in case A is the same as incase B but the gain increases in case B. The maximum gainincreases with the relative refractive index difference
increase. And bandwidth changes according to the change ofthe position of optical amplifiers. Also from table 3 the gain
and bandwidth for model 2 is better than that of model 1,
therefore by increasing the cascaded units we can improve
the overall performance of the Raman amplifier.
V. CONCLUSIONS
By using N cascaded Raman amplifier units, N pumping
signals are injected in a parallel processing at different
pumping powers and wavelengths, we have obtained thefollowing simulation results:
1- The overall gain of Raman amplifier is increased due toputting the pumping wavelengths equal to the offset
wavelengths of the amplifier units.
2- We have obtained a bandwidth of about 110 nm and
120 nm at different value of the relative refractive index
difference for 6 cascaded units of optical Raman
amplifiers.
3- And also we have obtained a bandwidth of about 120
nm at different value of the relative refractive index
difference for 8 cascaded units of optical Raman
amplifiers.
4- The overall gain of the cascaded Raman amplifier
increases if the number of optical amplifier units and/or
the relative refractive index difference increases.
5- The bandwidth and/or the flatness of the gain depend on
the position of the amplifier units (wavelength value of
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the cascaded units) corresponding to each other's and on
the number of amplifier units.

References
[1]
M.
N.
Islam,"RamanAmplifiersforTelecommunications," IEEE J. Selected Topics inQuantum Electron., Vol. 8,No. 3, pp.548-559, 2002.
[2] M. D. Mermelstein, K. Brar, and C. Headly, "RIN
Transfer Measurement and Modeling in Dual-Order
Raman Fiber amplifiers," J. Lightwave Technol., Vol.
21, No. 6, p. 1518, 2003.
[3] J. Bromage, "Raman Amplification for Fiber
Communications Systems," J. Lightwave Technol.,Vol. 22, No. 1, pp. 79-93, 2004.
[4] C. Headley, G. P. Agrawal "Raman Amplification in
Fiber Optical Communication Systems", Elsevier Inc.
2005.
[5] P. Xiao. O. Zeng, J. Huang, and J. Liu, "A New Optimal
Algorithm for Multi-Pumping Sources of Distributed
Fiber Raman Amplifier," IEEE Photonics Technol.
Lett., Vol. 15, No.2, pp. 206-208, 2003.
[6] X. Liu and B. Lee, "A Fast Stable Method for Raman
Amplifier Propagation Equation," Optics Express, Vol.
11, No. 18, pp. 2163-2176, 2003.
[7] N. Kikuchi, "Novel In-Service Wavelength-Based
Upgrade Scheme for Fiber Raman Amplifier, "IEEEPhotonics Technol. Lett., Vol. 15, No. 1, pp. 27-29,
2003.
[8] Y. Cao and M. Raja, "Gain-Flattened Ultra-Wideband
Fiber Amplifiers," Opt. Eng., Vol. 42, No. 12, pp.
4447-4451, 2003.
[9] M. S. Kao and J. Wu, "Signal Light Amplification by
Stimulated Raman Scattering in an N-Channel WDM
Optical Communication System", J. Lightwave
Technol., Vol.7, No. 9, pp. 1290-1299, 1989.
[10] T. Nakashima, S. Seikai, N. Nakazawa, and Y. Negishi,"Theoretical Limit ofRepeaterSpacing in Optical Transmission Line Utilizing Raman
Amplification," J. Lightwave Technol., Vol. LT-4, No.
8, pp. 1267-1272, 1986.
[11] Y. Aoki, "Properties of Fiber Raman Amplifiers andTheir Applicability to Digital Optical Communication
Systems," J. Lightwave Technol., Vol. 6 No. 7, pp.
1227-1239, 1988.
[12] W. Jiang and P. Ye., "Crosstalk in Raman
Amplification for WDM Systems," J. Lightwave
Technol., Vol. 7, No. 9, pp. 1407-1411, 1989.
[13] A. A. Mohammed, "All Broadband Raman Amplifiersfor Long-Haul UW-WDM Optical Communication
Systems," Bulletin of Faculty of Electronic
Engineering, Menouf, 32951, Egypt, 2004.
[14] A. Yariv, Optical Electronics in Modern
Communications, 5th ed., Oxford Univ. Press, 1997.

336
336

Electronics and communication engineering in 2007 from
Arab Academy for Science and Technology & Maritime
Transport, College of Engineering and Technology,
Alexandria, Egypt. He is joined the PhD program in Mina
university in 2011. Her areas of interest include opticalcommunications, optical amplifiers.

Ashraf A.M. Khalaf: received his B.Sc. and
M.Sc. degrees in electrical engineering from Minia
university, Egypt, in 1989 and 1994 respectively. He
received his Ph.D in electrical engineering from Graduate
School of Natural Science and Technology, Kanazawa
university, Japan in 2000. He works at electronics andcommunications engineering Department, Minia University.
He is a member of IEEE since 12 years.

F. A. El-Geldawy: He is a professor inElectric Engineering Dept.,faculty of engineering, Minia,
Egypt.

Fathy M. Mustafa: received the B.Sc. degree
in Electronics and communications department
from the Faculty of Engineering, Fayoum
University, Fayoum, Egypt, in 2003. He iscurrently working a research assistant inElectronics and communications department at
Bani-suef University. He is earned the M.Sc degree in
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Optimum Stealthy Aircraft Detection
Using a Multistatic Radar
Hassan El-Kamchouchy, Khaled Saada, Alaa El-Din Sayed Hafez
Electrical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt
El-Kamchouchy@ieee.org, Khaledsaada@yahoo.com, Alaahafez@ieee.org
Abstract— Radar systems, based on Multistatic radar concept
attracted a substantial attention in the recent years. The paper
proposes system geometry for S-band Multistatic radar. This
technique is used for detecting and tracking the small cross section
area and stealthy aircrafts. The proposed geometrical structuresare studied with different radars spacing to extend the detection
coverage over the Monostatic radar used for air surveillance. The
radar detection coverage is also studied with all possible stealthy
aircraft paths to find the improvement achieved from using thistype of radar. The simulation is done using Matlab program. The
results show that the first system geometry with two transmittersand four receivers extends the detection coverage 80 Km for small
aircraft and 62 Km for stealthy targets. The second systemgeometry with two transmitters and six receivers extends thedetection coverage 85 Km for small targets and 69 Km for stealthytargets. The achieved SNR from these system geometries
guarantee a high probability of detection for small and stealthy
aircraft detection.
(Pt.9)Keyword— Multistatic radar, Stealthy Aircraft Detection

I. INTRODUCTION

M

ultistatic radar uses antennas at different locations fortransmission and reception. This means that thetransmitter and receiver are not co-located in the sameplace. There are different versions of Multistatic radar systemdoes not specify how far the transmitting and receiving sites
must be separated. In recent years, an extended growth ofactivities in the area of radar systems, based on the concept of
the Multistatic radar is reported from many research centers
and universities. Marc Brooker [1] presents the design andimplementation of a signal level simulator supporting a wide
variety of radar systems, focusing on Multistatic and netted
radars. The simulator places few limits on the simulated
system, and supports systems with arbitrary numbers ofreceivers, transmitters, and scatterers. Bezousek et. al. [2]Manuscript received February 12, 2013. This work was supported in part by
the Electrical Engineering Department, Alexandria University, Alexandria,
Egypt.
Hassan El-Kamchouchy, Professor of the Communications, Faculty ofEngineering, Alexandria University, Alexandria, Egypt.
(Email: El-Kamchouchy@ieee.org; Tel:+20-03-5627902)
Khaled Samir Saada, Ph.D. student, Alexandria University, Alexandria, Egypt.
(Email: Kaledsaada@yahoo.com; Tel:+20-0122-4516005)
Alaa El-Din Sayed Hafez, Affiliate Instructor, Faculty of Engineering,Alexandria University, Alexandria, Egypt.
( Email: Alaahafez@ieee.org; Tel:+20-0100-1525961)

described the Multistatic arrangement system with noncooperative transmitters and various aspects of signal
processing and signal parameters. Benson [3] investigatesmodeling method to optimize the location of receivers in orderto achieve maximum coverage of aircraft moving around Cape
Town International Airport. Several researches deal withimproving the detection coverage using Multistatic radarsystems [4-6]. These researches didn't study the radar coveragewith different system geometry and various radar spacing. The
paper proposes system geometry for S-band Multistatic radarfor detecting and tracking the small cross section area andstealthy aircrafts. The proposed geometrical structures arestudied with different radars spacing to extend the detection
coverage over the Monostatic radar used for air surveillance.
The radar detection coverage is also studied with all possible
stealthy aircraft paths to find the improvement achieved fromusing this type of radar. The first section of the paper
introduces theoretical description of the Multistatic radar. The
second section presents the simulation results and the findings
from this paper. The last section displays the conclusion of thepaper.
II. THEORETICAL BACKGROUND
A Multistatic radar system contains multiple spatiallydiverse Monostatic radar or bistatic radar components with ashared area of coverage. An important distinction system
based on these individual radar geometries is the added
requirement for some level of data fusion to take place
between component parts. The spatial diversity afforded byMultistatic systems allows for different aspects of a target tobe viewed simultaneously. The potential for information gain
can give rise to a number of advantages over conventional
systems. Multistatic radar is often referred to as ‘Multisite’ or‘netted’ radar. Fig. 1. shows the idea of using all available
sources of transmission for target localization and co-locatedreceiver module, [7]. An obvious advantage of separating thereceiver and the transmitter is that the receiver is passive anddifficult to locate and which doesn’t make it a target for e.g.
Anti Radiation Missiles (ARM). Personnel are safe from ARM
when located at the Rx. Separation also has effect on theeffectiveness of Electronic Counter Measures (ECM) since theTx and Rx are not co-located and therefore the Rx may beoutside the main-lobe of the jammer or even outside the LineOf Sight (LOS). Since the receiver is not readily discovered it
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is suitable for covert operations. Several receivers can operate
without disclose of their positions with the transmitter standoff by a large distance. Relations of Signal to Noise ratio (S/N)
will be discussed. The range equation for a bistatic radar isderived in a manner completely analogous to that for a
Monostatic radar. With this analog, the bistatic radar
maximum-range equation can be written as [8-9],(1)nS / N netted  i 1 j 1

PTi GTi GRj i2 tij FTi2 FRj2

4 3 KTs Bni RTi2 RRj2 LTi LRj(2)

Considering the simplest case where the radar parameters
for every transmitter-receiver combination are the same, theMultistatic radar equation can be simplified as,

P G G 2 F 2 F 2mnS / N netted  T T3 R t T R  21 2
4  KTs Bn LT LR i1 j 1 RTi RRj 
Rxref  Rref sinref 
Ryref  Rref cos ref

Ry 2  Ryref
Ry3  Ryref  2a
Rx3  Rxref

where RT, RR are the transmitter and reciever to target
range respectively, PT is the transmitted power, GT, GR are thetransmit and receive antenna power gain respectively, λ is thetransmitted signal wavelength, σt is the bistatic radar crosssection, FT, FR are the transmit and receive propagation factor
respectively, K is the Boltzmann constant, T s is the receive
system noise temperature, Bn is the noise bandwidth of thereceiver, S/N is the signal to noise ratio and LT, LR, are thetransmit and receive system losses. The Multistatic from theradar equation is developed to evaluate Multistatic radar
sensitivity properties. A fully coherent radar network is
considered, which means that the radars comprising the wholenetwork have a common and highly precise knowledge of timeand space. The whole radar network is composed of mtransmitters and n receivers. It is assumed that the wholenetwork is well synchronized and works cooperatively suchthat each receiver is capable of receiving echoes due to anytransmitters in the network. Under these assumptions, it isreasonable to calculate the overall radar sensitivity bysumming up the partial signal to noise ratio, which is given by,[10].
m

stations is constant for the system geometry. The coordinate
system must be converted for each radar station according to
the following equations,

Rx 2  Rxref  a

1

2
2 2

2
RT RR max   P3T GT GR   t FT FR  4  KTs Bn S / N min LT LR 

338

(3)

From this equation it is clear to see that the Multistaticradar geometry, i.e. the positions of target and radar in the
network, will have great influence on the overall Multistaticradar sensitivity.
III. THE PROPOSED SYSTEM GEOMETRY
The proposed system geometries are demonstrated in Fig.
2. The first system geometry G1 consists of two transmittersand four receivers. The second system geometry G2 consists of
two transmitters and six receivers. The spacing between radar

Ry 4  Ryref  2a
Rx 4  Rxref  a
Ry5  Ryref  a
Rx5  Rxref  a
Ry 6  Ryref  a

Rx6  Rxref  a
Ryx1  Ryref  a
Rxx1  Rxref
Ryx 2  Ryref  a
Rxx 2  Rxref  a

IV. SIMULATION RESULTS
The system geometries are simulated under Matlab
program environment to obtain the radar sensitivity under the
proposed conditions, and demonstrate all possible radar
spacing to achieve an optimum radar arrangement satisfying
the optimum detection. The radar transmitter parameters areillustrated in table 1. Multistatic radar system geometry G1and G2 are simulated with radar spacing from 5 Km to 70 Km
for aircraft range from 50 Km to 140 Km. It is found that the
optimum radar spacing for the system geometry G1 is 48 Km
and 50 Km for G2. Fig. 3. shows the received S/N at different
radar spacing and target ranges for system geometry G1withsmall aircrafts (σ = 0.1 m2), and stealthy aircrafts (σ = 0.025
m2). A comparison between Multistatic radar system geometry
G1 and Monostatic radar with small cross section isinvestigated in Fig. 4. Same comparative study is demonstrated
in Fig. 5. for stealthy aircrafts. Fig. 6. shows the received S/Nat different radar spacing and target ranges for systemgeometry G2 with small aircrafts (σ = 0.1 m2), and stealthy
aircrafts (σ = 0.025 m2). Received S/N at different azimuth
angles and target ranges for system geometry G2 isdemonstrated in Fig. 7. The results are concluded in Fig. 8.

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The system geometry G1 extends the detection coverage 80
Km for small aircraft and 62 Km for a stealthy target over the
Monostatic radar. The second system geometry G2 improves
the detection coverage for the small aircrafts by 85 Km and 69
Km for a stealthy aircrafts.
TABLE I

Radar Transmitter Parameters
ParameterValue

PT (Kwatt)
GT, GR (dB)
F (MHz)Bn (MHz)
FT, FRLT, LR (dB)

200
33
2450
1
1
5

(a)

(b)
Fig. 1. The combined use of available sources in the process of producing the
air picture

V. CONCLUSION
The paper proposes two Multistatic radar systemgeometries for S-band radar to improve the detection coverageof the small and stealthy aircrafts. The first system geometry
G1 consists of 4 transmitters and two receivers while thesecond one consists of six transmitters and two receivers. The
optimum radar spacing for G1 is 48 Km and 50 Km for systemgeometry G2. The simulation results show that systemgeometry G1 detection coverage extended 80 Km for small
aircrafts over detected by Monostatic radar and 62 Km forstealthy aircrafts. System geometry G2 extends the detection
coverage 85 Km with small aircrafts and 69 Km for stealthy
aircrafts. The results show that the extended range from thefirst system geometry satisfies the need for the small andstealthy aircrafts detection with economic number of radar
stations.

