Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 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

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 3 Issue. 6

1Frequency Offset Estimation for Satellite Communications with Adaptive Frame Averaging

6J

533

Julian Webber, Masanori Yofune, Kazuto Yano, Naoya Kukutsu, Kiyoshi Kobayashi and Tomoaki Kumagai
Advanced Telecommunications Research International, 2-2-2, Hikaridai, Seika-cho, Kyoto, 619-0288, Japan

2

Radio Propagation Characteristics in the Large City and LTE protection from STL interference

542

YoungKeun Yoon, JongHo Kim, MyoungWon Jung, YoungJun ChongRadio Technology Research Department, ETRI, Republic of Korea

3

Vertical Handoff Decision Algorithm in Heterogeneous Wireless Network Based on Queuing Theory

550

Yong Sun*,***Beijing Key Laboratory of Network System Architecture and Convergence**Beijing University of Posts and Telecommunications, Beijing, China 100876

4

A Real-time Rectification using an Adaptive Binary Encoding for High-resolution Video
Jong-hak Kim*, Jung-kyun Oh**, Seong-muk Kang***, Jun-dong Cho****Department of IT convergence, Sungkyunkwan University, Korea**Department of Human ICT convergence, Sungkyunkwan University, Korea***Department of Electrical and Computer Engineering, Sungkyunkwan University, Korea

557

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

533

Frequency Offset Estimation for Satellite
Communications with Adaptive Frame Averaging
Julian Webber, Masanori Yofune, Kazuto Yano, Naoya Kukutsu, Kiyoshi Kobayashi and Tomoaki Kumagai
Advanced Telecommunications Research International, 2-2-2, Hikaridai, Seika-cho, Kyoto, 619-0288, Japan{jwebber, yofune masanori, kzyano, kukutsu, kumagai}@atr.jp
Abstract—The modified Luise and Reggiannini (L&R) algorithm is one of the frequency offset estimation algorithms suitablefor use with the Digital Video Broadcasting - Satellite (DVBS2) standard. Recently we demonstrated an enhanced polypolarization multiplexing (EPPM) system incorporating L&R
frequency recovery as a hardware prototype in order to evaluate
its high spectral-efficiency in a real satellite channel. In order toprovide sufficient performance at low SNR, it is recommended toaverage the correlation estimates over 2048 frames. In high SNR
regions however, such a large averaging size is unnecessary. In
this paper, two techniques are proposed in order to reduce theaveraging size. The first technique measures the average noise
power and selects an efficient frame averaging length using anoise look-up-table (LUT). The second technique uses a cyclic
redundancy check (CRC) to determine if sufficient performanceis achievable with the averaging size. Performance results showthat the size of the averaging window can be reduced whilst
maintaining a target BER. The noise LUT adaptive scheme has
been implemented in hardware and we describe the real-time
behavior.
Keywords—satellite communications, polarization multiplexing,
frequency offset estimation, latency reduction, performance investigation, hardware implementation.

I. I NTRODUCTION
High spectral efficiency is especially important in satellite
communications due to the constraints of limited transmit
power and fixed-bandwidth whilst facing ever-increasing demands on data throughput. The recent DVB-S2X standards
Manuscript received Oct 31, 2014. This work is supported by Japan Ministryof Internal Affairs and Communications with the fund of “Research andDevelopment of Capacity Enhancing Technology for Satellite Communications
by Employing Dynamic Polarization and Frequency Control.
J. Webber is with the Wave Engineering Laboratory, Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0288, Japan.
(Tel: +81-774951313; email: jwebber@atr.jp).
M. Yofune is with the Wave Engineering Laboratory, Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0288, Japan.
(email: yofune masanori@atr.jp).
K. Yano is with the Wave Engineering Laboratory, Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0288, Japan.
(email: kzyano@atr.jp).
N. Kukutsu is with the Wave Engineering Laboratory, Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0288, Japan.
(email: kukutsu@atr.jp).
K. Kobayashi was with the Wave Engineering Laboratory, Advanced
Telecommunications Research Institute International (ATR), Kyoto, 619-0288,
Japan. He is now with NTT Access Network Service Systems Laboratories,
1-1, Hikarinooka, Yokosuka, Japan. (email: kobayashi.kiyoshi@lab.ntt.co.jp).
T. Kumagai is with the Wave Engineering Laboratory, Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0288, Japan.
(email: kumagai@atr.jp).

extension [1] has brought a number of new design features that
increase the spectral efficiency. Increasing the modulation order on each polarization is a common technique to increase thethroughput. Although multiple-input multiple-output (MIMO)
techniques have revolutionized the mobile communications
space, it is less straight-forward to apply them to satellite
systems due to the very strong line-of-site channel. In thiswork, the base-system uses a concept of multiple polarizations,and we focus on the system frequency recovery.
Most modern transponders operate a scheme called orthogonal polarization multiplexing (OPM) in which they simultaneously transmit on vertical (V) and horizontal (H), or lefthandand righthand circular, polarizations and achieve sufficientisolation. Multiplexing more than two signals onto the V andH channels creates interference that cannot be removed bya linear spatial filter as the number of independent paths islimited to two. However, by appropriately selecting the signal
constellations at the transmitter and applying powerful digital
signal processing techniques at the receiver, it is possible torecover the signals and achieve significant efficiency gains
compared to the conventional OPM system.
In general, stricter requirements are made on the phase andfrequency recovery algorithms as the modulation order increases. Improved frequency and phase accuracy are therefore
becoming important issues with extensions to 256-APSK modulation listed in DVB-S2X and also in our multi-polarization
system. Estimation performance can be improved by increasing
the UW length and increasing the frame averaging size usedin the phase estimation. Here we investigate an adaptivearchitecture that reduces the frame averaging size while aiming
to maintain error performances.
There have been a number of research proposals relating
to the optimization of correlation based frequency recovery
algorithms. A technique to optimize the correlation length
depending on the frequency offset was proposed for the L&R
in [2]. In this paper we aim to optimize the frame averaging
size, L. We first proposed the adaptive-L by noise estimation(herein termed ALNE) architecture in [3] and we extend thatwork to describe the hardware performance. In addition, we
propose an alternative technique called adaptive-L by CRC(ALC).
This paper is organized as follows. The EPPM technique
is briefly introduced in Section II. The frequency estimationand correction method is described in Section III. The ALNE
and ALC architectures are detailed in Section IV and V
respectively. A software performance investigation is presented

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

UW insertion

Linear mapping
P1
P2

SH

MOD

R[2]

R[3]

R[4]

R[N]

+

+
UWV

RF

…

Pm

MOD

R[1]

z

coarse

…

S/P

…

PN

SV

MOD

c (UW )

V

534

LC

L

∑R
l =1

+

l

BER D NUW

∑R
l =1

l

tan −1 (⋅)π ( N + 1)

fine

L

H
UWH

tan −1 (⋅)π ( N + 1)
Noise
Power
Est.

RF

LUT

σ V2 (t) + σ H2 (t)Fig. 2. Block diagram of the frequency recovery showing the additionalarchitecture for noise power estimation with LUT for L.
Fig. 1.

EPPM transmitter architecture.

performance in noise [6].
in Section VI and a discussion of the hardware design in
Section VII. Finally a conclusion is drawn in Section VIII.

II.

fˆLR =1arg
πTs (N + 1)

B
∑

∑

N/2

(2)

x(i, l)x∗ (i − k, l)(3)

l=B−L+1 k=1
N∑

R(k, l) =E NHANCED P OLY-P OLARIZATION M ULTIPLEXING

1
R(k, l)
N −k

i=k+1

The enhanced poly-polarization multiplexing (EPPM) system transmitter architecture is shown in Fig. 1. In the EPPM
system, the H and V components of the n-th transmitted
symbol, TH (n), TV (n) are expressed as[

TH (n)TV (n)

]
=

]
M ([
∑
αm
m=1

βm

)
Xm (n)(1)

where Xm (n) is the m-th stream transmit data symbol to
be modulated, αm , βm are generalized complex mapping
coefficients. An optimized set of mapping coefficients arecomputed offline by searching for a constellation set thathas the maximum minimum-Euclidean distance. The enhanced
PPM scheme is a superset that includes the basic PPM scheme.
In PPM, a polarization angle between the V and H planes
determines the I-Q constellation points on the additional polarization planes. In the simulations in this paper, we transmit
on 3 data streams, corresponding to a polarization angle of
45 degrees, with QPSK modulation on each stream. Further
details of the technique are described in [4].

III.