Fig. 2. Multistatic Radar System Geometry (a) System Geometry G1 and (b)System Geometry G2

REFERENCES[1] Marc Brooker, " The Design and Implementation of a Simulator for
Multistatic Radar Systems,"PHD Thesis, University of Cape Town, June,
2008.
[2] Pavel Bezousek, Vladimir Schejbal," Bistatic and Multistatic Radar
Systems," Radioengineering, Vol. 17, No. 3, Sep. 2008.
[3] Benson Chan, " Receiver Site Optimisation for Passive Coherent Location(PCL) Radar System,"Graduation Project, University of Cape Town, Oct.,
2008.
[4] Vladimír S., Dušan Č., Zdeněk N., Jan P., Jiří K., Pavel B., Ondřej F.,"
Multipath Propagation of UWB Through-Wall Radar and EMC
Phenomena," Radioengineering, Vol. 15, No. 4, Dec. 2006.
[5] Pavel Bezoušek, Marek Pola, Jan Pidanič," Detection of Multiple Targets
by a Multistatic Radar," Number 4., Volume IV., Dec. 2009.
[6] Steven Kay," Waveform Design for Multistatic Radar Detection," Dept. ofElectrical, Computer, and Biomedical Engineering, University of Rhode
Island Kingston, RI 02881, 2007.
[7] Terje Johnsen and Karl Erik Olsen," Bi- and Multistatic Radar," Advanced
Radar Signal and Data Processing Educational Notes RTO-EN-SET-086,
Paper 4. Neuilly-sur-Seine, France: RTO. Available from:http://www.rto.nato.int/abstracts.asp, visited Oct. 2010[8] Merril I. Skolnik," Radar Handbook, Second Edition," McGraw-Hill, 1990
[9] Bassem R. Mahafza," Radar Systems Analysis and Design Using
MATLAB," CRC Press, USA, 2000
[10] Chris Baker, "Multistatic radar processing and systems," Advanced Radar
Signal and Data Processing Educational Notes RTO-EN-SET-133, Paper 4.
Neuilly-sur-Seine, France: RTO. Available from:http://www.rto.nato.int/abstracts.asp, visited Oct. 2010

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(a)

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340

(b)

Fig. 3. Received S/N at different radar spacing and target ranges for system geometry G1 (a) Small Aircrafts σ = 0.1 m 2, (b) Stealthy Aircrafts σ = 0.025 m2.

(a)

(b)

Fig. 4. Received S/N at different azimuth angels and target ranges with small aircrafts σ = 0.1 m2 (a) Multistatic radar G1, (b) Monostatic radar

(a)

(b)

Fig. 5. Received S/N at different azimuth angels and target ranges with stealthy aircrafts σ=0.025m 2 (a) Multistatic radar G1, (b) Monostatic radar

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(b)

Fig. 6. Received S/N at different radar spacing and target range for system geometry G2 (a) Small Aircrafts σ = 0.1 m 2, (b) Stealthy Aircrafts σ = 0.025 m2.

(a)

(b)

Fig. 7. Received S/N at different azimuth angels and target ranges G2 (a) Small Aircrafts σ = 0.1m2 , (b) Stealthy Aircrafts σ = 0.025 m2.

Fig. 8. Received S/N versus target range for Multistatic radar G1, G2, and Monostatic radar with target cross section σ = 0.1 m2 and σ = 0.025 m

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Biographies
Hassan El-Kamchouchy is professor in Faulty ofEngineering, Alexandria University, Alexandria,
Egypt. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering,Alexandria University, He also holds M.Sc. and Ph.D.
in Electrical Engineering from Faculty of Engineering,Alexandria University. He received many technicalcourses in Electronic system design and Implementation, work as
System Engineer for more than 20 years, teach up to 30 undergraduate
subjects, supervising more than 75 thesis, publish more than 150
papers in different international conferences and Journals

Alaa El-Din Sayed Hafez is an affiliate instructorin Faulty of Engineering, Alexandria University,
Alexandria, Egypt. He holds B.Sc. in Electronics
and Communications from Faculty of Engineering,Alexandria University, M.Sc. in Electronics andCommunication from Arab Academy for Scienceand Technology and Maritime Transport,
Alexandria. He also holds M.Sc. and Ph.D. inElectrical Engineering from Faculty of Engineering, Alexandria
University. He received many technical courses in Surveillance Radar
design and Implementation, work as Radar System Engineer for morethan 5 years, teach up to 20 undergraduate subjects, supervising morethan 12 thesis, publish more than 40 papers in different international
conferences and journals.

Khaled Samir Saada is a Post Graduate Studen(Ph.D.), Alexandria University, Alexandria, Egypt.
He holds B.Sc. in Electronics and Communications
from Faculty of Engineering, Alexandria University,
M.Sc. in Electrical Engineering from Faculty ofEngineering, Alexandria University. He received
many technical courses in electronic engineering
design and Implementation.

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Dynamic Physical Impairment-Aware Routing andWavelength Assignment in 10/40/100 Gbps
Mixed Line Rate Optical Networks
Filippos BALASIS *, Xin WANG *, Sugang XU *, and Yoshiaki TANAKA *, *** Global Information and Telecommunication Institute, Waseda University, Japan** Research Institute for Science and Engineering, Waseda University, Japanfbalasis@akane.waseda.jp, xinwang@ruri.waseda.jp, xusugang@m.ieice.org, ytanaka@waseda.jp
Abstract—The growing global Internet traffic will inevitably
lead to a serious upgrade of the current optical networks'
capacity. Instead of upgrading the data rate of each wavelengthin every fibre link across the entire optical WDM network
infrastructure, it is more efficient and cost-effective to support
different data rates within one fibre link (e.g., 10, 40 and 100Gbps). This is called optical network with mixed line rates(MLR). Moving to higher than 10Gbps data rates that can beused within the same fibre requires the implementation of phasemodulation schemes.
Nevertheless, the co-existing OOK
channels cause a critical physical impairment to the phasemodulated channels, namely cross-phase modulation (XPM), that
limits the network's performance. In order to mitigate this typeof impairment a more sophisticated physical impairment awarerouting and wavelength assignment scheme needs to be adopted.
In this paper, the critical impairment for each data rate and theway it affects quality of transmission (QoT) is presented.
Secondly, QoT aware RWA schemes for a MLR optical network
are presented and evaluated in terms of performance throughsimulations.
Keywords—Mixed Line Rate, Optical Network, XPM, ASE,
Physical Impairment, RWA

I. INTRODUCTION
The emergence and establishment of bandwidth intensive
applications such as IPTV, cloud computing, has led to an
explosive growth of the heterogeneous internet traffic. Manytechnologies have been proposed as candidates for the
upgrade of the network's capacity and the greater utilisation of
optical fibre's limited bandwidth. However, the concept ofManuscript received July 10, 2013.
Filippos BALASIS is with the Global Information and Telecommunication
Institute, Waseda University, Tokyo, 169-0051, Japan (Tel: +81 3 3203 9450;
fax: +81 3 3203 9450; e-mail: fbalasis@akane.waseda.jp).
Xin WANG is with the Global Information and Telecommunication
Institute, Waseda University, Tokyo, 169-0051, Japan (Tel: +81 3 3203 9450;
fax: +81 3 3203 9450; e-mail: xinwang@ruri.waseda.jp).
Sugang XU is with the Global Information and Telecommunication
Institute, Waseda University, Tokyo, 169-0051, Japan. (Tel: +81 3 3203 9450;
fax: +81 3 3203 9450; e-mail: xusugang@m.ieice.org).
Yoshiaki TANAKA is with the Global Information andTelecommunication Institute, Waseda University, Tokyo, 169-0051 Japan.
He is also with the Research Institute for Science and Engineering, Waseda
University, Tokyo, 162-0044 Japan (Tel: +81 3 3209 5210; fax: +81 3 3209
5210; e-mail: ytanaka@waseda.jp).

Fig. 1. A wavelength switched optical network with mixed line rates andmultiple modulation formats.

MLR optical network has recently drawn the academic
community's attention due to its cost and energy efficiency[1]-[3]. Other advantages of mixing different rates within thesame fibre link are the more gradual and cost-effective
upgrade of the capacity and the fact that it is possible to apply
into the current WDM network infrastructure without many
modifications [4]. Some early experiments from Nokia
Siemens and Alcatel [5],[6] have shown the industry's interest
as well.
Until recently, the highest data rate available in awavelength channel was 10 Gbps achieved through on-off
keying (OOK) modulation format within the standard ITU
grid of 50 GHz. In order to migrate to higher data rates withthe same spectral separation more advanced and spectral
efficient phase modulation schemes are needed. The mostpromising candidates, in terms of noise resiliency, for 40 Gbps
and 100 Gbps transmission have been DQPSK and PM-QPSK
respectively and are the ones that are considered in our study.
Fig. 1 depicts such an optical network with mixed line rate
and multiple modulation formats. Nevertheless, the biggest
obstacle for the realisation of a MLR optical network is XPM
occurred to the xPSK channels by the adjacent 10 Gbps OOK
channels. This non-linear impairment is caused by the Kerr
effect [7]. The high optical intensity of the propagated OOK
pulses generates oscillations in the refractive index of the

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fibre's material. The changes of the refractive index produce a
non-linear phase shift to the adjacent xPSK channels and thusincrease the bit error rate at the receiver. In order to overcome
this impairment that affects performance a more sophisticatedphysical impairment (PI) aware routing and wavelength
assignment (RWA) scheme that ensures quality oftransmission (QoT) is needed.
The challenge of developing a PI-aware for such non-linear
impairment is not only the more complex calculation of theaccumulated phase noise along the candidate path but also thecalculation of the distortion caused to the already establishedlightpaths. To the knowledge of this paper's authors, with the
exception of [8] and [9], there are not many proposals in theliterature for XPM-aware provisioning of MLR optical
networks. In [8], the authors consider a design of such a
network in the static scenario only and they propose analgorithm that adapts the optical reach of 40Gbps channels
whenever they co-propagate with 10Gbps ones. However,they apply a fixed parameter to modify the length of a fibre
link regardless of the number of co-existing 10Gbps channels
or their spectral separation with the 40Gbps ones and thus,
they do not accurately estimate the XPM impairment. In [9],the authors consider a MLR optical network where eachlightpath is set up dynamically and its QoT metrics arecalculated based on analytical models. They also consider the
worst case scenario for XPM impairment whenever a xPSK
lightpath is established and the use of guard bands between
xPSK and OOK channels. However, the overestimation of
XPM impairment can cause unnecessary blocking of xPSK
lightpath requests and the use of guard bands may lead toinefficient use of wavelength resources in order to secure QoT
for xPSK connections.
In this paper, we study the case of dynamically set up
lightpaths in a MLR optical network realised by transceivers
that can be either tuned or not on a fixed data rate andmodulation format. For that case, we present and compare
two novel PI-aware RWA schemes that take into account thepresence of XPM impairment and optimises performance andresource utilisation. The rest of this paper is organized asfollows. In section II, we describe the types of impairment
and their calculation method for OOK, DQPSK and PMQPSK channels respectively. In section III, we present theconcepts behind the proposed algorithms. In section IV, ourmethods are compared to the shortest path and minimum hop
routing schemes. For the comparison, the case study via
simulation and its results are shown. Finally, section V
concludes this paper.
II. PHYSICAL IMPAIRMENTS IN OOK, DQPSK AND PM-QPSKWAVELENGTH CHANNELS
A. OOK
In optical transmission that uses OOK scheme amplified
spontaneous emission noise (ASE) and chromatic dispersion(CD) are the most critical impairments. Since CD can be

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managed through dispersion compensation fibres (DCF), ourstudy focuses on ASE noise only. For future work however,
we intend to include other types of impairment as well, suchas polarization mode dispersion (PMD) and switching
crosstalk. ASE noise is due to the spontaneously emitted
photons by the fibre's material. This radiation cannot be
distinguished by the optical amplifier and therefore is
amplified along with the propagating optical pulse and added
as noise to the signal [7]. If erbium-doped fibre amplifiers(EDFA) are considered then the ASE noise power at theoutput of the amplifier is calculated as Eq.(1) [7]:PASE = 2nsphfo(G- 1)Bo(1)

where nsp is the spontaneous emission factor, h the Planck
constant, fo the frequency of the propagating signal, G the gainof the amplifier and Bo the optical bandwidth of the receiver.
In the case of distributed amplification across the fibre links,
then after Ni spans in a fibre link i the total accumulated ASE
noise power is Eq.(2) [7]:PASE,i = 2nsphfoNi(G- 1)Bo , Ni=Li / ls(2)where Li is the length of the link i and ls the length of the span.
The optical SNR is calculated as Eq.(3) [7]:(3)where Pin is the average power of the optical signal that due tothe distributed amplifing is considered constant. From the
SNRo it is possible to calculate the BER of the OOK signals
by the following Eqs.(4) and (5) [7]:(5)where M=2BoT, Bo=50GHz the optical bandwidth of thereceiver and T the symbol duration (100ps for OOK channels).
B. DQPSK
The calculation of impairment for the DQPSK channels is
more rigorous since, in addition to the XPM, ASE noise also
causes a non-linear phase shift to the propagating signal.
Moreover, the XPM impairment induced in fibre link i by anadjacent OOK channel j depends on its power level andspectral distance. The variance of non linear phase noisecaused by XPM is shown as Eq.(6) – Eq.(8) [9]:
(6)
,

=

(7)

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Technology(TACT)(8)
where αi , Di and γi are the attenuation, dispersion parameter,
and nonlinear Kerr coefficient of fibre, respectively. Tj, Δλj
and Pi,j are the bit-time of the interfering OOK channel, its
spectral separation with respect to the DQPSK channel, and its
average power at the input of the fibre, respectively.
The total variance of the non-linear phase noise in a link i
and a route r can be calculated with Eq.(9), (10) [9] [10]:(9)(10)
where the parameter μj = 0, 1 indicates whether the OOK
channel j is active or not, and n is the number of the
wavelength channels of the link with OOK modulation.
The phase shift variance caused by ASE noise is calculated
as Eqs.(11) and (12) [10]:,ρ=n∙BREFTSNRo

(11)
(12)

where ρ is the SNR per symbol, n=2, BREF is the reference
bandwidth in which OSNR is measured (12.5 GHz) and T is
the symbol duration (50 ps for 40 Gbps DQPSK channels).
Finally, the BER due to phase noise for a DQPSK connection
is calculated as Eqs.(13) and (14) [9],[10]:

∙

(13)
(14)

where is Ik(x) the k-order modified Bessel function of the firstkind. The BER formula can be calculated with very good
approximation within a finite number of terms.

C. PM-QPSKIn PM-QPSK modulation scheme, each wavelength channel
carries two orthogonally polarised QPSK modulated bit
streams. Therefore, in this case the QoT model is similar tothat of DQPSK. However, there are some differences thatneed to be taken into account. First of all, since there are twopolarisations of the signal, Eq.(13) will change into Eq.(15)[9]:ρ=n'∙ REFT'SNRo

(15)

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Additionally, in PM-QPSK transmission, not differential
but coherent detection technique is used instead in the receiver.
The phase estimation algorithm takes into account K
previously received symbols and this affects the method theXPM phase noise variance is calculated [9]. Particularly,Eq.(6) is modified as shown in Eq.(16) [9]:(16)
where K is the number of past symbols. K=6 is assumed for
optimal phase detection [10]. Finally, the BER for QPSK
modulation is calculated as Eq.(17) [9], [10] :

∙

(17)

III. QOT-AWARE PROVISIONING OF THE LIGHTPATHS
A. Type of Transponders and Wavelength Selection Strategy
As shown in Eqs.(6)-(8), the XPM induced phase shiftdepends mainly on the spectral distance and power levels of
the adjacent OOK channels. Due to the complexity ofdefining the optimal power level, especially in a dynamic
scenario, power management is left for future work. Instead,this paper is mainly focusing on provisioning the lightpaths insuch way so that the xPSK channels have the maximum as
possible spectral separation from the OOK channels. Thisrequires a specific placement of the channels in the fibre's
spectrum and a particular wavelength selection scheme aswell. In addition to the wavelength selection strategy that will
prevent the xPSK channels to entangle with the OOK channels
in the fibre's spectrum, another factor that should be taken into
account when planning MLR optical networks is the type ofthe used transponders. The transponders can be either fixed,
namely, tuned on a specific wavelength and bit rate, or fully
tunable where the wavelength, the bit rate and the modulation
format can be configured dynamically on request. A tunableoptical transponder has the obvious advantage of greater
flexibility and efficiency in the usage of wavelength resources
and thus, for the same traffic demands, a network with tunabletransponders is expected to perform better than a network with
fixed ones. In Fig. 2 the architecture of an optical add-drop
multiplexer (OADM) node is shown where the opticaltransponders are assumed to be tunable.
Although multirate transponders have become recently
available, the range of the variable bit rate is still limited andis achieved through the same modulation scheme. An opticaltransceiver with adaptive modulation format is needed inwhere n'=1 and T'=40 ps (because the symbol rate is equal to25 Gbaud/sec).

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Fig. 2. A node with an optical add-drop multiplexer (OADM) and tunableoptical transponders.

Fig. 3. Tuning of the fixed rate optical transponders in the fibre's spectrum
according to their data rate.

order to scale from 10Gbps up to 100Gbps because OOK
modulation is still preferred for lower data rates due to its
longer optical reach and lower energy consumption [3],[11].
Currently, there are no transponders commercially available
that employ different modulation formats and when they get
released it is almost certain that the implementation cost will
be high. Furthermore, in the case of a MLR optical network
the probability of xPSK channels intermingling with OOK
ones is higher and hence some wavelength channels need to
be inactive in order to guarantee QoT for 40G and 100G
transmission. In other words, the complexity of the QoTmodel increases. On the other hand, fixed rate transponders,
are more affordable and more realistic in terms of
implementation. In addition to their cost effectiveness, theproblem of optimising the provisioning of the lightpaths is
simplified. For the sake of a more complete research scope,
both scenarios of a MLR network with tunable and fixed ratetransponders are examined separately. Moreover, in the casewhere fixed rate transponders are considered, various
combinations of data rates are examined.
Either way, a wavelength selection strategy that considers
the XPM physical impairment can be proven quite beneficial
to network's performance. In the case of a MLR network withfully tunable transponders, the first-fit (FF) for xPSK
lightpaths and last-fit (LF) for OOK ones becomes the obvious
optimal choice. This is so as to maximise the spectral
separation among these two types of lightpaths. Furthermore,when fixed rate transponders are only available, in addition to
the FF-LF strategy, a specific tuning of the transceivers is
required.
Particularly, the optimal placement of the
wavelength channels in the fibre's spectrum is shown in Fig. 3,
where the channels of the same data rate (or modulation
scheme) are grouped in line. Whether there can be a fixed
number of inactive wavelengths between the xPSK and the
OOK channels or not, is left to the operators' judgement which
will based on real world measurements. In this paper
however, no guard-band is considered for the purpose of
maximising spectral efficiency (e.g., the possibility of an
xPSK connection next to an OOK one satisfying the BER
threshold due to link's short distance). Besides the wavelength

selection strategy and the tuning of the transceivers, as it ispresented in the following part of this section, for the case ofxPSK lightpaths, knowing in advance which wavelength isgoing to be assigned is required for the choice of the optimal
path. This is why in our proposed method, when a connection
request arrives, the FF wavelength is assigned first and thenthe route with the minimum XPM impairment is calculated.
B. QoT Estimation Model
In a MLR optical network, the provisioning of thelightpaths requires different treatment for OOK and xPSK
requests respectively. For the 10 Gbps connections, the pathwith the shortest length or minimum number of hops remains
the optimal choice, since ASE noise is the main impairment.
Nevertheless, this cannot be applied to the xPSK lightpaths
since besides ASE noise, XPM is a nonlinear impairment that
depends on the number and spectral distance of the alreadyexisting 10Gbps lightpaths and must also be taken into
account. What is more, when searching an optimal route for
an OOK lightpath, it should be investigated whether its set up
can lead to a serious XPM impairment to the alreadyestablished xPSK lightpaths. Therefore, an efficient RWA
scheme should be able to adapt on the data rate of an
incoming connection request. Next, the routing methods for
xPSK and OOK lightpaths are separately described and thenthe PI-aware schemes are presented in steps.
DQPSK and PM-QPSK Lightpaths
Regarding the xPSK lightpaths (40Gbps and 100Gbps
connections), a physical impairment-aware routing andwavelength assignment algorithm is proposed to calculate thepath that offers the minimum BER for a source-destination
pair. To achieve this, to every link, a weight is assigned that
equals to the phase noise variance of that link. Since thephase variances per link can be added linearly, as shown inEqs.(9) and (14), it is possible, by finding the "shortest" pathin terms of weights, to ensure that the least OOK congested
links will be selected and that this path will offer low BER.
At this point, it should be noted that, besides the XPM noise
variance, the ASE induced noise variance can also be added

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linearly per link. If the distributed amplification through
EDFA modules is considered, then for a link i the ASE phasenoise variance is calculated as shown in Eq.(18):1/(n∙ REFTSNRo)= PASE,i/n∙ REFT∙ Pin

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of matrix Ai and vector CASE,i can be pre-computed andconsidered constants while the elements of vector M, thatshow which OOK wavelengths are currently being used in thelink, are the only variables that change in time.

(18)

where PASE,i is the ASE noise in link i. And as it is shown inEq.(3) the ASE noise can be added linearly in every route.
Therefore, we can define as weight Wi for every link i its total
phase noise variance, Eq.(19):(19)
The benefit of this method compared to other XPM-aware
RWA schemes is that it is less XPM-biased and more accurate
in the estimation of QoT. To be more specific, if the
avoidance of links with established OOK lightpaths is the only
criterion in the lightpath provisioning, then this may lead to a
miscalculation of the optimal path since the ASE induced
phase noise of a much longer route can outweigh the XPM
impairment of other candidate paths.
Furthermore, assuming distributed amplification across the
network, we can consider the power of the optical signal to bestable. Therefore, the only parameter that changes in Eq.(6)(8) is the one of spectral separation which has a unit of 50
GHz (ITU grid) [4]-[6]. When fixed rate transponders areused in the network the number and type of wavelengths(OOK or xPSK) supported in every link are known in advance.
Therefore, it is possible for a specific xPSK wavelength, topre-compute the XPM phase noise variance caused by every
OOK wavelength independently (e.g. applying in Eq.(6) OOK
wavelength j, j+1, j+2...). Then, by knowing which OOK
wavelengths are currently being used in a link, the weight ofthat link is calculated by Eq.(10). This simplifies theprocedure of finding the optimal path for an xPSK connection
as well as the calculation of the distortion caused by a numberof OOK lightpaths. Assuming that in a fibre link i there are m
and n wavelengths carrying xPSK and OOK signals
respectively and arranged as in Fig. 3, it is possible to express
the above concept with the following formula Eq.(20):

+

(20)

where Wi is the 1xm row vector containing the weights of all
xPSK wavelengths for link i, Ai is the mxn matrix that
contains the phase noise variances for every combined pairbetween a xPSK and an OOK channel in link i, (e.g. element
ak,l (1≤k≤m, 1≤l≤n) is equal to the variance of the non-linear
phase noise induced by OOK channel l to xPSK channel k,Eq.(9)), M is the row vector containing the μ parameter from
Eq.(10) for every OOK wavelength in the link and CASE,i therow vector that contains all ASE induced phase noise
variances for every xPSK wavelength in link i. The elements

Fig. 4. When an xPSK lightpath is about to be set up, weights are applied toeach link of the network's topology and their value depends on the number
and spectral adjacency of the established OOK lightpaths.

When fully tunable transponders are considered, thecomplexity for the calculation of the phase noise varianceincreases along with the possible combinations of wavelengths
in the available spectrum. Nevertheless, the same logic can beapplied to reduce online calculation time. Assuming that there
are n supported wavelengths in a fibre link, the number ofpossible XPM noise variances caused by one OOK lightpath isn-1. Again, by using Eq.(6) we can define the XPM noise
variance caused by an OOK lightpath that is j wavelengths
away as σ'j2, 1 ≤ j ≤ n-1. Hence, by pre-computing all possible
values of σ'j2 the total XPM noise variance in link i for a xPSK
lightpath that uses wavelength k is, Eq.(21) :(21)
where index ml={0,1} is 1 when wavelength l is used by an
OOK lightpath.
A simple example of the proposed scheme can be seen in
Fig. 4. When a request for a 10Gbps connection arrives
between nodes A and D, the optimal path is the one with theshortest length A→B→D. Once it is established and a new
100Gbps request arrives between the same nodes, weights will
be applied to the topology's links in order to find the optimalroute as described above. The presence of the alreadyestablished 10Gbps lightpath will result to larger weights forthe route A→B→D and therefore A→C→D route is opted.
OOK Lightpaths
Regarding the 10Gbps lightpaths that make use of OOK
scheme two methods can be applied: i) When a 10Gbps
connection request arrives, the wavelength is assigned first

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according to FF-LF strategy and then the shortest path isestablished. ii) A route is found through the k-shortest fixed
length path algorithm and then the wavelength is assigned.
The first method sets as a priority the protection of xPSK
lightpaths and the objective is to assign a wavelength to the
OOK lightpath that is as distant as possible from thewavelengths of xPSK lightpaths. However, assigning the
wavelength first might not lead to the shortest length orminimum hop route to the destination node. The second
method on the other hand is more conventional and more
beneficial to the QoT of OOK lightpaths but the risk of setting
an OOK lightpath closer to a xPSK one is higher.
As a result, two schemes can be derived from the above
analysis and their distinction lies on the way they handle the
10Gbps lightpaths: i) Minimum weight path for xPSK
lightpaths and first wavelength then shortest length path for
OOK lightpaths (MW-FW); ii) Minimum weight path for
xPSK lightpaths and first shortest length path then wavelength
assignment for OOK lihgtpaths (MW-FP). Both of thealgorithms are presented in steps as follows:
MW-FW Algorithm steps
Step 1: When a new connection request arrives, select awavelength according to the First-Fit/Last-Fit
strategy described above. If no available wavelength
is found then block the request;
Step 2: If a wavelength is available then select it as thecandidate wavelength, and find the optimal route forthis wavelength:1) For xPSK requests a modified Dijkstra algorithm
aforementioned is employed by setting a weightto each link, i.e., with the value of the phase noise
variance of that link.
2) For OOK requests standard Dijkstra algorithm forshortest path calculation is employed.
The links where the candidate wavelength is alreadyoccupied, are considered to have infinite
weight/length. If no route to destination is found,then go back to Step 1 to select another candidate
wavelength;
Step 3: If a route to destination is found then calculate the
BER. If it is below threshold:1) For xPSK requests establish the lightpath.
2) For OOK establish only if it does not violate theQoT of already existing xPSK lightpaths.
If the BER is not satisfied or there is violation to theexisting lightpaths then go to Step 1 to select anothercandidate wavelength;
Step 4: If a new OOK lightpath is established or released
update the XPM variances and BER of the affected
xPSK lightpaths.
MW-FP Algorithm steps

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Step 1: When a new connection request arrives:
If it is a xPSK lightpath request go to Step 2
else go to Step 3;
Step 2: Search for an available wavelength according to FirstFit strategy. If no wavelength is found block therequest else use the modified Dijkstra where theweight of each link equals the phase noise variance ofthat link; The links where the candidate wavelength isalready occupied, are considered to have infinite
weight/length. If no route to destination is found,then select the next available wavelength and repeat
Step 2 else go to Step 4;
Step 3: In the shortest length path to the destination search
for an available wavelength with Last-Fit strategy. If
no wavelength is found proceed to the next path in
the list of the k-shortest length paths and repeat theprocedure. If no free wavelength is found for all kshortest paths block the request, else go to Step 4;
Step 4: If a route to destination is found then calculate theBER:
1) For xPSK if it is below threshold establish thelightpath, else go to Step 2;2) For OOK if it is below threshold and does not violate
the QoT of already existing xPSK lightpaths establish
the lightpath, else go to Step 3;
Step 5: If a new OOK lightpath is established or released
update the XPM variances and BER of the affected
xPSK lightpaths.

IV. SIMULATION AND RESULTS
A. Optical Transponder Considerations
Depending on whether fixed wavelength-rate or fully
tunable transponders are implemented in the network, there isa difference in checking availability for a wavelength ortransponder when a connection request arrives. In the case of
fixed transponders, in order to set a lightpath with a specificwavelength and data rate, both concerned transponders in thesource and destination nodes should be available, i.e. in idle
state. Therefore, when a RWA algorithm is running, acandidate wavelength is considered available if the following
conditions are satisfied:i) the wavelength continuity
constraint along the route is not violated; ii) the transponders
that are tuned on that specific wavelength in sourcedestination nodes are not currently used by other lightpaths.
Hence, a connection request in a fixed transponder network
might be blocked because no transponder of that rate isavailable or there is no "matching" pair of transponders interms of wavelength and data rate between source anddestination nodes. In the case of fully tunable transponders
however, the latter limitations do not exist. Between any twotransponders in two different nodes, it is possible to establish alightpath at any data rate or wavelength as long as there is no
violation of wavelength continuity constraint or QoT threshold.
Thus, when a RWA scheme is searching for an available

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Parameter
Bo
Pin
ASE factor nsp
Amplifier gain G
Channel spacing
BER threshold

Value
50 GHz
1 mW
1.5
10 dB
50GHz
10-9

Parameter
Dispersion
parameter
Span length
Nonlinear Kerr
coefficient
Fiber attenuation

Value

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TABLE IPARAMETER CONFIGURATION EMPLOYED IN SIMULATIONS

17ps/nm/km
60 km
2.2(W∙m)-1
0.2db/km

wavelength it will start examining from one end of thespectrum (first in order for an xPSK lightpath and last in orderfor OOK one) and will stop when it finds a wavelength thatsatisfies the conditions for the lightpath establishment orreaches the other end of the spectrum and blocks theconnection request..