F REQUENCY OFFSET ESTIMATION

In order to estimate the frequency offset and channel state
information (CSI), an orthogonal Gold code unique word (UW)
is inserted at the start of each frame on V and H polarizations.
The UW length is between 64 and 256 symbols is inserted
and should be minimized in order to maximize the effective
bandwidth efficiency. The target in this paper is to reduce thelength from NU W =256 to 64 symbols while maintaining theperformance.
The L&R frequency offset [5], fˆLR is estimated from theargument of the sum of correlations, R. The so-called ‘modified L&R’ computes an average correlation over L preceding
frames prior to calculating the argument and improves thewhere Ts is the symbol period, B is the current frame number,x(i, l) is the UW pilot at position i of frame l, and N is thecorrelation length, NU W /2 in this paper.
The frequency recovery consists of four estimators: a coarse
feedback loop and fine feed-forward loop on each of the V andH channels. The architecture of a single-branch is shown by
the continuous lines in Fig. 2. The optimum performance ofthe fine recovery loop is achieved by setting the correlation
length equal to half the UW size, as it achieves the CramerRao lower-bound on the estimation error [5]. The coarse loop
has a reduced length of between 2-6 symbol delays in orderto achieve a large frequency pull-in range.
A. Frame Averaging
The potential benefits of frame averaging were investigatedby calculating the average residual frequency offset as afunction of the correlation length, N and averaging length, L.
The performance of a NU W =256 system, at Eb /N0 =14 dB,
symbol-rate 1.6 MBaud and assuming a ± 3.2 kHz frequency
offset on V and H is shown in Fig. 3. It can be seen thatthe absolute residual offset can be substantially reduced byframe averaging. The residual error reduces at about the samerate with L for a given value of N . The graph shows that
it is possible to trade-off L and N in order to achieve a
required maximum residual offset. In this work however, we
restrict the value of N to half the UW length, in order tosimplify an adaptive implementation in hardware. Although
the cost of programmable logic memory is now relatively low,
it is beneficial to limit excessive values of L particularly in
conditions where the offset has high temporal variation. Thissituation can arise when the phase noise is high or there is
movement of, or within the vicinity of, the satellite userstation causing Doppler shift. In a practical system, an averagetolerable frequency offset is determined based on the particularquality of service (QoS) requirements. In the next section we
propose a method to select L adaptively.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

-41

Average residual offset∆f = ±3.2 kHz

L=2
L=4
L=8
L=16
L=32

E b/N 0 = 14 dB

10

0.5

|P i|

10

535

0

-5

-0.5

-1
5

10

15

20

25

30

i

10

-6

20

40

60

80

100

Correlation length (N)Fig. 4. The first 8 UW symbols with a constant spacing of D=4 separated
by data symbols.
TABLE I.

Fig. 3. Average normalized residual frequency offset versus correlation lengthfor different averaging lengths L.

In general, frequency estimation performance degrades
when shorter UW lengths are used as there are less symbols
used and the maximum distance between them is less. We haverecently shown that the estimation performance can be improved by increasing the distance between the UW constituent
symbols for a given UW length [7]. This technique improves
the estimation performance at low values of L and enables thebenefits of the adaptive architectures to be further exploitedby short UW lengths. The distribution of UW symbols on Vpolarization with a constant separation of D=4 are shown in
Fig. 4. The data symbols are sandwiched between the UW
symbols and the remainder are placed together after the last
UW symbol. In the receiver, the distributed UW symbols arerepacked into a continuous memory array of length NU W . The
frequency estimate is then obtained by dividing the standard
L&R estimate with the spacing, D. The variable D is a systemlevel parameter that needs to be known at both the transmitterand receiver. Therefore it is set at the start-of-packet andremains constant for the entire transmission.

IV.

A DAPTIVE -L BY N OISE E STIMATION (ALNE)

The frame averaging length is selected based on the average
estimated noise power jointly measured on the V and H channels. The basic modified L&R algorithm forms the architecturebasis. The additional components comprise a noise estimation
process and a look-up table (LUT) containing appropriate
averaging lengths. The modifications are indicated by thedashed lines in Fig. 2.
The normalized noise power on each branch, σV2 /H is estimated by subtracting the known transmitted signal component
from that of the received signal plus noise. An average value iscalculated across N pilots and a sliding-window of W frames

Lower boundUpper bound

L

0

0.18

1.00

4096

1

0.12

0.18

1024

2

0.06

0.12

512

3

0.04

0.06

256

4

0.028

0.04

128

5

0.02

0.028

64

6

0.012

0.02

16

7

0.00

0.012

4

Index

B. Distributed Unique Word structure

ALNE L OOK - UP - TABLE CONTENTS .

asσV2 (t) =

1
WN

t
∑

N
∑

|rV (n, m)|2 − |rV (n, m)x∗V (m)|2n=t−W m=1

(4)
2
σH
(t) =

1
WN

t
∑

N
∑

|rH (n, m)|2 − |rH (n, m)x∗H (m)|2n=t−W m=1(5)where, xV and xH are the transmitted UW on V and H
polarizations, rV and rH are the received UW signals on V
and H polarizations. An average of the two branches iscomputed asσV2 H (t) =1 2
2(σ (t) + σH
(t))
2 V

(6)

The average noise power indexes a LUT containing valuesof L. The default LUT contains eight entries for the noiseboundaries and the associated values for L, as listed in Table I
and plotted in Fig. 5. The size of L is a power of two, to makeefficient use of reserved memory. Noise boundaries should be
pre-computed off-line through a study of BER performance
simulations with different L. The LUT can be expanded if
more optimal values are determined and can be updated at
run-time via a GUI interface.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

10

l=1

0

l=2

536

l=L

Upper
Lower

Boundary

CRC
10

-1

Lset(1) Lset(2) Lset(Np)
LMAX

10

-2

0

1

2

3

4

5

6

7

L=64

L

L=16

L fixedFig. 7. ALC packet structure showing (top) CRC symbol appended after
L frames (bottom) NL blocks with decreasing value of L followed bytransmission with fixed L

Index
Fig. 5.

Start

Plot of the ALNE look-up-table boundaries.

Set: n=0, D=~8,
Lset= {LMAX, 64, 16}
n=n+1
L = Lset(n)Decode LC frames.
CRCf (frame)=1, if fail.
N
Fig. 6.
Calculating the moving average correlation R̄ with example of
updating the value of L at frame 1024.

n=NL?
Y

At start-up, the memory containing the correlation values is
empty and hence the procedure to update L is delayed until
the memory buffer is sufficiently full. An example showing
the memory structure for the first 1536 frames and associatedcorrelation computations is shown in Fig. 6. The correlation
average, R̄1023 , is computed from frames 0 to 1023 inclusive.
After determining the new value of L to be 512, the window
is updated to cover frames 513 to 1024. The correlations
computed at frames 514 to 1025 are used to calculate theaverage correlation for frame 1025, and so on.
V.

A DAPTIVE -L BY CRC (ALC)

In this second architecture, the Tx appends a cyclic redundancy check (CRC) symbol to the end of each frame asshown in Fig. 7. A value of D is set at both the Tx and Rx
and is held constant throughout the transmission. At the Rx,

Select min. L
with min. fails
Fig. 8.

ALC flowchart for determining the value of L.

a set of L descending values is specified before the start ofpacket reception, e.g. Lset = {LM AX , 64, 16}, where LM AX ,is the maximum value of L to be tested (Fig. 8). It can be set
according to the satellite frequency band, D, or MCS. If thechannel has a low SNR or a high modulation scheme is used,
LM AX is set high e.g. 768. If the channel has high SNR or ahigh value of D is set, LM AX can be reduced e.g. 128.
After LM AX frames have been received, a frequency
estimate is made by averaging the L correlation values.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

SIMULATION PROPERTIES.

UW codeUW length (NU W )Data length
Modulation
Freq. offset
Frequency recovery
Baud rate
FEC
ChannelOrthogonal Gold64, 2562304 symbols / stream
3-streams @ QPSK
±2,4 kHz
Modified L&R
1.6M, 3.2M
None, LDPC (R=5/6)
AWGN

10

BER

TABLE II.

10

10

The l-th frame data is then demodulated and RxCRC (l)obtained. T xCRC (l) is then compared with RxCRC (l). If
RxCRC (l) ̸= T xCRC (l), a CRC fail variable Fail(n) is incremented. Next, T xCRC (l+1) is compared with RxCRC (l+1).
Then n is incremented to 1 and the Rx next sets L=Lset (1). As
Lset (1) < Lset (0) the averaging process, and hence frequency
offset estimation, can actually be done using the correlation
contents already held in memory and thus without any wastefulretransmissions. The smallest value of L that passes the CRC
test is finally selected.

10

-2

L=1
L=2
L=4
L=8
L=16
L=32
L=64
L=128
L=256
L=512
L=1024
L=2048

-4

-6

-8

8

UW

10

11

12

13

Fig. 9. BER performance of 3 stream × QPSK, NU W =64 with UW symbol
spacing D=1.

-2

L=1
L=2
L=4
L=8
L=16
L=32
L=64
L=128
L=256
L=512
L=1024
L=2048BER

S OFTWARE S IMULATION

A performance evaluation was conducted for the EPPM
system using the parameters shown in Table II. Random PN
data of length 2 bits × 2304 symbols was generated for eachof three streams. The stream data was mapped onto the twopolarizations using EPPM modulation and a UW preamble
inserted of length NU W . The data packet was convolved with aroot raised cosine filter having roll-off factor α=0.5. A ± 4 kHz
frequency offset was applied to V & H. At the receiver,
AWGN is added and the signal matched filtered and downsampled. The UW sections are extracted and the frequency
offsets corrected on both V and H-channels. The noise levelis averaged over 32 symbols and indexes the LUT. The data
was estimated by MLD processing. The BER measurements
started after 2048 frames (i.e. the correlation memory was full
for all values of L) and the first value of L had been selectedfrom the LUT.
The BER performance for NU W =64 is plotted in Fig. 9 andshows that when the UW length is short and the UW symbols
are adjacent to each other (D=1), the performance is degradedby selecting small values of L. As Eb /N0 increases however, L
can be reduced whilst maintaining a given BER. By increasing
the UW symbol spacing to D=8, the performances for lowvalues of L are substantially improved as shown in Fig. 10. At
the BER 1E-4, it can be seen that L can be reduced from 2048
to 128, with a negligeable loss in performance. The BER
performance for NU W =256 is plotted in Fig. 11. It can beseen that L can be set to 128 with little performance loss
compared to the maximum value.
The required value of Eb /N0 to achieve an error rate of 1E4, as L increases for a fixed value of D, is plotted in Fig. 12. A
plateau shows the region where there is little additional benefit
from increasing the value of L further. It can be seen that9

3 stream × QPSK
∆f = ± 4 kHz
FEC-off
UW sym. spacing, D=1
N=64

Eb/No (dB)

10

VI.

537

10

10

10

-4

-6

-8

8

9

3 stream × QPSK
∆f = ± 4 kHz
FEC-off
UW sym. spacing, D=8
N
=64
UW

10

11

12

13

Eb/No (dB)
Fig. 10. BER performance of 3 stream × QPSK, NU W =64 with UW symbol
spacing D=8.