B. Simulation Conditions
To evaluate the proposed methods we compare them withthe shortest path and minimum hop routing schemes. All ofthem will use the same FF-LF wavelength selection strategy
and the QoT metrics presented in section II to define whether
the incoming request will be blocked or not. The simulations
use NSF network's topology with 14 nodes and 21
bidirectional links as depicted in Fig. 5. Every fibre linksupports 40 wavelengths. In the current study the length of
the network's links is downsized by a factor of 10, instead of
thousand kilometres per link, hundreds are considered in thesimulations. As mentioned in Section III-A both scenarios ofa MLR network with fixed rate as well as tunabletransponders are examined separately. For a network that uses
fixed rate transponders four different combinations areinvestigated: i) 20 OOK 10Gbps — 20 DQPSK 40Gbps
wavelength channels in every link (Fig. 6) ii) 20 OOK
10Gbps — 20 PM-QPSK 100Gbps wavelengths (Fig. 7) iii)
20 OOK 10 Gbps — 10 DQPSK 40Gbps — 10 PM-QPSK
100Gbps wavelengths (Fig. 8) and iv) 14 OOK 10 Gbps — 13
DQPSK 40Gbps — 13 PM-QPSK 100Gbps wavelengths (Fig.
9). In any case, the transponders are assumed to be tuned as
explained in Section II-A (Fig. 3). Moreover, the results for
the MLR network with tunable transponders (scenario v) are
shown in Fig. 10.
For all scenarios the number oftransponders in every node is assumed to be 40. The lightpath
requests arrive in Poisson process and are uniformly
distributed among all source-destination pairs and theavailable data rates. That means in the scenarios i) and ii) the
probabilities of a 10Gbps lightpath request and a 40/100 Gbps
request, P10G P40G and P100G respectively, are the same andequal to 0.5. Similarly, in the scenario iii) it is P40G=
P100G=0.25 and P10G=0.5 and in scenario iv) it is P40G= P100G=P10G≈0.33. The probability condition of requests of scenarioiv) are also applied to the scenario v) where tunabletransponders are assumed. The holding time of the lightpaths
is exponentially distributed.
Other parameters of thesimulations are shown in Table 1.

Fig. 5. NSF network topology used in the simulations.

Fig. 6. Blocking Probability in the 10-40 Gbps scenario i).

Fig. 7. Blocking probability in the 10-100 Gbps scenario ii).

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Technology(TACT)Fig. 8. Blocking probability in the 10-40-100 Gbps scenario iii).

Fig. 9. Blocking probability in the 10-40-100 Gbps scenario iv).

Fig. 10. Blocking probability in the 10-40-100 Gbps scenario v) with tunabletransponders.

C. Simulation Results
The blocking probability performances are plotted in Figs.
6 - 10. The figures show that when networks with fixed ratetransponders are considered the proposed methods offers
superior performance, especially under moderate traffic load.

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When the traffic load increases the advantage of the proposed
scheme over the compared methods diminishes. This isprobably due to the fact that along as the traffic is not large,
our proposed schemes assign the lightpaths by efficiently
utilizing the network's available wavelengths. While underheavy traffic load, the links are heavily congested and thus
XPM noise and wavelength unavailability become
unavoidable. The highest blocking probability is observed inFig. 7. That is expected since 100 Gbps lightpaths are more
vulnerable to XPM and ASE noise than the 40 Gbps lightpaths
in scenario i). This can be seen by their BER formulas(Eq.(13) and (17)). Moreover, the results in Figs. 6 and 8 aresimilar because the wavelength channels placed in the first 10
places of the spectrum (10 number of 40Gbps channels in
scenario i) and 10 number of 100Gbps channels in scenario iii)
are fairly protected from the OOK channels (placed 21-40)
since they are separated by at least 10 wavelengths. Therefore,
the main reason of blocking for these wavelengths (places 110) in both scenarios is ASE noise and wavelength
unavailability. In total, the proposed schemes are proven to be
more beneficial in terms of performance in scenarios i)‒iii).
In scenario iv) (as shown in Fig. 9) the benefits are minor andthis can be due to the fact that OOK wavelengths are only 33%of the available resources and thus, the network resembles one
that has only xPSK wavelengths and no XPM noise. In thelast scenario (as shown in Fig. 10) where any wavelength
supports any data rate the conventional shortest length pathand minimum hop routing schemes seem to scale better thanMV-FW scheme. This is could be interpreted by the fact thata network with fully tunable transponders provides greater
flexibility in finding an available wavelength for a specificroute and subsequently the blocking probability is lower thanthe previous scenarios.
Finally, it is also shown that the MV-FP scheme is the mostreliable in terms of performance. This can be attributed tohaving a "first shortest route then last-fit wavelength" for 10
Gbps lightpaths combined with a good impairment aware
strategy for 40-100Gbps lightpaths which leads to a more
efficient provisioning of the connections.

V. CONCLUSION
In this paper, two dynamic PI-Aware RWA schemes for
MLR optical networks were presented and compared. A
modified Dijkstra algorithm was introduced for 40-100 Gbps
lightpaths that mitigates the XPM impairment by adjacent
OOK channels and outperforms conventional shortest pathand minimum hop routing schemes. It is shown that, in thecase of a MLR optical network, QoT based provisioning of thelightpaths can have a strong impact on the network's
performance. For future work, the proposed schemes can beextended to include more types of impairment as well as
different scenarios of operation such as dynamic power
management and implementation of 3R regeneration.

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REFERENCES
A. Nag, M. Tornatore, and B. Mukherjee, “Optical network design with
mixed line rates and multiple modulation formats,” J. Lightwave
Technology, vol.28, no.4, pp.466-475, Feb. 2010.
[2] M. Batayneh, D. A. Schupke, M. Hoffmann, A. Kirstaedter, and B.
Mukherjee, “Optical network design for a multiline-rate carrier-grade
ethernet under transmission-range constraints,” J. Lightwave Technology,
vol.26, no. 1, pp.121–130, Jan. 2008.
[3] P. Chowdhury, M. Tornatore, A. Nag, E. Ip, T. Wang and B. Mukherjee,“On the design of energy-efficient mixed-line-rate (MLR) optical
networks,” J. Lightwave Technology, vol.30, no.1, pp.130-139, Jan.
2012.
[4] T. Wuth, M.W. Chbat and V.F. Kamalov, “Multi-rate (10G/40G/100G)
transport over deployed optical networks,” Optical Fiber
Communication / National Fiber Optic Engineers Conf. 2008, (OFC
2008/NFOEC 2008), San Diego, USA, no.NTuB3, Feb. 2008.
[5] J.-P. Faure, B. Lavigne, C. Bresson, O. Bertran-Pardo, A. C. Colomer,
and R. Canto, “40G and 100G deployment on 10G infrastructure:market overview and trends, coherent versus conventional technology,”
Optical Fiber Communication / National Fiber Optic Engineers Conf.
2010 (OFC 2010/NFOEC 2010), San Diego, USA, no.OThE3, March
2010.
[6] T. J. Xia, et al, “Multi-rate (111-Gb/s, 2x43-Gb/s, and 8x10.7-Gb/s)transmission at 50- GHz channel spacing over 1040-km field-deployedfiber,” 34th European Conf. Optical Communication, Brussels, Belgium,
no.Th.2.E.2, Sept. 2008.
[7] G. Agrawal, Fiber-Optic Communication Systems, 4th Ed., John Wiley& Sons, Hoboken, New Jersey, USA, 2010.
[8] K. Christodoulopoulos, K. Manousakis and Emmanouel Varvarigos,“Reach adapting algorithms for mixed line rate WDM transport
networks,” J. Lightwave Technology, vol.29, no.21, pp.3350-3363,
Nov. 2011.
[9] N. Sambo, M. Secondini, F. Cugini, G. Bottari, P. Iovanna, “Modelingand distributed provisioning in 10–40–100-Gb/s multirate wavelengthswitched optical networks,” J. Lightwave Technology, vol.29, no.9,
pp.1248-1257, May 2011.
[10] A. Bononi and M. Bertolini, “Cross-phase modulation induced by OOK
channels on higher-rate DQPSK and coherent QPSK channels,” J.
Lightwave Technology, vol.27, no.18, pp.3974-3983, Sept. 2009.
[11] F. Idzikowski, “Power consumption of network elements in IP over
WDM networks,” Telecommun. Netw. Group (TKN), TU Berlin, Berlin,
Germany, TKN Tech. Rep. TKN-09-006, July 2009.
[1]

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China, in 1994 and 1997, respectively, and Ph.D.
degree in information and communication engineering
at the University of Tokyo, Tokyo, Japan, in 2002. He
joinedthe
GlobalInformation
andTelecommunication Institute (GITI), Waseda
University in 2002, as a research associate. Since
2005, he joined National Institute of Information andCommunications Technology (NICT), Japan, as anexpert researcher. He is also a visiting researcher in
GITI. His research interests include algorithms, network architectures,photonic network control, optical grid network systems, parallel anddistributed processing. Dr. Xu is a member of IEEE and IEICE.
Yoshiaki Tanaka (B.E. 1974–M.E. 1976–Ph.D.
1979) received the B.E., M.E., and D.E. degrees inelectrical engineering from the University of Tokyo,
Tokyo, Japan, in 1974, 1976, and 1979, respectively.
He became a staff at the Department of ElectricalEngineering, the University of Tokyo, in 1979, andhas been engaged in teaching and researching in thefields of telecommunication networks, switching systems, and network security. He was a guest professor
at the Department of Communication Systems, Lund Institute of Technology,
Sweden, from 1986 to 1987. He was also a visiting researcher at the Institutefor Posts and Telecommunications Policy, from 1988 to 1991, and at theInstitute for Monetary and Economic Studies, Bank of Japan, from 1994 to
1996. He is presently a professor at Global Information andTelecommunication Institute, Waseda University, and a visiting professor at
National Institute of Informatics. He received the IEEE Outstanding Student
Award in 1977, the Niwa Memorial Prize in 1980, the IEICE Achievements
Award in 1980, the Okawa Publication Prize in 1994, the TAF Telecom
System Technology Award in 1995 and in 2006, the IEICE Information
Network Research Award in 1996, in 2001, in 2004, and in 2006, the IEICE
Communications Society Activity Testimonial in 1997 and in 1998, the IEICE
Switching System Research Award in 2001, the IEICE Best Paper Award in2005, the IEICE Network System Research Award in 2006 and in 2008, theIEICE Communications Society Activity Award in 2008, the Commendation
by Minister for Internal Affairs and Communications in 2009, the APNOMSBest Paper Award in 2009, and in 2012, and the IEICE DistinguishedAchievement and Contributions Award in 2013. Professor Tanaka is a Fellowof IEICE.

Filippos Balasis (B.E 2010) received his B.E. inelectrical engineering and computer engineering from
National Technical University of Athens, Greece, in2010. Currently, he is a Master student in Global
Information and Telecommunication Studies, Waseda
University, Tokyo, Japan. His study emphasizes on
physical impairment aware RWA schemes intranslucent WDM optical networks. After fulfilling
his military service he worked as an engineer in OTE,
Greece's largest telecommunications provider, and he was part of the teamresponsible for the management and expansion of the company's accessnetwork. Mr. Balasis is a student member of IEICE.
Xin Wang (B.S. 2002–M.S. 2006) received the B.S. incomputer science and engineering from Dalian
University of Technology, Dalian, P.R.China, in 2002
and M.S. degree in computer science from Paris-sud
11 University, Paris, France, in 2006. Currently, he is a
Ph.D student in Global
Information andTelecommunication Studies, Waseda University, Tokyo,
Japan. His research interests focus on planning andcontrol issue in wavelength switched optical networks.
He received the IEICE ICM English Session Award and IEICE Young
Researcher’s Award in 2012. Mr. Wang is a student member of IEICE.
Sugang Xu (B.E. 1994–M.E. 1997–Ph.D. 2002) received his B.E. and M.E.
degrees in computer engineering from Beijing Polytechnic University, Beijing,

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Intelligent Packaging and Intelligent Medicine
Box for Medication Management towards theInternet-of-Things
Zhibo Pang*, Junzhe Tian**, Qiang Chen***Corporate Research, ABB AB, Västerås, Sweden** ICT School, Royal Institute of Technology (KTH), Stockholm, Sweden
pang.zhibo@se.abb.com, {junzhe, qiangch}@kth.se

Abstract—The medication noncompliance problem has caused
serious threat to public health as well as huge financial waste
would wide. The emerging pervasive healthcare enabled by theInternet-of-Things offers promising solutions. In addition, an
in-home healthcare station (IHHS) is needed to meet the rapidly
increasing demands for daily monitoring and on-site diagnosis andprognosis. In this paper, a pervasive and preventive medication
management solution is proposed based on intelligent andinteractive packaging (I2Pack) and intelligent medicine box(iMedBox). The intelligent pharmaceutical packaging is sealed by
the Controlled Delamination Material (CDM) and controlled bywireless communication. Various vital parameters can also becollected by wearable biomedical sensors through the wirelesslink. On-site diagnosis and prognosis of these vital parameters aresupported by the high performance architecture. Additionally,friendly user interface is emphasized to ease the operation for theelderly, disabled, and patients. A prototyping system of the I2Packand iMedBox is implemented and verified by field trials.
Index Terms—Medication Management; Internet-of-Things(IoT); In-Home Healthcare Station (IHHS); Wireless Sensor
Network (WSN); Controlled Delamination Material (CDM);
Radio Frequency Identification (RFID);

I. INTRODUCTIONPERVASIVE healthcare has been recognized to be thenext generation form of healthcare, and distributed,
patient-centric and self-managed care is emphasized as analternative to the traditional hospitalized, staff-centric andprofessional-managed care [1]. Pervasive healthcare based on
the emerging technologies of the Internet-of-Things (IoT), as
so-called Health-IoT, is highlighted as one of the killer
applications of the IoT [18, 21]. Many projects and initiatives

Manuscript received July 29, 2013. This work was supported in part by the
VINN Excellence Center of iPack at the Royal Institute of Technology.
Zhibo Pang is with ABB AB, Corporate Research, Forskargränd 7, 72178,
Västerås, Västmanland, Sweden (phone: +46-21 325104; fax: +46-21 323212;
e-mail: pang.zhibo@se.abb.com).
Junzhe Tian and Qiang Chen are with Royal Institute of Technology,
Department of Electronic Systems, Isafjordsgatan 39, 16440, Kista, Sweden.
(e-mail: {junzhe, qiangch}@kth.se).