L=512 is an efficient setting for the case {NU W =64, D=2}.
In the case of {NU W =256, D=1} and {NU W =64, D=4,8},
L can be set to 64 without any observable degradation in
performance.
The reduction in the size of L compared to a fixed valueof 1024 is shown in Fig. 13 for the case of NU W =64. Withsymbol spacing D=2, the averaging length can be reduced
by about 75% whilst maintaining the target BER at 1E-4. In
the case of LDPC with D=4, L can be optimally set to 512
at 4.8 dB and decreased to 32 at 6.0 dB. The optimized settings
depend on the particular modulation coding scheme (MCS) anda specific LUT for each MCS should be investigated as partof our future work.
A BER simulation was set-up to investigate the ALC behavior. Three simulations with a different value of D={8,16,32}
were conducted. At the transmitter, EPPM 6-bit modulation

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

538

BER

10

10

10

10

-2

L=1
L=2
L=4
L=8
L=16
L=32
L=64
L=128
L=256
L=512
L=1024
L=2048-4-6-8

3 stream × QPSK
∆f = ± 4 kHz
FEC-off
UW sym. spacing, D=1
N
=256
UW

% of maximum L at target BER

100
D=1
D=2
D=4
D=8

80
N

60

=64

UW

FEC-Off
40

20

0

Eb/No (dB)1213
Eb / No (dB)

Fig. 11. BER performance of 3 stream × QPSK, NU W =256 with UW
symbol spacing D=1.

Fig. 13. Percentage in size of L relative to fixing L=1024 whilst maintaining
a target BER of 1E-4 for NU W =64 without LDPC.

8

14

10

11

12

13

11

14

-1

10

3 stream × QPSK
∆f = ± 4 kHz
FEC-off

UW256 D=1
UW64, D=1
UW64, D=2
UW64, D=4
UW64, D=8

13

D=8
D=16
D=32

-2

10

-3

10

12.5

BER

Eb/N0 for BER 1E-4 (dB)

13.5

9

12

-4

10
11.5

3 stream × QPSK
∆f = ± 2 kHz
LDPC R=5/6
N
=32
UW

-5

10

11
10.5
0
10

-6
1

2

10

10

3

10

10
2

4

L
Fig. 12. Required Eb /N0 to achieve an uncoded BER of 1E-4 as L increases.

with coding rate was R=5/6 was applied. At the receiver, a± 2 kHz offset was added. The frame averaging size wasdetermined by the adaptive algorithm which selects a valueof L that achieves the desired lowest sum of CRC fails.
Without, the ALC control, the significantly different D for a
fixed value of L would result in different BER. However, theerror graph shows that each transmission had almost identical
performance (Fig. 14). When a low value of D was used, alarger value of L was selected automatically in compensation.
Both ALC and ALNE use different techniques to achieve thesame aim. In terms of complexity, the ALNE is preferred as thenoise estimation requires relatively few hardware multiplications and has no CRC overhead. Further, the noise estimation
may already be required and computed by the LDPC block.
The ALC could however be considered if a CRC module isalready part of the system design.

6

8

10

Eb/N0 (dB)
Fig. 14.

ALC error performance for D={8,16,32} with ± 2 kHz offset.

VII.

H ARDWARE I MPLEMENTATION

Each transceiver board consists of five Xilinx FPGAs withthe frequency recovery and signal processing operations computed on a Virtex XC6SLX100 device. The symbol rate isvariable between 0.05-10 MBaud, the input sampling rate
is 102.4 MHz. The ADC and DAC precisions were 12-bits
and 16-bits respectively. The transmitter and receiver settingsare entered via a GUI on the respective Tx and Rx PCs.
The required Eb /N0 , frequency and phase offsets were set on
a SLE900 satellite channel emulator. The transceiver system
is shown in Fig. 15 and further details of the design are
in [8]. The BER is computed by comparing the transmitted
and received signals using an Anritsu MP8931A BER tester.
The estimated frequency offset on each polarization as
measured in hardware is plotted in Fig. 16. The value of
D=12 and L was determined by LUT. The estimated offset

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

539

4

10

ControlGUI

SNR set in Channel Emulator

3

12
dB

A2 A3

B2 B3

14
dB

16
dB

18
dB

C2 C3

D2 D3

20
dB

Trend indecreasing
L as noise
decreases

L

10

10
dB

TRX

E2

2

10

E3

ChannelEmulator

A1

BERT

Offset Estimate (Hz)Fig. 15. Satellite testbed system comprising Tx, channel emulator, Rx and
BER tester.

Offset Estimate (Hz)

O

1

10

0

B1

A0

2

4

C1

D1

B0

C0

6

8

Frame

E1

D0

F2 F3
F1

E0

10

F0

12

14
4

x 10

Fig. 17. Variation of L versus frame as SNR increases from ALNE hardware
experiment.

2100∆f = +2 kHz

2050
2000
1950
1900
200

400

600

800

1000

1200

Frame

-1900

∆f = -2 kHz

-1950
-2000
-2050
-2100
200

400

600

800

1000

1200

Frame

Fig. 16. Frequency offset estimate in hardware in response to a ± 2 kHz offset
set by the channel emulator (top) H-polarization, (bottom) V-polarization.

was within ± 8 Hz of the value set in the channel emulator
with 10 dB SNR.
A. Adaptive-L by Noise Estimation
The LUT in hardware was verified by examining frame
versus index results data. The SNR on the SLE channel
emulator was varied from 10 dB to 20 dB in steps of 2 dB.
The frame number at which the index changed is written to
file and from this the variation of L with frame number isplotted in Fig. 17. After switching on the machine, the LUT
is filled with correlation data. At position O an SNR of 10 dB
is set with the addition switch off in the channel emulator andhence L reverts to the low value of 16. At position A1 , noise
addition is switched on and quickly the value of L changes
to 1024 at postion A2 . It stays at this position for about 6000
frames. It briefly reduces to 512 but then returns to 1024. At
position A3 , the noise is switched off and the value of L rapidly

reduces to 16. A similar process is repeated for the remaining
SNR values. That is, the SNR is set to {12,14,16,18,20} dB
at positions {B1 ,C1 ,D1 ,E1 ,F1 } respectively. The respective
noise is added until positions {B3 ,C3 ,D3 ,E3 ,F3 } respectively.
Two further general observations can be made from thisfigure. First, there is a downward trend in L observed as
the SNR increases and confirms the desired behavior. The
exact SNR at which the value of L changes depends on the
LUT boundary entries and whose determination is an off-line
optimization task to achieve a desired performance. Second,occasionaly (e.g. D2 to D3 ) there are oscillations between twovalues of L. When L is large, the estimation performance ishigh and the estimation is very accurate. A lower value of
L is subsequently selected by the algorithm. The estimation
performance degrades slightly and subsequently a higher valueof L is required. To avoid the averaging size being updated too
frequently, control circuitry should manage the case when themeasured noise power oscillates across a LUT boundary. Thissituation becomes more important when the LUT is small andthus there are relatively large steps of L.
Various techniques can be applied to control the oscillatory
behavior. A hysteresis can be set so that the new setting of L
only becomes valid if the boundary is crossed for a sufficientnumber of frames. Alternatively, the update rate for L can be
set to once per a given number of frames. The optimization canalso achieved by adjusting the boundary positions or changing
the frame averaging LUT entries. This optimization together
with the hysteresis should be added as a future upgrade to thefirmware. A software simulation with {D=12, L=256} wascompared with hardware results for D=12 with L determined
by LUT. There is a close match between the software andhardware performances as shown in Fig. 18. LUT-B achieves
slightly better performance than LUT-A due to a larger valueof L being used.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

-1

10
D=12, LUT-A
D=12, LUT-B
D=12, L=256 (SW)

-2

10

BER

-3

10

-4

10

-5

10
10

3 stream × QPSK
∆f = ± 2 kHz
f = 3.2 MBds
N UW =64, FEC-off
11

12

13

14

15

16

540

[5] M. Luise and R. Reggiannini, “Carrier frequency recovery in all-digitalmodems for burst-mode transmissions,” IEEE Trans. Commun., vol. 43,
no. 2/3/4, pp. 1169–1178, Feb-Apr. 1995.
[6] ETSI “Digital Video Broadcasting (DVB) User guidelines for the second
generation system for broadcasting, interactive services, news gatheringand other broadband satellite applications (DVB-S2),” ETSI TechnicalDocument TR 102 376 v1.1.1, Sophia Antipolis, Feb. 2005.
[7] J. Webber, M. Yofune, K. Yano, H. Ban, and K. Kobayashi, “Performanceof frequency recovery algorithms for a poly-polarization multiplexing
satellite system,” IEEE Malaysia International Conference on Communications (MICC’13), Malaysia, Nov. 2013.
[8] J. Webber, M. Yofune, K. Yano, H. Ban, and K. Kobayashi, “Experimental evaluation of a poly-polarization multiplexing system with timing/frequency recovery for satellite communications,” AIAA International
Communications Satellite Systems Conference (ICSSC’13), Florence,
Italy, Oct. 2013.

17

SNR (dB)
Fig. 18. BER of ALNE in hardware for uncoded 6-bit EPPM with ± 2 kHz
offset, 3.2 MBaud, D=12.

VIII. C ONCLUSION
We have proposed two adaptive techniques to reduce theframe averaging required in satellite frequency offset estimation. These techniques are called adaptive-L by noise estimation (ALNE) and adaptive-L by CRC (ALC). In ALNE, thewindow-size for frame averaging was reduced by selecting it
based on the estimated noise power on both V and H branches.
A BER of 1E-4 could be maintained with a reduction in L
of 75% for D=2 with UW length 64 at 11 dB Eb /N0 . The particular algorithm selection partly depends on implementation
complexity and whether CRC and noise estimation modules
are already used in other parts of the system. Further work
should optimize the performance by adding a hysteresis to
avoid excessive switching between boundaries and optimize
the averaging sizes for the LUT across a range of MCS andchannel conditions.