have been devoted in this promising area. Unfortunately, theconcern to prescription medication noncompliance, a basic typeof self-managed care, is insufficient in the existing research [2].
A frequently cited fact is: medication noncompliance costs theUnited States healthcare system up to $100 billion per year, and
it is the cause of approximately 11% of US annual
hospitalizations [3]. It has been proven that, for the 4 mostdrug-spending chronic conditions (diabetes, hypertension,
hypercholesterolemia, and congestive heart failure),
hospitalization rates are significantly lower for patients withhigher medication compliance [4]. More startling figures arelisted in a report from the National Council on Patient
Information and Education (NCPIE) in 2007 [5]: only about
50% of American patients take their medicines as prescribed,
resulting in approximately $177 billion direct and indirect costs
to the U.S. economy annually.
To address the medication noncompliance problem, one
solution from traditional packaging industry is the One Dose
Packaging [6] which packetizes the tablets or capsules of one
dosage into one small box of bag. It just makes medication moreconvenient for patients, but neither improves the compliancenor prevents from noncompliance. Noncompliance detecting
and recording capability is offered by the Smart Medical
Refrigerator in [7], the microchip powered tablet package in [8]and the Smart Dose Reminder in [9]. But these are mainly
afterward checking measure instead of preventive measure, andthe operations of these solutions are so complicated that they are
only usable for trained caregivers instead of the elderly,
disabled, and patients.
At the same time, the increasing demands of daily monitoring
prompt the Health-IoT solution to integrate more sensing anddata processing capacities especially for on-site diagnosis andprognosis.
For
example,tri-axis
accelerometer,
electrocardiogram (ECG), blood pressure, blood oxygen
saturation (SpO2), respiration oxygen saturation, blood sugarconcentration, body temperature can be monitored on 24/7 basis[10-14]. So a powerful in-home terminal is needed not only toaddress the medication noncompliance but also to be used as a
generic in-home healthcare station (IHHS) in everyone’s home.
In this paper, extending our previous works in [16-21], anin-home medication management and healthcare system is

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proposed based on intelligent and interactive packaging (I2Pack)and intelligent medicine box (iMedBox). Preventive medication
management is enabled by the intelligent pharmaceuticalpackaging which is sealed by Controlled Delamination Material(CDM) and controlled by wireless communication. Various
vital parameters can also be collected by wearable biomedical
sensors through the wireless link. On-site diagnosis andprognosis of these vital signals are supported by the powerfularchitecture. Additionally, friendly user interface is emphasized
to ease the operations especially for the elderly, disabled andpatients. A prototyping system is implemented and verified byfield trials.
The rest of this paper is organized as follows. The vision of
Health-IoT and related concepts are introduced in section II.
The system functionality and architecture are given in section
III. Implementation and experimental results of the I2Pack and
iMedBox are presented in section IV and V respectively.
Section V concludes the paper and discusses open issues.

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one side, it is linked to public area (e.g. the hospital, themedicine supply chain and the emergency help center) throughwireless internet. On the other side, it controls a suit ofintelligent pharmaceutical packages (iPackage) and a suit ofwearable biomedical sensor tags (iTag) through radio frequency
identification (RFID) links and wireless biomedical sensor
network (WBSN) respectively. More information is available
in our previous work [16-21].

II. THE VISION
A. The Health-IoT
The in-home healthcare (IHH) service enabled by the IoTtechnology (the so-called Health-IoT) is promising for bothtraditional healthcare industry and the ICT industry. The
Health-IoT service is ubiquitous and personalized and will
speed up the transformation of healthcare from career-centric topatient-centric. Typically, a Health-IoT solution includes thefollowing functions:
Tracking and monitoring. Powered by the ubiquitous
identification, sensing, and communication capacity, all theobjects (people, equipment, medicine, etc.) can be tracked andmonitored by wearable WSN devices on a 24/7 basis.
Remote service. Healthcare and assist living services e.g.
emergency detection and first aid, stroke habitation and training,
dietary and medication management, telemedicine and remotediagnosis, health social networking etc. can be delivered
remotely through the internet and field devices.
Information management. Enabled by the global
connectivity of the IoT, all the healthcare information (logistics,
diagnosis, therapy, recovery, medication, management, finance,and even daily activity) can be collected, managed, and utilized
throughout the entire value chain.
Cross-organization integration. The hospital information
systems (HISs).are extended to patient’ home, and can be
integrated into larger scale healthcare system that may cover acommunity, city or even state.
B. Application Scenario
A typical application scenario of the Health-IoT system is
illustrated in Figure 1. The center of the system is a powerfulintelligent medicine box (iMedBox), which works not only as a
traditional in-home medicine container (such as a drawer ofcabinet, a thermostat or an icebox), but also as a “medication
inspector”, and an “on-site examiner” in daily monitoring. On

Figure 1 Application Scenario

C. Intelligent and Interactive Packaging“Ubiquitous” is the distinct feature of IoT technologies.
Ideally, it should be able to reach every item of the objects. The
Health-IoT system should be able to track every package of
medicine, record the medication activity of every tablet ofcapsule, and present all prescription information related to thepatient. To realize this feature, a suitable format of a smart
device is demanded. It should be able to carry the capacities andbenefits of IoT with affordable cost and in a natural manner.
According to the vision of iPack VINN Excellence Center [22],the Intelligent and Interactive Packaging (I2Pack) is a suitableformat of smart device for this purpose.
As shown in Figure 2 [22], the I2Pack is the next generationof packaging which can interact with customers by integrating
RFID, sensing, energy harvesting, communication, display,
acting and other functions onto traditional packaging. Whenpaper-based actuators and display are integrated, the packaging
can not only be aware of the presence of customers, but also beable to inform the customer what’s on.
The vision of I2Pack may make big transformations to thebusiness world. The information carried by the packaging willtransform from static to dynamic, the flow of information willtransform from single-directional (product-to-consumer only)todual-directional(both
product-to-consumer
andconsumer-to-product), and the role of packaging will transform

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Technology(TACT)from “passive” (only controlled by consumer) to “active”(self-controlled or remotely controlled). So the I2Pack can be
assigned more responsibilities in addition to the containing andprotecting of goods. The role of packing becomes“communication medium” between suppliers and consumers. It
is expected to be a seller on-site, an information presenter, an
information collector, and even an executor of particular
operations. For example, in the retailing application scenario,
the I2Pack with a touch sensor knows who has touched it, and if
integrated with a price tag, it can inform the customer “today’s
special offer”. In the Food-IoT application scenario, it can
inform customer the quality and freshness of the foodautomatically. In the Health-IoT application scenario, an
intelligent pharmaceutical packaging glued can be electrically
opened through finger touch. [22]

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user/patient cannot by hand open the iPackage which is sealed
tightly by the CDM films. Thus early or excessive medication
can be completely prevented.
III. SYSTEM ARCHITECTURE
A. System Architecture Diagram
As shown in Figure 3, multiple wireless links are integrated in
the iMedBox. User friendly user interfaces are provided
including a LCD displayer, a touch screen, a camera, speakers, amicrophone, shining LED lamps, and vibrators.
Wireless internet link: includes WiFi and/or GPRS/3G
communication stacks, system security, data base access, webserver for data shearing.
Global Positioning System (GPS) link: is useful to deliver
emergency supporting to the right location at the first moment.
WBSN link: includes stacks for star-topology networking
and data collection.
RFID link: includes medicine inventory management, CDM
controlling and medication compliance detection.

Figure 2 The vision of the intelligent and interactive packaging [22]Intelligent pharmaceutical packaging is one of the typical
implementations of the vision of I2Pack (see section III). It hasoffered a technical solution to achieve the so-called preventive
medication management.
D. Intelligent and Preventive Medication Management
Comparing to the existing solutions, preventive medication
management has been suggested to be more effective to address
medication noncompliance problem [5]. Preventive medication
management requires real-time reminding, recording,preventing of the medication activity, on-site diagnosis andprognosis of vital parameters, and emergency supporting to theusers. in particular, it includes the following functionalities.
Medicine Inventory: The iMedBox can register, record,
query and statistic all medicine utilities automatically byreading the RFID tags on them.
Medication Reminding: The iMedBox can download andparse the prescription automatically and remind the patient totake the medicine on time e.g. by flashing on the screen, playing
music via the speakers, flashing the lights, etc.
Noncompliance Recording: All of the patient’s medication
activities (e.g. opening the box, taking a dose, forgetting to takea dose, destroying some medicines by mistake, throwing somemedicines away, etc.) are detected and recorded.
Noncompliance Preventing: Without an “OPEN” commandfrom the iMedBox according to the prescription, theFigure 3 System Architecture

In the iPackage, a significant difference comparing totraditional RFID-enabled packages is, an array of CDM films
and corresponding control circuits are added. The CDM film is
a 3-layer foil composed of an aluminum bottom layer, an
aluminum top layer and an adhesive middle layer made ofelectrochemical epoxy. When the voltage subjected between thebottom layer and top layer is higher than a particular thresholdcalled Delamination Voltage (Vd), the electrochemical reaction
is triggered in the middle layer. If the voltage maintains longer
than a particular time called Delamination Time (Td), the epoxy

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layer is destroyed and thus the top layer bottom layer are
separated. Vd and Td are both distinct parameters determined by
the formula and processing when the material is manufactured[15].
The distinct characteristics of the CDM are used to perform
noncompliance preventing function of the intelligent
pharmaceutical packaging. The iPackage which is sealed with a
CDM film can be opened by the iMedBox by sending an OPEN
command to the microchip in the iPackage. And it cannot be
opened by the patient without the OPEN command since theadhesive glue of CDM is too tight to human’s hands. For eachtablet or capsule, there is one dedicated piece of CDM which iscontrolled separately. In other words, each tablet or capsule canbe controlled by the iMedBox. So the compliance ofprescription is achieved at tablet level.
The energy needed to open the CDM can be transmitted bywirelessly by near field magnetic resonance, so the intelligent
pharmaceutical is still battery-less. Or as an alternative, aprinted battery could be manufactured along with the package tomake a self-powered packaging solution. This has been provenby experimental results in the next sections. Both thebattery-less and battery-power solutions will have similar
out-looking to the traditional ones
The iTag has a typical architecture of WBSN. It comprises a
WBSN interface, a low power MCU, a suit of biomedical
sensors and a battery. The battery-powered WBSN device hassignificant advantages in communication distance and sensing
capacity comparing to the battery-less and self-powered ones.

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users feedback at first time and first place, which is very
important for some urgent situations such as acute myocardial
infarction.
So we adopt the On-site Diagnosis Framework to design the
proposed system. In iMedBox, a suit of basic diagnosis andprognosis examinations can be performed in the software.
Feasible algorithms are becoming available during the efforts inmany research activities, e.g. human movement classificationand detection based on tri-axial acceleration, ECG signal
compression based on discrete wavelet transformation, heartpremature detection based on ECG signal classification [11-14].
IV. IMPLEMETNATION OF THE INTELLIGENT PACKAGING
A. Prototype of the CDM
To analyze and evaluate the CDM material for iPackage, we
made a dedicated demonstration board including a near-fieldresonance coil, a resonance driver chip, an MCU, two button
batteries, a DC-DC converter, a power switch and a piece of
CDM film (Figure 5). In this step, the main power source is the
button batteries and the near-field resonance coil is just forcontrol commands. The DC-DC converter multiplies 3V battery
voltage to 30V to the CDM if an OPEN command is received
from the coil. More information is available in our previous
work [17].
CDM(intact)

CDM film

B. On-site Diagnosis Framework
Remote Diagnosis

Onsite Diagnosis
CDM (opened)~KBps/User~MIPS/User

Raw Data
Terminal
Raw Data

Results

VS

Terminal
Realtime

Non Realtime

Non Realtime~MBps/User~GIPS/User

Raw Data

Figure 4 On-site diagnosis vs. remote diagnosis

There are mainly two kinds of frameworks in pervasive
healthcare systems (Figure 4). In Remote Diagnosis Framework,
the raw data collected by on-site terminal is transmitted directly
to the server and a non-real-time and long term feedback is
given to the user according to analysis results. Contrary, in
On-site Diagnosis Framework, preprocessing is performed in
the on-site terminal and only results (instead of raw data) aretransmitted to the server. In this way, both the traffic load
between terminal and server and the computation load in theserver are reduced significantly. This makes the latter solution
more effective and efficient, because the data transmission andprocessing in Remote Diagnosis Framework would overwhelm
the communication system and resources in server when it is
widely adopted. Furthermore, the on-site terminal could give

Coil
Driver IC

MCU

DC-DC
Convertor

Authentication
RFID Card

Assembled
Packaging Box

Figure 5 Prototypes of CDM (left) and assembled intelligent package (right)

B. Setup of experiments for CDM charactorization
The main task of CDM characterization is to evaluate theenergy and power needed to open a unit area of CDM film,
where the Delamination Voltage (Vd), Delamination Maximum
Current (Id), Delamination Time (Td), Delamination Power (Pd)and Energy expense (Ed) are measured and calculated.
As mentioned in the previous study [15], CDM will quickly
open if it is added a voltage in 10-50V range, and if someenvironment conditions affect CDM electrochemical
characteristics. The following tests are performed at atemperature round 22°-25° and a relative humidity of 30%-37%,
within the safety range of 36V for human. To formalize the test
results, the size of test CDM pieces is set to 1cm2 because thesize of film is almost unrelated with opening time [15]. During
the whole delamination period, current through the CDM film ismeasured by a 180 Ohm serial resister.

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To monitor the delamination behavior of CDM, we apply a
30V DC power source to a 1cm2 CDM film. As shown in Figure
10, in the beginning, the current reaches its maximum value
0.95 mA/cm2 (Id) in a few seconds. When holding power source
voltage for about ten seconds, the current drops down
significantly. After 90 seconds, the current is reduced by over
90%, and the diminution turns to slow. At this moment, theCDM film is so weak that just a slight tough can make it peel off.
But as we can see in the last 60 seconds, without any external
force, the current through the material is very small which isinfinitely close to zero but never equal to zero.
In order to standardize the delamination behavior, a static
shear force is added to one surface of the CDM film by hanging
a mall weight. When the adhesive strength cannot afford theweight, the CDM film is disbanded immediately. The current atthat moment is called Turnoff Current which signifies thematerial is delaminated. And Td is defined as the interval frompower supply is applied on the surface till the current reaches
the Turnoff Current in a current curve.
D. Impact of Power Source Dutycycle
Four duty cycles of a 100 KHz and 30V power source areapplied to a 1cm2 CDM film respectively. When duty cycle ofpower source increases from 20% to 100%, the Td decreases
disproportionately (Figure 6).

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Figure 7 Impact of Power Source Voltage

F. Impact of Material Process Variety
For CDM pieces at different position from the same reel, thedifference of Td is neglidgible. But the difference between
pieces from different reels is larger according to measurement
results of 4 pieces from 4 different reels in Figure 7. The main
reason is that the difference in the material process and quality
from reel to reel is larger than that from region to region in one
reel.

CDM Current Curves (1cm*1cm)
1.4
DC 30V
100KHz 20% 30Vpp Squarewave
100KHz 50% 30Vpp Squarewave
100KHz 80% 30Vpp Squarewave

1.2

Current (mA)

1
0.8
0.6
0.4

Figure 8 Impact of Material Process Variety

0.2
0
-0.2

0

50

100

150

Time (s)Figure 6 Impact of Power Source Duty Cycle

E. Impact of Power Source Voltage
The current through the material under different powersource voltages from 5V DC up to 30V DC are plotted in Figure
6. When the power supply is lower than 5V, the CDM will notopen within 2 minutes without any large external shear force.
When the voltage increases from 5V to 30V, the Td decreases
significantly and disproportionately.