Julian WEBBER received the M.Eng. and Ph.D.
degrees from the University of Bristol, UK in 1996
and 2004 respectively. From 1996-98, he was with
Texas Instruments, Europe engaged on ASIC and
DSP systems. From 2001-07 he was employed asa Research Fellow at Bristol University engaged
in real-time MIMO-OFDM testbed implementation,and from 2007-12 he was a Research Fellow at
Hokkaido University, Sapporo, Japan, working on
MIMO signal processing & wireless communications. He is currently a researcher at ATR, Kyoto,
Japan principally working on frequency recovery and spectrally efficientmodulation techniques for satellite communications. His other current researchinterests include MIMO, high resolution direction of arrival estimation andM2M. He is a member of the IEEE and IEICE.

ACKNOWLEDGMENT
This work is supported by Japan Ministry of Internal Affairs and Communications with the fund of “Research andDevelopment of Capacity Enhancing Technology for Satellite
Communications by Employing Dynamic Polarization andFrequency Control.”R EFERENCES[1] DVB Project Office, “DVB-S2X - S2 extensions: Second generation
satellite extensions,” DVB Fact Sheet, May. 2014.
[2] F. Gong, G. Shang and K. Peng, “Initial estimation based adaptive
carrier recovery scheme for DVB-S2 system,” IEEE Transactions on
Broadcasting, Vol. 58, No. 4, Dec. 2012.
[3] J. Webber, M. Yofune, K. Yano, H. Ban, K. Kukutsu and K. Kobayashi,“Adaptive frequency offset estimation for practical satellite communication channels,” IEEE International Conference on Advanced Communications Technology, Korea, 16-19th Feb. 2014.
[4] M. Yofune, J. Webber, K. Yano, H. Ban, and K. Kobayashi, “Optimization of signal design for poly-polarization multiplexing in satellite
communications,” IEEE Commun. Letters, pp. 2017–2020, vol. 17, no.
11, 2013.

Masanori Yofune received the B.E. and M.S. degrees in systems engineering from Hiroshima City
University, Hiroshima, Japan, in 2008 and 2010, respectively. In 2010, he joined Mobile Techno Corporation, Kawasaki, Japan, where he has been engaged
in research and development on wireless communication systems. In 2012, he was assigned to ATR
Wave Engineering Laboratories as a researcher andengaged in research and development on advanced
techniques for frequency efficiency in satellite communication systems. His interest areas are wirelesscommunication systems and digital signal processing. He is a regular memberof the Institute of Electronics, Information and Communication Engineers(IEICE) of Japan.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

Kazuto Yano received a B.E. degree in electrical andelectronic engineering, M.S. and Ph.D. degrees incommunications 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 research interests include
space-time signal processing for interference 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’s Award in 2007, theEricsson Young Scientist Award 2007 and the IEICE 2007 Active ResearchAward in Radio Communication Systems. He also received 2010 Young
Investigator Award in Software Radio from IEICE Technical Committee on
Software Radio. He is a member of IEEE and IEICE.

541

Tomoaki Kumagai received the B.E. and M.E.
degrees in Electrical and Communication Engineering, and Ph.D. degree in Information Science fromTohoku University, Sendai, Japan, in 1990, 1992
and 2008, respectively. Since joining NTT in 1992,
he has been engaged in research and developmentof personal communication systems and high-speed
wireless LAN systems. Since 2014, he is the Director of Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International(ATR). He received the Young Engineer Award fromthe Institute of Electronics, Information and Communication Engineers (IEICE) in 1999. He is a member of the Institute of Electronics, Information andCommunication Engineers (IEICE) and IEEE.

Naoya Kukutsu received the B.E., M.E., and D.E.
degrees in electrical engineering from Hokkaido University, Sapporo, Japan, in 1986, 1988 and 1991,
respectively. His D.E. dissertation described research
for a time-domain electromagnetic wave numericalanalysis method. In 1991, he joined the Nippon
Telegraph and Telephone Corporation (NTT), Applied Electronics Laboratories, and was engaged indeveloping high speed IC packages. From 20082013, he was a senior research engineer, supervisor
at NTT Microsystem Integration Laboratories, andleader of the group that develops millimeter-wave and terahertz-wave radiotransmission, as well as imaging systems. He is currently a senior researchengineer at ATR Wave Engineering Laboratories and head of the departmentof Environment Communications. Dr. Kukutsu is a member of the IEEE MTT
and COM Societies and a senior member of the Institute of Electronics,
Information and Communication Engineers (IEICE).

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. Since then, he has been engaged
in the research and development of digital signal
processing algorithms and their implementation techniques including modem, synchronization control
and diversity for satellite and personal wireless communication systems. From 2011 to 2014, he was the
director of ATR Wave Engineering Laboratories at
Advanced Telecommunications Research Institute International, where he wasengaged in research on advanced technologies for wireless communications.
Currently, he is a senior research engineer, supervisor and a group leaderof Satellite Communication Group in NTT Access Network Service Systems
Laboratories, working on development of satellite communication systems. He
is a member of IEEE and IEICE.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

542

Radio Propagation Characteristics in the Large
City and LTE protection from STL interference
YoungKeun Yoon, JongHo Kim, MyoungWon Jung, YoungJun ChongRadio Technology Research Department, ETRI, Republic of Korea
ykyoon@etri.re.kr, jonghkim@etri.re.kr, mwjung@etri.re.kr, yjchong@etri.re.kr

Abstract— This paper describes composite propagation
effects of both the median pathloss and shadow fading in thelarge city. In general, the propagation characteristic for the
power profile of received signal has the relative power reduction
and fluctuation according to distance to the receiver’s location
far away from the transmitter. The result of these channel
propagation losses is due to the reduction of electric field in the
air according to the separated distance between a transmitterand receiver, and due to the multipath effect such as the planereflection and edge diffraction of the propagated radio signals at
the plane of buildings and roads or vehicles etc. Propagatedmultipath radio signals via different paths may besimultaneously received to a target receiver with different fieldstrength and phases. Actually, it is not easy to how to predict the
optimum radio propagation characteristics in the large city froma transmitter to target receiver at the specific mobile or fixedcommunication link, because the outdoor environment have thedifferent height of transceivers and various structure factors
such as buildings, roads, and vehicles in the large city including
the urban, suburban and rural area. Therefore, themeasurement is currently candidate solution for predicting theradio propagation characteristics in the novel environments. In
this paper, two different stations between a fixed and mobile
station are considered. Measurement results are compared withthe general radio propagation model of 3GHz frequency andlower. As another issue, we discuss and propose on the closed
form of the interfering received signal strength intensity when
two different stations like a fixed system as the repeater and amobile system as Long Term Evolution coexist in the same area.
In the coexisting environment, two different stations overlap thecell coverage and are assigned in the adjacent channel frequency.
Therefore, the potential interference may cause to their
reception each other. Here, it is important to analyze theinterference impact to the victim station as a receiver from theinterfering station as a transmitter to get out of the interference.
In this paper, it is assumed that an interfering station as atransmitter is fixed repeater and a victim station as a receiver isa mobile Long Term Evolution system. For estimating———————————————————————
Manuscript received November 1, 2014. This work was supported by the
ICT R&D program of MSIP/KCA. [2013-005-036-001, Wireless Channel
and Frequency Characterization based on Field Measurements for
Broadband Mobile Hot-Spot Applications]
Y. Yoon is with radio technology research department, Electronics andTelecommunications Research Institute, Daejeon, Republic of Korea (email:ykyoon@etri.re.kr)
J. Kim is with radio technology research department, Electronics andTelecommunications Research Institute, Daejeon, Republic of Korea (email:jonghkim@etri.re.kr)
M. Jung is with radio technology research department, Electronics andTelecommunications Research Institute, Daejeon, Republic of Korea (email:mwjung@etri.re.kr)
Y. Chong is with radio technology research department, Electronics andTelecommunications Research Institute, Daejeon, Republic of Korea (email:yjchong@etri.re.kr)

interfering received signal strength intensity, it is issued tofigure out a radio propagation characteristic. However, it isunknown for the available median pathloss model to apply for
analyzing the performance of Long Term Evolution system frominterfering fixed repeater in the large city with none line of sightin the coexisting environment. Finally, we propose the medianpathloss characteristic from measuring the electric fieldstrength of a fixed repeater as an interfering transmitter andcalculated interfering received signal strength intensity to a
victim station. Using both the measurement and the theoretic
calculation results, the separated distance between aninterfering transmitter and victim receiver is discussed for theprotection of a victim station.
Keyword— interference, wave, pathloss, fading

I. INTRODUCTION
ONG

Term Evolution (LTE) has been developing to

Ltransport increasing traffics of mobile communication
data [1]. Studio transmitter link (STL) station does transport aradio station’s audio data from the broadcast studio to arepeater in another location. The transmitter studio link (TSL)
is the return link, which transports telemetry data fromremotely located a repeater back to the broadcast studio. STL
and TSL station are a kind of the fixed microwave link station.
Figure 1 shows the frequency allocation and arrangement
between STL and LTE uplink bands. Two stations operateeach other in adjacent frequency bands. Let us consider the
potential LTE interference impact from STL station’s
emission at 1.7GHz frequency bands in the coexisting caseboth LTE base station and STL system in the same service
area.
In this situation, the analysis and its results on the potential
LTE performance impact due to the interference of STL
station in the large city were not known to us, yet. Therefore,
we tried to derive a theoretic LTE interference impact fromthe harmful power of STL station in the adjacent frequency
bands.