G. Summary of the CDM Characterization
From the results described above, we can make the following
conclusions:
1) Id is at the order of 1mA/cm2
2) Vd is at the order of 30V3) Td is at the order of 100s4) Pd is at the order of 30mW/cm2 (=1mA/cm2*30V)
5) Ed is at the order of 3J/cm2 (=30mW/cm2*100s)
This implies that a printed thin-film battery with 3.3mAhcapacitance is sufficient to open up to 12 tablets sealed with
1cm2 CDM film for each. This is a typical application case inpractical tablet packages. But one important factor should betaken into considerations: most of printed batteries have a large
internal resistance which will decrease the peak current andprolong the Td. So, it is feasible to use the CDM material torealize the proposed intelligent package.
H. Battery-less Intelligent Pharmaceutical Packaging

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Figure 9 Battery-less intelligent pharmaceutical packaging based on wirelesspower transmission

Currently the prototype is power by two icon batteries which
is not environment friendly. A battery-less solution is moresuitable for the intelligent pharmaceutical packaging. As shownin Figure 9, a battery less intelligent pharmaceutical packaging
is proposed based on the wireless power transmission
technology (WPT). The WPT is already in mass-production in
consumer electronic products, e.g. Nokia Lumia 920 mobile
phone, with affordable cost and sufficient maturity. According
to the results of CDM characterization, today’s wireless powertransmission technology is sufficient to provide this energy. For
example, the single chip wireless power receiver IDTP9020
from Integrated Device Technology, Inc. can output up to
5W@5V conforming to the Wireless Power Consortium (WPC)“Qi” specification. It is sufficient to drive a CDM array in thepharmaceutical packaging.

V. IMPLEMENTATION OF INTELLIGENT MEDICINE BOX
A. iMedBox Prototype
As shown in Figure 10, the 3nd generation of the iMedBox
prototyping system is implemented based on Samsung Galaxy
Tab10.1 tablet PC. The processor is Tegra 2 dual-core ARM
Cortex-A9 at the speed of 1GHz and manufactured by NVIDIA.
It has 16GB internal flash memory and could be extended byMicroSD card up to 32GB. It also has internal 2.0MP
front-camera, Wi-Fi/Bluetooch/3G access module, 6860mAh
battery, and USB connection. To support different port types of
WSN, a connection bridge based on FTDI Vinculum IIprogrammable USB Host/Slave controller is also implemented.
The connection bridge support UART, SPI Master, SPI Slave,and USB Host/Slave connection.
The tablet is embedded in the lid of the medicine box. And
the connection bridge together with plug-in modules is placed in
the bottom of the box. In the prototype, the connection bridge is
implemented with four adapter nodes. Each adapter node is
implemented with a VNC2 from Future Technology Devices
International Limited development module (Vinculum II), andcan provide one USB host port, one USB slave port, one SPI
master port, one SPI slave port, and one high speed UART port.
Four nodes are connected with SPI port as a chain. The first
node is connected to the tablet via USB port with the table inslave mode. The Android Open Accessory Protocol of Android
OS ensures the Vinculum II can be recognized and accessed by
the tablet without extra driver.

Figure 10 Hardware of the 3rd generation of iMedBox prototype

With the Vinculum II chips, the USB port of the tablet isremapped to four UART ports. One of the ports is connected toa main node receiving wireless sensor signals. One is connected
to a RFID reader which monitors the status of the tags on the
medicine package. The prototyping system also has a built-in
NFC reader. Take the advantage of the pear to pear
communication capability of NFC, the NFC reader can be
another interface for the iMedBox to communicate withbiomedical devices and authentication cards. One extra UART
is left for future extension.
B. Software and User Interface
The tablet is running Android 3.1 Honeycomb open source
mobile operating system released by Google under Apache
license. It is also capable to upgrade the system to Android 4.0
Ice Cream Sandwich or higher.
The demonstration software is called iMedBox – Pervasive
Healthcare Station. The software contains seven modules: core
activity, screen subsystem, database, USB controller, net
controller, alarm service and setting & configure subsystem.
The basic unit of the user interface of this iMedBox application
is tab. Users can add or remove tabs to customize the userinterface, and switch between each tab by swapping on thescreen. The screen subsystem support for displaying tab pagesof two basic types: the Chart Tab and Table Tab. The Chart Tab
content is rendered by a third party engine called AChartEngine.
The Table Tab is rendered by the build-in List View element.
Both the Chart tab and the Table Tab are designed to becustomized easily for a specified application. One customized
tab is called medicine tab.
There are two predefined tabs, a Chart Tab called “Signal
View” (Figure 11a) for real-time monitoring of biomedical
signals (e.g. ECG, body temperature, blood pressure, etc.), anda Table Tab called “Medication View” (Figure 11b) for
medication management application.
The Signal View shows the waveforms of signals e.g. ECG.
The chart can live update with the input ECG data packets. It
can also show some extra summarized results like the heart beatrate and body temperature. The Medication View shows the
medicine taken schedule for the user, contains the history andthe following plan. User can enable the alarming service of thistab. Then if the next time is up to take a dose of medicine, thetablet will wake up automatically, play a piece of alarm music,

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and show the medicine information for user to take.
When the alarm is triggered, the connected RFID reader
starts to detect the status of a specified tag. The tag ID has
mapped to the medicine which needs to be taken at the moment.
When the user picks up the right medicine, the alarm will stop
automatically and the alarm for the next medicine is set, but thescreen keeps showing a warning message. When the user
finishes taking the medicine and puts the bottle back, thewarning message will go away. Each successful medication
procedure will be marked with a happy face.

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Language (SQL) are shown in Figure 12b.

(a)

(a)

(b)
Figure 12 The Medication Database (a) tables and keys, and (b) procedureof queryD. Service Backend(b)Figure 11 User interfaces of the iMedBox software for (a) Signal View,and (b) Medication View.

Additionally, there is one reserved tab called “Home Tab”
which is a welcome page showing some general information likecurrent time, a tab list, an icon for switching to chatting
application (e.g. Skype), and a note area for displaying
customized messages.
C. Medication Database
The primary entities (tables) of the Medication Database(MDB) and relations among them are shown in Figure 12a
where all prescription and medication information is wellorganized. Correctly handling of the DoseList table in MDB isthe core of the database system. Firstly, when a new prescription
is issued in hospital, corresponding records are added in bydispatching one prescription into multiple dose lists. Secondly,
when user opens the iMedBox, a timely dose list that should betaken today is retrieved. Thirdly, medications records will be
updated to the MDB no matter if the doses are taken correctly ornot. Example procedure and codes in Structured Query

Patient list

Dosage list

Patient management
menu

Figure 13 Web interface for doctors to manage the medication information

A demonstrative backend system has been implemented. A
web-based server as well as databases are implemented by Javaand deployed on an Apache Tomcat web server. Professional
management interfaces are implemented as web page (Figure13), such as prescription management, case history management,
and remote diagnosis. The iMedBox can connect to the backend
system via either 3G modem or WiFi.
E. Demonstration and field trials

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Start machine
Load and Show Prescription;
Start reading RFID (> 5 rounds per second)

Show MainScreen

Connect to Server
Connected?
NY

If a medicine is taken away (notdetected in the last 5 rounds)?

Message “Server notavailable, using local data?”;Show two buttons “Yes” “No”
N

User selsect
Y
Load small local database

Load remote
database partly

High-light this medicine;
Message “Have you taken it correctly?”;Show two buttons “Yes” “No”
N

Message “Scan your PatientCard”;
Start scanning bar-code
N

User selsect
Y

Read bar-code?

Valid PatientID?
N
Show patient information on
MainScreen;
Stop reading bar-code

N

Dose finished?

Y
Query patient information
Y

Message “Youtook wrong
medicine,please change?”

This medicine is in thedose?

Y
Message “Invalid
PatientCard, please
try again”

Message “Congratulations!! You have finished thecurrent dose! Repeat demonstration?”;Show two buttons “Yes” “No”
N
User selsect
Y

Figure 14 Flow chart of the demonstration
Prescription list
Finished dose
Missing dose
Current dose
Future dose
Feedback
buttons

Medicine packagewith RFID tag

Figure 15 Demonstration of the iMedBox for medication management
application

Field trials for the medication management services havebeen carried out with Blekinge Center of Competence (BCC),County Council of Blekinge, Sweden [18]. The scenario isshown in Figure 14 and the flow chart in Figure 15. The
iMedBox prototype were demonstrated to patients, relatives
that take care of patients, elder people, and healthcare personal.
Three different groups have been chosen:(1) A sheltered housing “Vidablicks” in the city of Ronneby.
We demonstrated to a group of nurses that have meeting every
second months to share their experiences in the field ofdementia care.
(2) Healthcare staffs at Valjeviken’s rehabilitation center.
We met the healthcare staffs including medicine doctors, nurses,

undersköterskor, arbetsterapeuter, sjukgymnaster and kuratorer
to get the detailed information about the benefits andconsequences of using iMedBox.
(3) Staff and caretakers at an elderly living in Karlskrona“Fregatten”. We discussed the acceptances of different kind ofassisting equipment to remind medicine time or to make it easier
to take the right amount.
The concepts and ideas proposed in this paper have been
confirmed by positive feedback. All audience agree that the
medication reminding and recording functions can significantly
improve the compliance especially for elderly, and even morenecessary for the elderly with light dementia symptoms. It isnecessary to integrate it to the hospital’s prescription system toreduce the workload.
At the same time, the comments from the audience haveindicated directions of improvement in the future. For example,(1) To add pictures of the drugs and drug packages on thescreen.
(2) The iMedBox should be secured with a locking system,
making it accessible only by specified user rather thanunsuitable person such as the patient with dementia symptom.
(3) To simplify the user interface further and make the texts
and colors clearer.
VI. CONCLUSION
In this paper, a pervasive and preventive medication
management solution addressing the medication noncomplianceproblem is proposed. A prototype system is implemented which
confirms the feasibility of the presented intelligent

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November 20132013pharmaceutical packaging and intelligent medicine box.
As future work, on-site algorithms will be implemented aswell as data collection through WBSN devices. Integrating the
CDM film with wireless power transmission and RFID tag onto
one flexible substrate is another task. User interfaces andexperience will be further improved too.

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[20] Zhibo Pang, Qiang Chen; Lirong Zheng, Elena Dubrova. “An In-homeMedication Management Solution Based on Intelligent Packaging andUbiquitous Sensing”. International Conference on Advanced
Communications Technology (ICACT). Jan 2013, Pyeongchang, Korea.
[21] Zhibo Pang, “Technologies and Architectures of the Internet-of-Things(IoT) for Health and Well-being”, PhD Thesis, Royal Institure ofTechnology (KTH), Stockholm, Sweden, 2013.
[22] iPack VINN Excellence Center, (2010). A General Presentation on
Ubiquitous Intelligence in Paper and Packaging. [on-line]
http://www.kth.se/ipack [accessed Dec 1, 2012]

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”“

Zhibo Pang received B.Eng. degree in Electronic
Engineering from Zhejiang University, Hangzhou,
China, in 2002, MBA in Innovation and Growth from
University of Turku, Turku, Finland, in 2012, and PhD
in Electronic and Computer Systems from the RoyalInstitute of Technology (KTH), Stockholm, Sweden, in
2013.
He is a research scientist at ABB Corporate Research,
Västerås, Sweden. Before joined ABB, he worked astechnical manager in semiconductor industry, designing baseband andapplication processors and turn-key solutions for mobile smart devices. He has
15 patents and over 30 peer-reviewed papers in international journals andconferences. He was awarded the National Great Invention Award by the
Ministry of Information Industry of China in 2005, won the First Place Prize of
the RFID Nordic EXPO in 2008 and Outstanding Paper Awards in
ICACT2013. His current research interests include the Internet-of-Things,
wireless sensor network, industrial communication, real time embedded
system, enterprise information systems, automation networks, and multicore
system-on-chip and network-on-chip. He also works on thebusiness-technology joint research such as business model design, value chain
formulation, strategy, and entrepreneurship & intrapreneurship.

Junzhe Tian received B.Eng. degree in
Communication Engineering from Beijing University ofPosts and Telecommunications (BUPT), Beijing, China,
in 2010, M.Sc. degree in System-on-Chip Design fromRoyal Institute of Technology (KTH), Stockholm,
Sweden, in 2013. After graduate, he became a Softwaredeveloper at Excosoft AB Stockholm, Sweden.
Currently he is working on design and implementationof Graphical Editing Framework (GEF) and Document
Type Definition (DTD) in publishing industry.
Qiang Chen received the Ph.D. degree in electronics from
Linköping University, Linköping, Sweden, in 1993.
He was with Ericsson Microelectronics, Stockholm,
Sweden, from 1994 to 2002 and became an Ericsson
Expert in 2001. He joined Infineon Technology Sweden,
Stockholm, in 2003 as a Principal. He has been at RoyalInstitute of Technology, Stockholm, as a Senior Research
Fellow and Project Leader with the Department ofElectronic Systems since 2008. He is a Senior Research
Scientist with the Department of Electronic Systems and iPack VINN
Excellence Center, School of Information and Communication Technology,
Royal Institute of Technology. He has published more than 40 international
reviewed scientific papers. His current research interests include electronic
devices and circuits, wireless sensor network and sensor technology, andprinted electronics.

Copyright ⓒ 2013 GiRI(Global IT Research Institute)
Copyright ⓒ 2013 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology
Technology(TACT)(TACT)Vol.
Vol.2,
2,Issue
Issue6,
6,November
November2013
2013

361
361

Introducing Dual Regularization Parameters into
Regularized Channel Inversion (RCI)-Based Vector
Perturbation for Modulo Loss Reduction
Yafei Hou, Satoshi Tsukamoto, Kazuto Yano, Masahiro Uno, Kiyoshi Kobayashi
ATR Wave Engineering Laboratories, Kyoto, Japan
2–2–2, Hikaridai, Seika–cho, Souraku–gun, Kyoto, 619–0288, Japan{yfhou, tsukamoto, kzyano, uno, kobayashi}@atr.jp

Abstract—This paper proposes a method to reduce the symbol error caused by modulo operator for regularized channel
inversion (RCI)-based vector perturbation (RCI-VP). The proposed method introduces dual regularization parameters of RCI
precoding matrix. First regularization parameter α1 is set so as
to maximize the signal to interference plus noise ratio (SINR)
value of each stream to find the optimal perturbation vector.
The second regularization parameter α2 is set so as to minimize
the total MSE between the transmitted perturbation vector andreceived perturbation vector to reduce the symbol error caused bymodulo operator. The simulated results confirm that the proposed
method can improve the performance of RCI-VP over i.i.d.
MIMO channel and frequency-selective fading MIMO channel.
Keywords—Vector perturbation, SINR maximization, Modulo
loss, Regularization parameter, Minimum mean square error

I. I NTRODUCTION
HE technique of multiuser multiple-input multiple-output(MU-MIMO) can be utilized to improve downlink transmission rate of each user in picocell or femotocell systems. Fora downlink MU-MIMO system, many spatial division multiple
access (SDMA) techniques can be selected to improve the MUMIMO channel capacity [1]. Among them, linear precoding
combined with vector perturbation (VP) [2] is an algorithm toachieve MU-MIMO capacity bound using dirty paper coding(DPC) [3].
VP algorithm includes regularized channel inversion (RCI)based VP (RCI-VP) [4] and zero-forcing (ZF)-based VP (ZFVP). RCI-VP utilizes a parameter α for RCI precoding matrix
and it is equivalent to ZF-VP if α equals zero. RCI-VP
generally outperforms ZF-VP if the α value is appropriately selected. Therefore, finding an efficient regularization parameter
α for RCI precoding matrix is a research topic for performance
improvement.
The general objective function for finding an optimal α isto maximize the signal to interference plus noise ratio (SINR)

T

Manuscript received August 1st, 2013. This work was supported by the
Ministry of Internal Affairs and Communications under a grant entitled“Research and development on nonlinear multiuser MIMO technologies.” This
paper has been partially presented at the 15th international conference on
advanced communications technology (ICACT 2013).
The authors are with the ATR Wave Engineering Laboratories, 2–2–2,
Hikaridai, Seika–cho, Souraku–gun, Kyoto, 619–0288, Japan. Corresponding author to provide phone: 81-774-95-1574; fax: 81-774-95-1508; e-mail:yfhou@atr.jp

of each stream. However, this optimal value is still an open
issue and is only searched with a numerial method [2], [5].
Another objective function for optimizing α is to minimize
the total mean square error (MSE) of the transmitted data
and received data. To reach the aim, several methods havebeen proposed such as processing VP with continuous-valued
perturbation vector [6] or minimizing the bit-error ratio (BER)of generalized RCI-VP system as the optimization criterion [7].
Several schemes improve the RCI-VP performance by shaping
the transmitted symbols with different lattice transformation
techniques [8]. Reference [9] showed that finding an optimal
α to maximize the SINR value of each stream is equivalent
to finding the optimum perturbation vector that minimizes
the total MSE of the transmitted data and received data. But
the method requires a transformed precoding matrix whichneeds some high-complexity matrix decomposition processes
for searching the optimal perturbation vector.
In this paper, we first show that, using precoding matrix
with optimized α based on SINR, the total MSE between
the transmitted perturbation vector and received perturbation
vector is not generally minimized. In such cases, the modulo
operator at receiver sides deteriorates BER performance. In
order to improve the performance of RCI-VP, we propose amethod using dual regularization parameters of RCI precoding
matrix. First, we find the optimal regularization parameter α1
to maximize the SINR value of each stream by simulation fordifferent modulation to realize the optimal perturbation vector
search. Then we find the optimal regularization parameter α2
to minimize the total MSE between the transmitted perturbation vector and received perturbation vector to reduce thesymbol error generated from a modulo operator. The simulated results show that the proposed method can improve theperformance of RCI-VP especially for QPSK modulation overi.i.d. MIMO channel and frequency-selective fading MIMO
channel.
This paper is organized as follows. The RCI-VP algorithm
and QR-decomposition M-algorithm encoder (QRDM-E) [10]are briefly described in Section II. Then the conventional
RCI-VP with optimized α based on SINR and the proposed
method are introduced in Section III. The performance ofthe proposed method over flat-fading and frequency-selective
fading channels is shown in Section IV. Finally Section V gives
conclusions.

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ICACT Transactions on Advanced Communications Technology
Technology(TACT)(TACT)Vol.
Vol.2,
2,Issue
Issue6,
6,November
November2013
2013

N otation : We use upper (lower) boldface letters to denote matrices (column vectors), sometimes with subscripts toemphasize their sizes. (.)∗ , (.)T , (.)H will denote complex
conjugate, transpose, conjugate transpose. E{.} and Tr{.} are
the expectation operator and matrix trace operator.
II.

RCI- BASED VECTOR PERTURBATION (RCI-VP) AND
QRDM-E NCODING

A. RCI-based vector perturbation (RCI-VP) algorithm for
MU-MIMOA general model of downlink MU-MIMO system includes
a BS with Nt transmitting antennas and K users, each with
Nr receiving antennas. We assume that Nt = KNr . The
corresponding vector equation isy = Hu + n
T

(1)
T

where y = [y1 , . . . , yNt ] u = [u1 , . . . , uNt ] , andn =[n1 , . . . , nNt ]T . The Nt × Nt matrix H has complex
element hi,j of MIMO channel with E{|hij |2 } = 1. We
assume the power constraint E{|u|2 } = 1 and σ 2 is the signal
to noise ratio (SNR) at the receiving antenna named as SNR
per stream. Therefore the noise variance matrix E{nnH } isrepresented as Nt1σ2 INt and INt is Nt × Nt identity matrix.
The linear RCI MU-MIMO structure is shown in Fig. 1 (a).
Suppose that BS has the perfect CSI H, the RCI spatial filter
is represented asα
W = HH (HHH + 2 INt )−1 .
(2)σThe transmitted data x is processed asαs = Wx = HH (HHH + 2 INt )−1 x.
σAfter normalized the symbol s as

1
u=
s
γ

(3)

(4)

with γ = ||s||2 = ||Wx||2 , the transmit symbol u istransmitted over MU-MIMO channel H to all users. Therefore,
the received symbol y is represented as1α
HHH (HHH + 2 INt )−1 x + n.
(5)
y=
γ
σ
√
√
After multipled by γ, the received symbol γy is demodulated as x̂. The regularization parameter α is used to minimize
the total MSE between the transmitted symbols x and received
symbols x̂.
However, if H is a high correlated channel matrix, thenormalized parameter γ becomes large therefore the SNR
becomes small. To mitigate its effect, vector perturbation (VP)
perturbs the transmitted symbol x with a perturbation vector
to reduce the normalized parameter γ [2]. We use Fig. 1(b) toshow its sturcture. Let us set x̂ = x + τ  where τ is chosenas 2(|c|max + dmin /2). Here |c|max is the absolute value of
constellation point with the largest amplitude and dmin is the362

spacing between two adjacent constellation points.  is an Nt dimensional complex perturbation vector a + ib, where a andb are integers. The normalized parameter γ is computed asαγ = ||HH (HHH+ 2 INt )−1 (x+τ )||2 = ||W(x+τ )||2 . (6)σThe core design of VP is to find the optimum choice 0which minimizes the γ to γ0 , that is

2


α
 H
H
−1
 
H
0=arg min
(HH
+
I
)
(x+τ

)
(7)



N
t

σ2
 
with


2


 H

H α
−1
γ0 = H (HH + 2 INt ) (x+τ 0 ) .


σ

(8)

If BS uses the precoding matrix W with regularization
parameter α to pre-cancel the interference among streams, thetransmitted signal after perturbation vector search as Eq. (4)is1αu = √ HH (HHH + 2 INt )−1 (x + τ 0 ).
(9)γ0σIn such case, the purpose of RCI-VP is, in essence, theoptimization to get the smallest γ to improve SNR at thereceiver side.
The symbol u is transmitted √over MU-MIMO channel H to
all users. After multiplied by γ0 , the received symbol canbe represented asα√(10)ŷ = HHH (HHH + 2 INt )−1 (x + τ 0 ) + γ0 n.
σTo remove the effect of the integer multiple of τ for ith
stream, the receiver utilizes the modulo function asŷi + τ /2x̂i = mod τ (ŷi ) = ŷi −τ(11)τwhere the function . gives the largest integer less than orequal to its argument. With the modulo function, the receivercan demodulate its own data.
B. QR-decomposition M-algorithm encoder
The perturbation vector search of Eq. (7) is a 2Nt dimensional integer-lattice least-squares problem which canbe resolved using the sphere decoder [11]. However, thecomputational complexity (CC) of the sphere decoder is large
and limits the application of VP. An efficient sphere decoder
algorithm can be realized using the QR-decomposition Malgorithm encoder (QRDM-E) [10] which can balance thetrade-off between the CC and the diversity order of VP.
QRDM-E limits the integer value a and b of  selectedfrom the symmetric integer set A as A = {−T, −T +1, . . . , −1, 0, 1, . . . , T − 1, T }, which reduces the search complexity with a smaller value T . On the other hand, QRDM-E
factorizes the matrix W of Eq. (7) into the product of a unitary

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ICACT Transactions
Transactions on
on Advanced
Advanced Communications
Communications Technology
Technology(TACT)ICACT(TACT) Vol.
Vol. 2,2, Issue
Issue 6,
6, November
November 2013
2013

363
363

#

#

f 5 Sf) 3 2 2 2 3 ffl UL.

#

O 5 @@ @@@ # * AFl !x)

#

#
#

.

#

O 5 @@ @@@ # * AFl !x)

Fig. 1.

#The structures of linear MMSE MU-MIMO and nonlinear RCI-VP MU-MIMO.

22k ) Efjx k j g=°
S I N R k = (1+¯:
Efjv k j2 g=° +¾ 2+
yk = p1° [(xk +¿lk)+(¯kxk +±k¿lk) +vk]+nk
Fig. 2.

The symbol mismatch caused by modulo operator on received symbol with optimization of SINR for each stream.

matrix Q and an upper triangular matrix R. Thus, the search
problem in Eq. (7) is simplified to0=arg min ||QR(x+τ  )||2=arg min ||R(x+τ  )||2 . (12) ∈ANt ∈ANtThe search process of Eq. (12) is realized using an Malgorithm for each stream in a successive way. The best
M branches that have the least accumulative metrics to find
0 (k + 1)τ for the (k + 1)th stream are retained for the nextstage to find 0 (k)τ for the kth stream. We omit the illustrationfor QRDM-E process and reader can find the clear explanationin [10].

III.

T HE PROPOSAL OF RCI-VP ALGORITHM WITH DUAL
R EGULARIZATION PARAMETERS

A. Regularization parameter α for optimization of SINR foreach stream
The regularization parameter α can be optimized to balance
the inter-stream interference and noise power to maximize
the SINR. The optimal value α is generally obtained by anumerical method as pointed out in References [2], [5]. It isalso shown in [2] and [5] that the optimal α for maximizing
SINR is generally smaller than Nt . We briefly review this

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Advanced Communications
Communications Technology
Technology(TACT)(TACT) Vol.
Vol. 2,2, Issue
Issue 6,
6, November
November 20132013

process. To get the optimal α, the received signal is written as
H

HH
1
y= √
(x + τ 0 ) + n
H
γ0 HH + σα2 INt
1 
INt (x + τ 0 ) + F (x + τ 0 ) + n,=√γ0where F = {(INt + σα2 (HHH )−1 )−1 − INt }. The received
signal for the kth stream is1
yk = √ [(xk + τ 0 (k))+ < F (x + τ 0 ) >k ]+nk
γ0(14)where the notation < C >k represents the kth row of matrix
C. It is clear that < F (x + τ 0 ) >k is potentially correlatedwith xk and 0 (k). This correlation can be model as1
yk = √ [(xk + τ 0 (k))+(βk xk + δk τ 0 (k)) + vk ]+nk
γ0(15)where vk is co-channel interference (CCI) and uncorrelatedwith xk and 0 (k). Here βk and δk represent the correlation
coefficients of the term < F (x + τ 0 ) >k with xk and0 (k). As shown in Reference [5], the SINR of the kth stream
SIN Rk is
SIN Rk =(1 + βk )2 E{|xk |2 }/γ.
E{|vk |2 }/γ0 + (Nt σ 2 )−1realize such optimization, we consider a precoding matrix P.
The received symbol x̂ is represented as1x̂r = √ HPx̂ + n
γP

(13)

(16)

The regularization parameter α controls the value of γ0 , thecorrelation βk and the variance of vk . Increasing α generally
decreases γ0 , then potentially increasing the SIN Rk , but
increases the variance of vk , then potentially decreasing the
SIN Rk . Therefore the overall impact on the performance of
RCI-VP is difficult to determine analytically. Therefore, it isgenerally obtained by a numerical method.
However, the optimization of maximizing SINR does notconsider the effect of modulo operator which will increase
the symbol error caused by the modulo process. Let us use
Fig. 2 to show the process of symbol error on transmitting
symbol xk . After optimization of maximizing SINR, the RCIVP utilizes the regularization parameter α to the transmit data.
The receiver receives the symbol ŷk which statistically has the
maximum SINR. As shown in Fig. 2, the received symbol ŷk isout of modulo bound and causes symbol error after the modulo
operation.
B. Regularization parameter α for optimization of total MSE
between the transmitted perturbated vector and received perturbated vector
To minimize the symbol error caused by modulo operator,
α is to be optimized so as to minimize the total MSE between
the transmitted perturbated vector (x + 0 τ ) and its received
perturbated vector x̂r at the receiver side. After perturbation
vector search, the transmit data x is perturbated as x̂ = x+τ 0 .
Here τ 0 can obtain the smallest γ0 as shown in Eq. (8). To

364
364

(17)

where γP equals E{||Px̂||2 }.
The optimization can be represented as{Popt , γopt } = arg min E{||x̂r − x̂||2 }P,γP

S.t. :

E{||Px̂||2 } = γP(18)

The analytic solution for this optimization is represented as
Popt = HH (HH + ξINt )−1γopt = ||Popt x̂||2(19)twhere the regularization parameter ξ = Tr{ σ12 INt }= Nσ2 [12].
The solution also shows that if the previous optimal regularization parameter α1 for maximizing the SINR obtained bynumerical method is identical to the regularization parameterξσ 2 , the total MSE between the x̂ and x̂r is also minimized.
However, as shown in References [2] and [5], the optimal α
for maximizing the SINR is generally smaller than Nt andcannot realize both optimization targets.

C. Proposed method with dual regularization parameters
The proposed method, which is combined by both optimalization targets of the regularization parameters, is realized asfollowing steps.
(1) We use numerical method to find an efficient regularization parameter α1 which can achieve best BER performancefor each different modulation based on simulated results.
(2) For perturbation vector search, the optimum choice 
which minimizes γ is searched using precoding matrix withregularization parameter α1 as HH (x+τ  ) 2
 = arg min 
(20)
 .
α1
H
N



t
HH
+
I
 ∈A
σ 2 Nt(3) The precoding matrix W is set as W = Popt =−1
2
HH (HHH + α
with the minimum-mean square erσ2 INt )ror (MMSE) weight to realize the total MSE optimization.
Therefore, after obtaining the optimum choice τ , BS sets thetransmit symbol as1α2
u = √  W(x + τ ) = HH (HHH + 2 INt )−1 (x + τ ) (21)σγ−12where γ  = ||(HH (HHH + α)(x + τ )||2 .
σ2 INt )IV.

P ERFORMANCE EVALUATION OF PROPOSED METHODOVER THE FLAT- FADING AND CORRELATEDFREQUENCY- SELECTIVE FADING MIMO C HANNEL
A. Finding α1 by numerical method
We utilize a numerical method to find efficient value of α1 .
As shown in References [2] and [5], the optimal α1 is usuallysmaller than Nt . We simulate the BER performance of VP

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ICACT(TACT) Vol. 2, Issue 6, November 2013
ICACTTransactions
Transactionson
onAdvanced
AdvancedCommunications
CommunicationsTechnology
Technology(TACT)



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10

-3

Original method, D1=2, D2=2, 16QAM
Proposed method, D =2, D =4, 16QAM
1

1

10

-5

2

䎃

Fig. 4.