Fig. 1.

Frequency assignment of both STL and LTE uplink.

To calculate interfering STL and desired LTE station’s
received signal strength intensity to the LTE reception, let us
consider theoretic median pathloss and shadowing model,
because interfering or wanted transmitter power is reduced by
the separated distance between a transmitter and a victim
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

receiver. In addition, radio propagation characteristic varies
to the outdoor environment factors such as building, ground,and etc.
In this paper, general radio propagation models in thevarious environments like urban, suburban, and open area areconsidered. Extended Hata model is general and feasible
median pathloss model within lower than 3GHz frequency
bands in the mobile communication service. Extended Hata
model is used to calculate pathloss according to the separated
distance in none line of sight (NLOS) as well as line of sight(LOS) environment [2]. In the free space environment case,reference ITU-R P.525 model is available to calculate themedian pathloss between a wanted transmitter and desiredreceiver in open area [2]. In referred [1]-[2], using medianpathloss and long term fading characteristics with log-normal
distribution, we can calculate available cell area
corresponding to the minimum sensitivity level of desiredreceiver. If referred general model of [1]-[2] are always goodin agreement with measurement results, we could use
theoretic median pathloss model and shadowing value
without waste time for long measurement process anddatabase analysis. However, composite median pathloss andshadowing characteristics according to separated distance inthe large city including dense urban and suburban
environment was not known to us. Therefore, we measured
radio propagation characteristics in the large city and foundvarious median path loss characteristics, when STL stationtransmits the audio data from the broadcast studio to arepeater in another location. Currently, the median path loss
value getting from the referred theoretic model [1]-[2] is not agood match with our measurement results in the specificenvironment like the large city including the dense urban andsuburban, because theoretic median path loss model is very
general model in the specific site environment and is not good
agreement with all environments. Also, a choice of the medianpathloss model for the link budge calculation on thetransceiver station is not difficult. For example, mobile
operators choose Extended Hata model within lower than
3GHz frequency bands for calculating the site coverage inmacro or micro or hotspot environment. In the other hands,
TV broadcasters use the reference ITU-R P. 1546 model inthe point to multi point environment with time varying andspatial rates of the radio signal profile [3]. However, thissimple choice may happen a large error in the specificenvironment like the large city including the dense urban andsuburban. This error may happen to inaccurate the medianpathloss calculation and we could have faults calculating aninterference impact to the LTE reception in the adjacentfrequency bands from an interfering STL signal power.
Actually, for the link budge calculation, the mobile LTE
operators use Extended Hata model in the LOS and NLOS
environment in the urban or suburban. The other fixed STL
microwave broadcasters use the free space loss model in the
open area and the reference ITU-R P. 1546 model in the LOS
and NLOS environment over the coverage of 1km and higher.
This is the reason that each mobile or fixed station’senvironment has different conditions, which they include thetransmitter antenna height, the receiver antenna height, thetransmission distance, and the receiver sensitivity, etc.
In this paper, we talks to derive the composite median path543

loss and shadowing value for the LTE protection from theinterference impact of interfering STL transmitter. As an
approach, we measured the transmission power of STL station
during moving with a vehicular from the STL broadcast
studio to a repeater in another location. The measurement
frequency is at 1.7GHz bands and it is within in-bands of LTE
bands at the adjacent STL frequency bands. As the fixed STL
microwave broadcasters, we considered two stations withdifferent link paths in the large city. One STL station is Far
East Broadcasting Company (FEBC) and, while the other isSeoul Broadcasting Station (SBS).
Finally, we found to shall be used composite medianpathloss with shadow fading to correspond to various urban,
suburban, and rural environments in the large area.
Measurement results are in good match with the results ofcomposite different median pathloss models of various urban
and suburban.
II. STATION PARAMETERS
To calculate interfering transmitter and desired LTE
received signal strength intensity to a LTE reception, let us
consider fixed STL with wireless services as interferingtransmitter.
Fixed wireless service utilizes a transmission tower similar
with a cell phone tower on the top of the building. The
equipment installed upon transmission tower communicateswith one of a repeater for the wireless transmission of theinformation data. STL station as a transmitter station means akind of the transmission tower providing the fixed service anddoes transport a radio station’s audio and video data from thebroadcast studio to a repeater in another location. A repeater
as a receiver installs the transceiver equipment on the top ofmountain to communicate with a fixed wireless broadcast
studio on the top of buildings in the city. A repeater and thebroadcast studio include a grid meshed or dish shaped antennaconnected to the radio transceiver. The antenna beam of thebroadcast studio should be faced to the pointing direction of arepeater for LOS access to reduce pathloss between atransmitter and receiver. This is the reason that the
obstructions of a hill and several high buildings can adversely
affect to the performance impact of the quality of service(QoS). As the fixed STL microwave broadcasters operating at
1.7GHz frequency bands [4]-[7], we considered two stations
with different link paths in the large city. One STL station is
FEBC and, while the other is SBS. The STL transmitter sends
the radio station’s audio analogue or digital data from thebroadcast studio transmitter to the radio transmitter operating
as a receiver in another location. STL radio links can beanalogue or digital type and considered broadcast auxiliary
services (BAS). Table 1 shows system parameters of STL
stations and these values are assumed.
SBS is digital type and FEBC’s station is the older
analogue system. Two STL stations operate in the frequencyof 1.7GHz bands with the channel bandwidth of 230 kHz.
Antenna type is the same grid parabola with 26dBi including
the feeder loss of 2dB. The antenna height (sea level) of the
FEBC and SBS transmit station is 35m or 70m, relatively. The
antenna height (sea level) of receiving station is the sameheight of 625m. Receiver is located in the tower of on the top

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014of Mt. Gwanaksan. The antenna 3dB beamwidth of STL
stations is 6.5degree in the elevation axis as shown in Figure
2.
Table 2 shows system parameters of LTE station and these544

as shown in Figure 3.

TABLE ISTL STATION PARAMETERS [4] –[7]Parameters
Analogue/Digital
Channel bandwidth (㎑)Transmit power (W)Operating frequency (㎓)Antenna gain (dBi)
Antenna HPBW (degree)
Antenna height (m)FEBC(TFT8300)
Analogue
230
5.0
1.7GHz bands

SBS(SL9003)
Digital230
1.0
1.7GHz bands

26(grid parabola)
6.5(elevation)
Transmitter 35
Receiver 625

26(grid parabola)
6.5(elevation)
Transmitter 70
Receiver 625

TABLE IILTE STATION PARAMETERS [8]Parameters

ValuesUnits

Downlink peak rates
Uplink peak rates
Transfer latency
Scalable carrier channel bandwidth
Duplexing
Antenna gain

100
50
<5
1.4~20FDD
15
7.0(elevation)
-3

Mbps
Mbps
msec

Antenna HPBW
Antenna downtilt

Fig. 2.

㎒dBi
degreedegreeSTL station’s antenna elevation pattern (e.g. grid parabola) [4].

values are assumed.
LTE base station supports moving mobiles, and multi-castas well as broadcasting data. We assumed the channel
bandwidth of 5MHz x 2 supporting the frequency divisionmultiplexing (FDD). LTE stations operate in the frequency of
1.7GHz bands. Antenna type is the linear antenna withx-polarization and with 15dBi gain including the feeder lossof 3dB. Antenna height (sea level) of LTE base station is 15m.
LTE base station is a receiver in the uplink. The antenna 3dB
beam-width of LTE station is 7.0degree in the elevation axis

Fig. 3.

LTE base station’s antenna elevation pattern (e.g. Linear) [4].

III. MEASUREMENTS
A. Measurement Path
We assumed coexisting scenarios between STL and LTE
station in the large city. The antenna height (sea level) of
FEBC and SBS transmit station is 35m or 70m as shown in the
Table 1, relatively. The antenna height (sea level) of LTE
base station is 15m in the Table 2. STL transmit stations are
assumed as the interfering transmitters and LTE base station
is assumed as a victim receiver to protect from the interfering
STL transmit stations. To derive the interfering received
signal strength intensity to LTE base station receiver, we tried
to find the available median pathloss model in the case of boththe transmitter antenna height of 35m and higher, and thereceiver antenna height of 15m and higher. However, wedidn’t look for the available radio propagation characteristics.
Finally, we measured the median pathloss characteristics andcalculated interfering received signal strength intensity to a
victim LTE base station. Using both measured and theoretic
calculation results, the separated distance between the victim
station and interfering station is discussed for the protectionof a victim station. Figure 4 shows the STL link (Link 1) of
FEBC transceiver between the broadcasting transmit station
on ground and receiving station on the top of Mt. Gwanaksan.
The measurement of FEBC transmit power was performed
within the measurement region 1 during moving the car in thelarge city. Figure 5 shows the STL link (Link 2) of SBS
transceiver between the broadcasting transmit station on
ground and receiving station on the top of Mt. Gwanaksan.
The measurement of the transmit power of SBS was
performed within the measurement region 2 during moving
the car in the large city. The distance of Link 1 and Link 2 in astraight line between the transmit antenna pointing of thebroadcasting transmit station and receiver antenna pointing of
the receiver is about 10km and 12.4km, respectively.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

545

IV. RESULTS & ANALYSIS
A. General Median Path loss Models
For comparing measurement with theoretic analysis results,
we looked for the general candidate median pathloss model.
[2], [4]In this model, for line of sight in the open area, the freespace model is used.

(1)

Fig. 4.

Measurement route I (e.g. FEBC tansmit station to a receiver on Mt.
Gwanaksan) [4].

Fig. 5.

Measurement route II (e.g. SBS transmit station to a receiver on Mt.
Gwanaksan) [4].

B. Measurement Environment
Figure 6 shows the measurement scenario using a vehicularin order to measure the median pathloss and long term fading
characteristics at 1.7GHz bands in the large city.
Measurement was performed on road in the large city
including huge or small buildings and hills with moving carfrom the STL transmit station. A car with the measurement
system has been moved from the STL transmit station(Transmitter) to near by the front of the STL receiving station(Receiver) on the top of Mt. Gwanaksan. The measurement
system includes the vector network analyzer, the low noise
amplifier, the band-pass filter, and the omni antenna. The
antenna height (hv) on the top of a car is 2.5m. We defined theantenna height of STL station as follows; STL transmit
antenna height is H1 and STL receiving antenna height on the
top of Mt. Gwanaksan is H2.

The free space pathloss is proportional to the square of theseparated distance in km between the radio transmitter andradio receiver. In addition, it is proportional to the square of
the operating frequency in the unit of MHz of receiver in (1).
Free space pathloss is the minimum radio propagation loss inthe signal strength of electromagnetic wave that would result
from the unobstructed direct line of sight path through the air
in the open area. Free space pathloss is examples applied to
the fixed microwave link or broadcasting link ortelecommunication link over the short distance in the openarea, etc.
For the telecommunication link operating at 3GHz andlower bands, Extended Hata model is typically used. Extend
Hata pathloss is the radio propagation loss in the signal
strength of electromagnetic wave that would result from theobstructed none line of sight as well as the unobstructed lineof sight path through the air in the urban, suburban, and ruralarea. Extended Hata model was developed by European study
committee (COST 231). This model is an extension of
Okumura-Hata model, being applicable for the frequency to
3GHz and lower bands, with receiving antenna heights from1m to 10m and the transmitting antenna heights of 30~200m.
It is used for the prediction of pathloss for mobile wirelesssystem in urban, suburban, and rural environments.
In case of urban environment,(2)

In case of suburban environment,(3)

In case of rural environment,(4)

where,(5)(6)

where, f is frequency in MHz, d is distance between atransmitting antenna and receiving antenna in km, H1 is the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

546

transmitting antenna height above the ground level in meter,
hv is the receiving antenna height above the ground level inmeter. a(hv) is the correction factor of the receiving antennaheight. b(H1) is the correction factor of the transmitting
antenna height.
The long term fading has different variation values due toseparated distance between a transmitter and receiver in urban,
suburban, and rural environment. Shadow fading calls for thelong term fading or the slow fading, because channel
characteristics vary slowly and location between a transmitter(a) measurement vs. free space modelFig. 6.

Measurement method [4].

and receiver. The standard deviation of the long term fading(b) measurement vs. Extended Hata modelto the separation between a transmitting antenna andFig. 7. Comparison on measurement and analysis results(SBS’s RSSI in thereceiving antenna with the distance range from 0.1 to 0.2km islarge city).
17 dB in the roof below. For the distance range of 0.2km to
0.6km, the standard deviation is s.t.d = 17-20(d-0.2)dB. For
the separation distance range larger than 0.6km, there is 9dB[2], [4].
B. Results Comparison
Measurement was performed on the road in the large city
including huge or small buildings and hills with moving carfrom STL transmit station as shown in Figure 6. The received
signal strength intensity (RSSI) of SBS or FEBC STL
measured to the measurement system within the car show in
Figure 7 and Figure 8, relatively. Figure 7 shows on the RSSI
results of SBS and Figure 8 shows on the RSSI results of
FEBC on both measurement and theoretical Extended Hata
pathloss. We used the received antenna of omni directional
type. The height of the received omni antenna on the top ofroof is 2.5m on the ground level. In Figure 7 and Figure 8,
x-axis means the separated distance in a straight line between
the STL transmitting antenna and received antenna. The
maximum separated distacne of FEBC and SBS is 10 and
12.4km, respectively. But, the height of STL antenna on
ground level is enough high to install on the tower on Mt.
Gwanaksan. RSSI level is measured by enough distance to beable to approach near to the tower on Mt. Gwanaksan. For
Figure 7 and Figure 8, the maximum measurement distance of
FEBC is about 7 km out of 10km, and SBS is about 8.2km outof 12.4 km. As shown in Figure 4 and Figure 5, themeasurement region 1 in Link 1 path and measurement region
2 in Link 2 path are the area moving the car from the STLtransmitter to the front of Mt. Gwanaksan in order to measure
the RSSI level, respectively. The RSSI level according to(a) measurement vs. free space modelFig. 8.

(b) measurement vs. Extended Hata model
Comparison on measurement and analysis results (FEBC’s RSSI in
the large city).

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

separated distance from a transmitter and receiver varied dueto the around environment including multiple buildings, manycars, and the park etc.
Figure 7 and Figure 8 show different RSSI characteristics
according to distance considering the car moving path. The
reduction of RSSI level according to the distance means
pathloss value including the long term fading. Figure 7(a)shows that results of both measurement and the free spacepathloss are compared each other.
The RSSI values to receiving antenna with the antennaheight of 2.5m from the SBS transmit station with the antennaheight of 70m in the urban area are -70 ~ -100dBm in themeasurement results and -40 ~ -60dBm in the free spacepathlss results. Figure 8(a) shows that measurement andExtended Hata (urban, suburban) pathloss results arecompared each other. The RSSI values to receiving antenna
with the antenna height of 2.5m from the FEBC transmit
station with the antenna height of 35m in the urban area are-60 ~ -100dBm in the measurement results and -25 ~ -52dBm
in the free space pathlss results. Figure 7 and Figure 8 show
the long term fading characteristics.
Approximately, the fluctuation value is 10 ~ 13dB in SBS
and is 6 ~ 10dB in FEBC from the STL transmit station to the
Mt. Gwanaksan path. The standard deviation of shadowing in
COST231 model [2] provides 3 ~ 4dB ranges. In COST231,
the value has smaller variation than measurement results inthe large city including the urban and suburban environment.
This difference is due to the radio propagated environment
including the reflection and diffraction at the buildings,
obstructs, and forest etc. As shown in Figure 7 and Figure 8,
the measurement results of pathloss according to theseparated distance are good mapping with Extended Hata
urban and Extended Hata suburban pathloss except for themeasurement point of the near location from pointing station
as a transmitter, although relative shadowing variation islarger than reference COST231 value of 3 ~4dB. Finally, themedian pathloss is able to apply for estimating the reduction
characteristics of the signal power in addition to highvariation of the shadow fading with log-normal distribution in
the case of the large city at 1.7GHz bands.
C. Interference Calculation
As shown in Figure 1, the interfering STL transmit signal
cause the potential interference impact to the LTE base station
reception at the frequency of 1.7GHz bands. LTE is assumed
as the reverse FDD system. To calculate the interfering STL
signal strength intensity and desired LTE received signal
strength intensity to the LTE reception, let us consider thetheoretic median pathloss and shadowing characteristics asshown in Figure 7 and Figure 8, because the interfering andwanted transmitter power are reduced according to theseparated distance between the transmitter and receiver. We
found facts that the median pathloss and long term fading
characteristic is applied to estimate different attenuation
characteristics according to the separated distance between
the interfering transmitter of the fixed pointing station and thereception of the mobile communication station such as LTE
system in the large city as shown in Figure 7 and Figure 8.
Figure 9 shows the interference analysis scenario to the
LTE reception due to the interfering STL transmit station

Fig. 9.

547

Desired and interfered link [4].

when the LTE base station is located in straight line between
STL links (Link 1 and Link 2) as shown in Figure 4 and Figure
5, respectively. For the calculation of the interference impacts
between different systems such as the LTE receiver and STL
transmitter in this paper, we used the measurement results at
1.7GHz bands in the large city shown in Figure 7 and Figure 8.
Measurement was performed on the road using the receiverwith the antenna height of 2.5m of the omni directional
antenna type. The measurement results are calibrated on thereceiving antenna height because the antenna height ofreceiver in the measurement is 2.5m, while the antenna heightof the LTE base station is 15m. Therefore, the correction
value due to difference of the receiver antenna height between
2.5m in the car and 15m in the LTE base station is required.
We used the correction value of about 11dB based on themeasurement according to the receiver antenna height
difference.
We derived the interfering received signal strength
intensity to the LTE base station reception from the emission
power of the STL transmit station in the adjacent channel
usage. The emission power of the STL transmits stationmeans the totally interfering signal power received to the LTE
base station receiver to the out of band spectrum of STL
transmit station.
Figure 10 shows the emission characteristic of the FEBC
station and Figure 11 shows the emission characteristic of the
SBS station based on the realistic conducted power
measurement. Equation (7) is derived to calculate theinterfering received signal strength intensity to the LTE basestation reception [4].
(7)

where, iRSSI depicts the totally received interfering signal
strength intensity including the antenna gain and correction
value
in dB as the difference of the received signal
strength intensity due to the difference of between 2.5m and15m of the received antenna height,
means thereceived interfering signal strength intensity to the receptionof the LTE base station based on the measurement results asshown in Figure 7 and Figure 8. As shown in Figure 10 and
Figure 11, the out of band (ooB) emission power of the fixed
STL transmit station is potentially influenced to the system
performance to the reception of the LTE station. The totally
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

Fig. 10. FEBC Station’s Emission Characteristics [4].

Fig. 12. SBS Station’s iRSSI.

Fig. 11. SBS Station’s Emission Characteristics [4].