1

2
2

Proposed method, D =1, D =4, QPSK

Original method, D1=4, D2=4, 64QAM

1

0

1

Proposed method, D =2, D =8, 16QAM
Proposed method, D =2, D =16, 16QAM
1

2

10

-5

2

Proposed method, D =4, D =6, 64QAM
1

2

Proposed method, D =1, D =8, QPSK

Proposed method, D1=4, D2=8, 64QAM

Proposed method, D1=1, D2=16, QPSK

Proposed method, D =4, D =16, 64QAM

1

-6

2

Proposed method, D =2, D =6, 16QAM-4

Proposed method, D1=1, D2=2, QPSK
Proposed method, D1=1, D2=6, QPSK

10



0

-4

Original method, D =1, D =1, QPSK

10



-3

BER
10



The simulated BER performance of RCI-VP algorithm when α1 = α2 (left: QPSK modulation; right: 16QAM, 64QAM modulations)

10
10



615SHUVWUHDP>G%@615SHUVWUHDP>G%@

Fig. 3.

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365
365

3

2

6

9

SNR per stream [dB]

12

15

10

-6

1

䎃

0

3

6

2

9

12

15

SNR per stream [dB]

18

21

24

27

The simulated BER performance RCI-VP algorithm with different value of α2 (left: QPSK modulation; right: 16QAM, 64QAM modulations)algorithm with different value of α1 and α2 over i.i.d. MIMO
channel. We assume Nt = 8 and (T, M ) = (3, 7) for QRDME algorithm. The simulated BER results are shown in Fig. 3
for QPSK, 16QAM and 64QAM modulations. According to
the simulated results, α1 = 1, 2, 4 gives the best performancefor QPSK, 16QAM and 64QAM over (8×8) i.i.d. channel,
respectively.
B. BER performance evaluation of proposed method over flatfading MIMO Channel
Fig. 4 shows the simulated results of the proposed method
over i.i.d. MIMO channel with different modulations. We also
assume Nt = 8 and (T, M ) = (3, 7) for QRDM-E algorithm.
To show the impact of α2 , we choose different values tocompare the BER performance. The simulated results showthat the proposed method can improve the performance of
RCI-VP for all modulations especially for QPSK modulation.
The reason dues to that, for modulation with a large numberof constellation points, the effect of modulo function has
little impact on the interior constellation points than that ofboundary constellation points even if the MSE of between thetransmitted perturbated vector and received perturbated vector
is slightly increased.

C. Spectrum efficiency (SE) evaluation of proposed method
over flat and frequency-selective fading MIMO Channels
We compare the spectrum efficiency (SE) of the proposed
method using dual regularization parameters with the original
method using one regularization parameter based on SINR
optimization. The simulation specifications are given in Table
I. We use i.i.d. channel, one indoor correlated channel model(A1 LOS) and one suburban macro cell channel model (C1
NLOS) as shown in Table II to evaluate SE of the proposed
method and the original method. We also assume that thetransmitter and the receiver have the perfect knowledge of CSI.
We first compare the proposed method using dual regularization parameters with the original method using one
regularization parameter over i.i.d. channel and the simulated
results are given in Fig. 5. The values of α1 and α2 are set
based on the previous simulated results. As shown in the figure,the proposed method can achieve better SE performance thanthat of original method especially for QPSK modulation.
The simulated results over A1 LOS channel with 1λ and10λ spacing for BS antennas are shown in Fig. 6 and Fig. 7.
The values of α1 and α2 are also set based on the previous
simulated results over i.i.d. MIMO channel. As shown inboth figures, the simulated results confirm that using different

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ICACT Transactions
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on Advanced
Advanced Communications
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Technology(TACT)ICACT(TACT) Vol.
Vol. 2,2, Issue
Issue 6,
6, November
November 2013
2013

36

䎃

QPSK, D =D =1, original method
1

S IMULATION SPECIFICATIONS

Sampling rate
FFT size
Length of cyclic prefix
Number of subcarriers
Number of antennas
Number of users (N )Direction of UEs
Modulation scheme
Channel coding
Coding rate
Frame size

Interleaver
Decoding algorithm
Number of deocoding
iterations
Array configuration
Antenna spacing
Carrier frequnecy
Spatial filtering
Estimation of average
received SNR
Perturbated vector
search30

16QAM, D =D =2, original method
1

2

16QAM, D =2, D =8, proposed method
130.72 Msamples/s
2048
4.7μs
1200
8 (BS)
2 (UE)
4-67.5, -22.5, 22.5 and 67.5 (Deg.)
QPSK, 16QAM, 64QAM
Turbo code
3/43597 bits +3 pality bits (QPSK)
3597 bits +3 parity bits (16QAM)
5397 bits +3 parity bits (64QAM)
Random interleaver
soft-output Viterbi algorithm

264QAM, D1=D2=4, original method
24

64QAM, D1=4, D2=8, proposed method

18

12

6

0䎃
0

2

4

6

8

10

12

14

16

18

20

22

SNR per stream [dB] ((8u 8) VP, WINNER II A1 LOS, 1 O spacing for BS antennas)

24

Fig. 6.
The spectrum efficiency performance comparison of RCI-VP
algorithm with and without dual regularization parameters over A1 LOSMIMO channel (1 λ spacing for BS antennas).

6
Equally spaced linear array1λ and 10λ of DL carrier frequency (BS)0.5λ of DL carrier frequency (UE)3.36 GHz (DL)
MMSE

䎖䎙

䎃

QPSK, D =D =1, original method
1

2

QPSK, D =8, D =1, proposed method
1

Perfect䎖䎓

QRDM-E [10], (T, M) = (3, 7)

C HANNEL PARAMETERS FOR A1 LOS AND C1 NLOS [13]Channel model
Average delay spread [ns]Average AoA/AoD [Deg.]
Average K-factor [dB]

A1 LOS
40
44/45
5-6

Spectrum efficiency [bit/s/Hz]

TABLE II.

2

QPSK, D1=1, D2=8, proposed method

Spectrum efficiency [bit/s/Hz]

TABLE I.

366
366

2

16QAM, D =D =2, original method
1

2

16QAM, D1=8, D2=2, proposed method
64QAM, D1=D2=4, original method

䎕䎗

64QAM, D1=8, D2=4, proposed method

䎔䎛

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䎙

C1 NLOS
234
8/45
-

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䎕䎓
䎕䎕
䎕䎗
SNR per stream [dB] ((8u 8) VP, WINNER II A1 LOS, 10 O spacing for BS antennas)

Fig. 7.
The spectrum efficiency performance comparison of RCI-VP
algorithm with and without dual regularization parameters over A1 LOSMIMO channel (10 λ spacing for BS antennas).
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Fig. 5.
The spectrum efficiency performance comparison of RCI-VP
algorithm with and without dual regularization parameters over i.i.d. MIMO
channel.

values of α1 and α2 can increase the SE of the RCI-VP
system than that using the method based on the original SINR
optimization especially for the modulation with a small numberof constellation points such as QPSK modulation.
The simulated results over C1 NLOS channel with 1λ and10λ spacing for the BS antennas are shown in Fig. 8 andFig. 9. As shown in both figures, the simulated results confirm
that using dual regularization parameters increases the SE of
RCI-VP system than that using the method based on original
SINR optimization especially for QPSK modulation. However,compared with Fig. 8, the SE improvement in Fig. 9 using dual
regularization parameters is small because the channel is a low
correlated one even if the BS antenna spacing is 1 λ.
Based on the simulated results over flat and frequencyselective fading channels, using dual regularization parameters
for RCI-VP system can reduce the symbol error caused by amodulo operator compared with the system using one regularization parameter based on the criteria of SINR maximum.
The proposed method will provide better SE improvement

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Institute)Technology(TACT)ICACT Transactions on Advanced Communications Technology(TACT)Vol.
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6,November
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36

䎃

QPSK, D =D =1, original method
1

2

QPSK, D =8, D =1, proposed method
1

30

2

16QAM, D1=D2=2, original method
16QAM, D =8, D =2, proposed method

Spectrum efficiency [bit/s/Hz]

1

2

64QAM, D =D =4, original method
1

24

2

1

2

R EFERENCES

12

6

0䎃
-2
0
2
4
6
8
10
12
14
16
18
20
22
SNR per stream [dB] ((8u 8) VP, WINNER II C1 NLOS, 1 O spacing for BS antennas)

Fig. 8.
The spectrum efficiency performance comparison of RCI-VP
algorithm with and without dual regularization parameters over C1 NLOSMIMO channel (1 λ spacing for BS antennas).
䎃

QPSK, D1=D2=1, original method
QPSK, D1=8, D2=1, proposed method

Spectrum efficiency [bit/s/Hz]

䎖䎓

16QAM, D =D =2, original method
1

2

16QAM, D1=8, D2=2, proposed method
64QAM, D =D =4, original method1

䎕䎗

ACKNOWLEDGMENT
The authors appreciate that Dr. Masayuki Ariyoshi hassuggested many good advices and comments on this paper.

64QAM, D =8, D =4, proposed method

18

䎖䎙

367
367

2

64QAM, D1=8, D2=4, proposed method

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䎙

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䎕䎕
SNR per stream [dB] ((8u 8) VP, WINNER II C1 NLOS, 10 O spacing for BS antennas)

Fig. 9.
The spectrum efficiency performance comparison of RCI-VP
algorithm with and without dual regularization parameters over C1 NLOSMIMO channel (10 λ spacing for BS antennas).

especially over a low SNR range using the modulation withsmall number of constellation points.
V.

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[2] B. M. Hochwald, C. B. Peel, and A. L. Swindlehurst, “A
vector-perturbation technique for near-capacity multiantenna multiuser
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[3] U. Erez, and St. Brink., “A close-to-capacity dirty paper coding
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[4] C. B. Peel, B. M. Hochwald, and A. L. Swindlehurst, “A
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[6] W. S. Chua, C. Yuen and F. Chin, “A continuous vector-perturbation
for multi-antanna multi-user communication,” in Proc. 2007 IEEE VTCSpring, pp. 1806-1810, Apr. 2007.
[7] W. Yao, S. Shen and L. Hanzo, “ Generalized MBER-based vector
precoding design for multiuser transmission,” IEEE Transactions on
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[8] F. Liu, L. Jiang and C. Chen, “Low complexity MMSE vector precoding
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[10] M. Mohaisen, H. Bing, and K.H. Chang, “Fixed-complexity vector perturbation with block diagonalization for MU-MIMO systems,” in Proc.
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[11] B. Hassibi and H. Vikalo, “On sphere decoding algorithm. I. expectedcomplexity,” IEEE Transactions on Signal Processing, vol. 53, no. 8,
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winner.org/deliverables.html, Sep. 2007.

C ONCLUSIONS

We have proposed a method to reduce the symbol error
caused by a modulo operator for RCI-VP using dual regularization parameters of RCI precoding matrix. The proposed
method utilizes the RCI precoding matrix with regularization
parameter α1 which can maximize the SINR of each stream
to find the optimal perturbation vector. For the RCI precoding
matrix to pre-cancel the interference among the streams, the
RCI-VP uses the RCI precoding matrix with regularization
parameter α2 which can minimize the total MSE between
the transmitted perturbation vector and received perturbation
vector. The simulated results confirm that the proposed method
can improve the performance of RCI-VP especially for QPSK
modulation over i.i.d. MIMO channel and frequency-selective
fading MIMO channel.

Yafei Hou received his B.S. degree in Electronic
Engineering from Anhui University of Technology
and Science, China, in 1999 and M.S. degree in
Computer Science from Wuhan University, China,
in 2002, respectively. He received the Ph.D. degrees
from Fudan University, China and Kochi Universityof Technology (KUT), Japan, as an international
student of cooperation between KUT and Fudan
University in 2007. From August 2007 to September
2010, He was a post-doctoral reseach fellow at the
Research Center for Information and Communication
Systems, Ryukoku University, Japan. Since Oct. 2010, He has been a researchscientist at ATR Wave Engineering Laboratories, Japan. His main areas ofinterest are communication systems, wireless networks, ad-hoc networks andsignal processing. He is the member of IEICE and IEEE.

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ICACT Transactions on Advanced Communications Technology(TACT)Vol.
Vol.2,
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368

Satoshi Tsukamoto received his B. E. degree inelectronics engineering from Tokyo Denki University(TDU) in 1991 and BLA degree from the Open
University of Japan (OUJ) in 2005. From 1997 to
2005, he worked in Cybernetics Technology Co.,
Ltd., Tokyo, Japan, and involved in many projects
for developments of experimental systems and system prototypes for wireless communications. Since
November 2005, He has been a senior researcher
at Wave Engineering Laboratories of ATR, Japan.
He is a committee member of commission C, Japan
National Committee of URSI, and is also a member of IEICE and IEEE.

Kazuto Yano received a B.E. degree in electricaland electronic engineering, M.S. and Ph.D. degrees
in communications and computer engineering from
Kyoto University in 2000, 2002, and 2005, respectively. He was a research fellow of the Japan Society
for the Promotion Science (JSPS) from 2004 to
2006. In 2006, he joined the Advanced Telecommunications Research Institute International (ATR).
Currently, he is a senior research scientist of the
Wave Engineering Laboratories, ATR. His researchinterests include space-time signal processing forinterference suppression, MIMO transmission, and PHY/MAC cross-layer
design of cognitive radio for ISM bands. He received the IEEE VTS Japan
2001 Researcher’s Encouragement Award, the IEICE Young Researcher’sAward in 2007, the Ericsson Young Scientist Award 2007 and the IEICE 2007
Active Research Award in Radio Communication Systems. He also received
2010 Young Investigator Award in Software Radio from IEICE TechnicalCommittee on Software Radio. He is a member of IEICE and IEEE.

Masahiro Uno received the B.E. and M.E. degrees
in electronics engineering from Sophia University,
Tokyo, Japan, in 1988 and 1990. He joined Sony
Corporation, Tokyo, Japan, in 1990, where he wasinvolved in the research and development of wirelesscommunication systems. From Oct. 2010 to Mar.
2013, he has been with Advanced Telecommunication Research Institute, International (ATR) in Kyoto,
Japan as the header of Wireless Communication Systems Department, Wave Engineering Laboratories.
From Apr. 2013, he is in Device Solution BusinessGroup, Sony Corporation. His research interests are digital and analog signal
processing technologies for wireless communication systems. He is a memberof IEICE and IEEE.

Kiyoshi Kobayashi received the B.E., M.E. and
Ph.D. degrees from Tokyo University of Science,
Japan, in 1987, 1989 and 2004, respectively. He
joined NTT Radio Communication Systems Laboratories in 1989. Currently, he is the director of
ATR Wave Engineering Laboratories at Advanced
Telecommunications Research Institute International,
where he is engaged in research on advanced technologies for wireless communications. He is a member of IEEE, AIAA and IEICE.

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Copyright ⓒ 2013 GiRI (Global IT Research Institute)