Fig. 13. FEBC Station’s iRSSI.

emission power is calculated as the total transmit powerreceived within the separation frequency (∆F) bands from theoffset (Fc+a) frequency to the end frequency (Fc+b) of thechannel bandwidth of 5MHz of the LTE base station.
depicts the total antenna gain of the LTE
base station including both the antenna height of the STLtransmitter and LTE base station with the antenna up or down
tilting. For example, we assumed that the LTE base station
has the down tilt of 3dB, the antenna end of the STL transmit
station is pointed to the end of the STL receiving station. Also,
the antenna gain considers the direction of the elevation angle
to derive the total antenna gain. For predicting on theinterference impact to the LTE base station reception, wemust define the permissible totally received interfering signal
strength intensity to the victim station. In this paper, apermissible interference limit is -108dBm/5MHz to the victim
system as the LTE base station. This value is derived from theprotection ratio of LTE base station. LTE protection ratio is
I/N, which the ratio the totally received interfering signal
strength intensity to noise power of the victim station such as
the LTE base station [8]-[9]. The protection ratio of I/N isdefined as - 6dB. This means that the permissible totallyreceived interfering signal strength intensity to the reception
must be less than the total noise power. Total noise power isderived as the equation (8).
(8)

where, kT depicts boltzmann constant of k and ambient
temperature of T. This kT value is about -170dBm/Hz in thegeneral communication system. B is the bandwidth of 5MHz

548

of LTE base station and NF depicts the noise figure of 5dB of
LTE base station receiver.
Therefore, the permissible totally received interfering
signal strength intensity to the victim station is-114dBm/5MHz. Figure 12 and Figure 13 show the totallyreceived interfering STL (SBS in Figure 12 and FEBC in
Figure 13, respectively) signal strength intensity to the LTE
base station when the receiving antenna height of the roof top
to the measurement vehicular is 2.5m and the receiving
antenna height of the LTE base station 15m, respectively. To
limit the received interfering signal strength intensity, theseparation distance is required between the STL transmit
station as the interfering transmitter and the LTE base station
as the victim station. If the permissible totally received
interfering signal strength intensity to the reception is-114dBm/5MHz, the satisfied distance between the SBS
station and LTE base station is 2.2km over in Figure 12 andthe satisficed distance between the FEBC station and LTE
base station is 2km over in Figure 13.
V. CONCLUSION
This paper described to derive composite median path loss
and shadowing value for the protection of the LTE basestation from the interference impact due to STL transmitter.
As an approach, we measured the STL’s transmission power
during moving with a vehicular from the STL’s broadcast
studio to the STL receiver in another location. As the fixed
STL microwave broadcasters, we considered two STL
stations with different link paths in the large city. One STL
station is FEBC and, while the other is SBS. Finally, we foundto should be used the composite median pathloss with longterm fading corresponding to the various urban and suburban
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014environments in the large area. Measurement results are ingood match with the results of composite different medianpath loss. In addition, to calculate the interfering STL signal
strength intensity and desired LTE received signal strength
intensity to the LTE reception, we considered the theoretic
median pathloss and shadowing characteristics. For
predicting the interference impact to the LTE base station
reception, we defined the permissible totally received
interfering signal strength intensity to the victim station. If thepermissible totally received interfering signal strength
intensity to the reception is -114dBm/5MHz, the satisficed
distance between the SBS transmit station and the LTE basestation is 2.2km over and the satisficed distance between
FEBC transmit station and the LTE base station is 2km over.
For the coexistence between the STL station and LTE basestation, it is expected to use the required minimum separation
distance. And, the antenna correction factor is very importantvalue to predict the radio propagation phenomenon such as
the pathloss and shadowing for high accuracy and is available
to calculate the interference impact in the adjacent channel
bands.
ACKNOWLEDGMENT
This work was supported by the ICT R&D program of
MSIP/KCA. [2013-005-036-001, Wireless Channel and
Frequency Characterization based on Field Measurements for
Broadband Mobile Hot-Spot Applications]549

environments
JongHo Kim received B.S, M.S, and PhD degrees inelectronic engineering from Chungnam National
University, Daejeon, Korea, in 1986, 1988, and 2006,
respectively. Since 1989, he has been working for
ETRI, Daejeon, Korea, where he is a principal
member of the engineering staff of the RadioTechnology Department. His main interests are radio
propagation and spectrum engineering.
MyoungWon Jung received B.S, M.S, and PhD
degrees in electronic engineering from Chungnam
National University, Korea, in 2006, 2008, and 2014,
respectively. Since 2009, he has been working for
ETRI, where he is a senior member of research staff ofthe radio technology department. His main interests
are radio propagation study for mobile
communication and millimeter wave propagation
study in indoor and outdoor environments.
YoungJun Chong received the B.S degree from the
Jeju University, Jeju island, Korea, in 1992, and the
M.S degree in electronics engineering in 1994 from
Sogang University, PhD degree in electronicengineering from Chungnam National University,
Korea, in 2005, respectively. Since 1994, he has beenwith ETRI, Korea, where he is a leader of spectrum
engineering section and principle member of theresearch staff of the radio technology department. He
is currently involved in the development of the digital ultra-narrow band
walky-talky. His research interests include RF circuit and stations
development

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[3] Method for point-to-area predictions for terrestrial services in thefrequency range 30 MHz to 3 000 MHz, ITU Radio Communication
Sector(ITU-R), REC. ITU-R P.1546, Oct. 2009.
[4] Y. Yoon, J. Kim, M. Jung, and Y. Chong, “Radio propagation
characteristics in the large city,” in Proc. 16th International Conference
on Advanced Communication Technology 2014, vol.1, no.1, Feb.
2014, pp.558-562.
[5] Telecommunication chapter I—Federal Communications Commission
subchapter C—part 73—broadcast radio services, CFR Title 47,
Federal Communications Commission (FCC).
[6] http://www.tftinc.com/stlproducts.html.
[7] http://www.moseleysb.com/mb/starlink_family.html.
[8] Long Term Evolution (LTE): a technical overview, Motorola, 2007.
[9] Derivation of a block edge mask (BEM) for terminal stations in the
2.6GHz frequency bands (2055-2690MHz), European Conference ofPostal and Telecommunications Administration (CEPT),ECC Report131, Feb. 2009.
[10] Y. Shim, I. Lee, and S. Park, “The impact of LTE UE on audio
devices,” Electronics and Telecommunication Research Institute
Journal, vol.35, no.2, Apr. 2013, pp.332-335.

YoungKeun Yoon received B.S, M.S, and PhD
degrees in radio engineering from National Chungbuk
University, Korea, in 1997, 1999, and 2007,
respectively. Since 2000, he has been working
Electronics and Telecommunications ResearchInstitute (ETRI). He has been involved in the researchof radio resource management and propagation since2003. His main interests are radio propagation andspectrum engineering study in indoor and outdoor

Copyright ⓒ 2014 GiRI (Global IT Research Institute)ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

557

A Real-time Rectification using an Adaptive
Binary Encoding for High-resolution Video
Jong-hak Kim*, Jung-kyun Oh**, Seong-muk Kang***, Jun-dong Cho****Department of IT convergence, Sungkyunkwan University, Korea**Department of Human ICT convergence, Sungkyunkwan University, Korea***Department of Electrical and Computer Engineering, Sungkyunkwan University, Korea
jhakkim@vada.skku.ac.kr, jkoh@vada.skku.ac.kr, smkang@vada.skku.ac.kr, jdcho@skku.edu

Abstract—Previously, various rectification methods using
compressed lookup table have been studied for real-time
hardware stereo vision system. These loss compression methods
may occur distortion that could corrupt disparity estimation
process. Differentially encoded lookup table method which islossless compression has no distortion with reasonable
compression ratio. However, the method is limited to
low-resolution and low warping movement. In this paper, we
propose an adaptive binary encoding method. Our proposed
algorithm has approximately 10~26% compression ratio whichis comparable to the previous method and tolerates
high-resolution video.

geometry matrix as in [2]–[4]. And a hardware architectureusing FPGA (Field-programmable gate array) is also used toachieve real-time processing as in [5]–[7]. However,
simplified calculation methods are not suitable for real-timeprocessing. And FPGA methods could process in real-time,
but they consist of complex hardware architecture.
On the other hand, pre-calculation using a LUT has fast

Keyword—real-time processing, high-resolution rectification,
lookup-table, adaptive binary encoding, lossless compression

I. INTRODUCTIONASTEREO vision system has four steps. The first step isrectification as a pre-processing. A role of rectification
is to align epipolar lines. And the second step is disparity
estimation to obtain disparity-map. In the third step, filtering
techniques are utilized to enhance disparity-map. Finally,
disparity-map is used for various applications such as visual
fatigue decrease, object segmentation and tracking, motion
control and etc. Since rectification not only aligns epipolarlines but also decreases search-range for disparity estimation
process, it is essential process to achieve real-time processing.
Figure 1 shows reduced search-range.
Rectification could categorize into real-time calculationand pre-calculation using a LUT (Lookup table). Since
real-time calculation consist a large amount of computation,
many research tried to simplify calculation of epipolar———————————————————————
Manuscript received September 4, 2014. Followings are results of a study
on the “Leaders in Industry-university Cooperation” Project, supported by
the Ministry of Education, Science & Technology (MEST). This research
was supported by Basic Science Research Program through the National
Research Foundation of Korea(NRF) funded by the Ministry of
Education(NRF-2013R1A1A2058942)
Jong-hak Kim is with the department of IT convergence, Sungkyunkwan
University, Suwon, Gyeonggi-do 440-746, Korea (phone : 031-290-7200,
e-mail: jhakkim@vada.skku.ac.kr).
Jung-kyun Oh is with the department of Human ICT convergence,
Sungkyunkwan University, Suwon, Gyeonggi-do 440-746, Korea (e-mail:jkoh@vada.skku.ac.kr).
Seong-muk Kang and Jun-dong Cho are with the Department ofElectrical and Computer Engineering, Sungkyunkwan University, Suwon,
Gyeonggi-do 440-746, Korea ( e-mail: smkang@vada.skku.ac.kr;
jdcho@skku.edu).

Fig. 1. Example of reduced search-range[1].

processing-time with compact hardware architecture as in [8].
But disadvantage of this method is high memory
consumption. To overcome this issue, loss and losslesscompression techniques are applied. One of the loss
compression methods subsamples rectification mapping data
and reconstructs using interpolation as in [9], [10]. Although
the loss compression method has much higher compression
ratio than the lossless compression method, it also has
distortion, and could be critical for the performance of
disparity estimation process. To avoid distortion, reference[11] uses differential encoding and decoding using only
subtraction and accumulation that consumes only 1.3Mbytes
for a 1280 by 720 image with 27% compression ratio.
Another recent research using the run-length encoding
technique with different order as in [12] shows highperformance. However, these methods limit data magnitude
bits, so data magnitude have to be less than 3 for differential
coding and 1 for run-length coding which are not suitable forhigh-resolution rectification.
In this paper, we propose an adaptive differential encoding
method. We analysed probability of appearance of
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

differential values and built two codebooks for highperformance and resolution including two and four modes.
The first mode in the both codebooks is comprised ofminimum bit size to decrease compression ratio, and thefourth mode in the codebook with four modes could extent bit
size for magnitude to tolerate high-resolution video.
The rest of paper is organized as follows. We will
introduce basic concept of rectification using LUT in Section
II. Our proposed algorithm will be presented in Section III.
Proposed hardware architecture will be presented in Section
IV. Experimental result will be given in Section V, and finally,
we will draw the conclusion in Section VI.
II. BASIC CONCEPT OF RECTIFICATION USING LUT
Rectification is used for various applications such as
ortho-photo or ortho-image as in [13], [14], multi-view
camera system as in [6], [15], intelligent vehicle using stereo
vision system as in [16], [17] and etc. Difference between
stereo vision system and others is that position of eachcamera is fixed, which needs only one set of rectification
parameters if there is no individual movement of each camera
and uses it consistently. Therefore, we are allowed topre-calculate rectification mapping data and rectify images
by loading mapping data from LUT memory.

558

compressed. Compressed LUT methods are simply added
encoding and decoding parts before and after LUT memory,
respectively. And complexity increase depends on which
compression method is utilized. Compressed LUT methods
classify loss and lossless compression. Figure 2 shows
overall flow of rectification using LUT
A. Loss compression
The loss compressed LUT method usually applies
down-sampling to reduce a size of LUT in the encoding
process and interpolation to fill void pixels in the decoding
process as in [9], [10]. Given that interpolation change
calculated values based on relationship with adjacent values,a rectified image is distorted. Although rectified image using
a loss compressed LUT shows reasonable PSNR over 30 dB,
it could occur critical errors in the disparity estimation
process. To avoid potential errors, the lossless compression ismore suitable.
B. Lossless compression
The lossless compressed LUT method utilizes losslesscompression technique such as Huffman, differential,
run-length and etc. Among them, a differential encoding
method is used with high compression ratio and compact
hardware architecture as in [11]. It shows good trade-off
between complexity and compression ratio. However, since itlimits differential magnitude of sequential pixel values on 3
to minimize memory consumption, an error is occurred on
the current coordinate if magnitude of differential value ismore than 4. Moreover, when one error is occurred, it ispropagated on the current line by accumulation of erroneous
difference. Therefore, the previous differential encoding
method operates properly only with low warping movement.

Fig. 2. Overall flow of rectification using LUT

Rectification using LUT could divide into calibration andrectification procedures. The calibration procedure is tocalculate rectification movement using intrinsic parameters,
extrinsic parameters and camera calibration factors andobtain rectification mapping data. The mapping data includes
coordinate information that moves pixels on the original
image to the rectified image. This procedure processes insoftware environment. And the rectification procedurecompensates input images using mapping data from the LUT
memory. Since complex computation is mostly calculated in
the calibration procedure, the rectification procedure could bedesigned compact hardware architecture and be suitable forreal-time processing. However, there are four different
mapping data set which are X and Y coordinate informationfrom left and right images. And it affects high memory
consumption. Therefore, each mapping data set ought to be

Fig. 3. Distribution of difference values (a) differential values of X coordinate (b)differential values of Y coordinate (c) ratio between the highest probability caseand others.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

III. PROPOSED ALGORITHM
Adaptive compression method basically uses characteristicof input. Reference [18] adjusted sampling frequency dependon frequency of ECG (electrocardiogram) signal. Especially,
ECG signal with low frequency sampled with low sampling
frequency, which decrease amount of data. In the samemanner, our proposed method adjusted size of memory bits tostore coordinate data depend on magnitude of differential
values. Size of memory bits is minimized to decrease
compression ratio for the highest probability case and also
maximized to have enough bit size to store maximum
magnitude for the opposite case. Therefore, compression
ratio is decreased with increasing the highest probability
case.
As shown in figure 3(a) and 3(b), variance of differential
coordinate values from 50 stereo image sets is distributed
around 1, 0 for X and Y coordinate, respectively. Moreover,as shown in figure 3(c), since the ratio between the highest
probability case and others increases with higher resolution,
our proposed method is more suitable for higher resolution.
To satisfy above-mentioned conditions, we composed twocodebooks as shown in table 1.

559

magnitude. However, the codebook II can encode -5 that isincluded in mode ‘10’. Sign is minus and magnitude is 5. We
can assign mode bit as ‘10’, sign bit as ‘1’, magnitude bit as‘10’. Therefore, -5 is encoded as ‘10110’. Our proposed
method chooses a proper codebook based on magnitude.
Selected codebook is stored to the codebook table register inthe decoder. The encoding flow is shown in figure 4.

TABLE ITWO CODEBOOKS FOR ADAPTIVE BINARY ENCODING
Codebook I
Range ofmagnitude

1 for x axis
0 for y axis

-1, 0 ,2, 3 for x axis
-2, -1, 1, 2 for y axis

Mode bit

01

Sign bit-

1 bit

Magnitude
bit

-

1 bit

Total bit

1 bit

3 bitsCodebook II
Range ofmagnitude0±1~ ±2±3~ ±6±7~

Mode bit

00

01

10

11

Sign bit

-

1 bit

1 bit

1 bit

Magnitude
bit

-

1bit

2 bits3 bits~Total bit

2 bits4bits

5 bits6 bits~

The codebook I is used for small magnitude, which
consists of two modes. The first mode is for the highest
probability case; 1 for x axis, 0 for y axis. And the second
mode encodes rest of cases. T he codebook II is used for large
magnitude, which consists of four modes. The difference valuethat has the highest probability case sets as mode ‘00’ todecrease compression ratio. And to cover larger range ofmagnitude, the codebook could rearrange by expansion ofmagnitude bit in mode ‘11’.
Let’s assume that we have differential value 0 and -5 in yaxis. First, 0 is able to encode with two codebooks. In thecodebook I, 0 in the y axis is in the mode ‘0’. And mode ‘0’
has no sign bit and magnitude bit. Therefore, 0 is encoded as‘0’. On the other hands, in the codebook II, 0 is included inmode ‘00’. And mode ‘00’ is the same as mode ‘0’ in thecodebook I. Therefore, 0 is encoded as ‘00’. Second, -5cannot be encoded using the codebook I due to large

Fig. 4. Encoding flow

IV. PROPOSED HARDWARE ARCHITECTURE
Our proposed hardware architecture consists of LUT
memory, a decoder and a pixel address generator. First, thedecoder reads differentially encoded data from LUT memoryand decode into X and Y coordinate values. Then the pixel
address generator reconstruct X and Y coordinate values into
pixel address on the original image. The decoder consists ofdata controller and codebook table registers and the pixel
address generator consists of one accumulator for X
coordinate, one accumulators and one multiplier for Y
coordinate and one adder to combine X and Y coordinate
values. Overall hardware architecture is compact with low
latency and simple computation, which is suitable forreal-time processing.
Decoding flow is processed as followings. First, each data
controller obtains the information about which codebook isused and reads encoded data from LUT memory. Second,
data is decoded from the binary codes to the differential
values using the stored codebook. Lastly, we accumulatedifferential values to decode into coordinate values, andgenerate pixel addresses. The proposed hardware architect isshown in fig. 5.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014560

and 50 stereo image sets. As shown in figure 7, compression
ratio of our proposed method has minimum 18.9% andmaximum 26.1% lower than the previous one. And
difference of compression ratio in the same resolution
becomes larger with higher resolution.

Fig. 7. Compression ratio results with various resolutions
Fig. 5. Proposed hardware architecture

V. EXPERIMENTAL RESULT
Our proposed method is designed using OpenCV for theencoding process and Verilog for the decoding process. For
the evaluation, we applied two methods. The first one is abinary image of difference to show distortion. The equation
for binarization is as:
0, 𝑅𝑒𝑐𝑡𝑖𝑚𝑔 (𝑥, 𝑦) = 𝑂𝑟𝑖𝑖𝑚𝑔 (𝑥, 𝑦),B(x, y) = {(1)
1, 𝑅𝑒𝑐𝑡𝑖𝑚𝑔 (𝑥, 𝑦) = 𝑂𝑟𝑖𝑖𝑚𝑔 (𝑥, 𝑦).
Since distortion is the same as difference, distortion
regions represent with white pixels in the result images. As
shown in figure 6, the result images of the loss compression
method have white regions, whereas our proposed method
does not.

As shown in figure 8, differential encoding shows only
31.69% compression ratio regardless of stereo environment
due to fixed encoding bits. On the other hands, our proposed
methods show different results as per stereo environment,
since it has different encoding bits depend on modes. And for
performance, our proposed methods have approximately
two-third and one-third lower compression ratio with thecodebook II and the codebook I, respectively.

Fig. 8. Compression ratio results with 50 stereo image sets

VI. CONCLUSION

Fig. 6. Differential binary image

The second method is compression ratio. Compression
ratio (CR) is defined as:
𝐶𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑒𝑑 𝑑𝑎𝑡𝑎
CR =(2)
𝑈𝑛𝑐𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑒𝑑 𝑑𝑎𝑡𝑎

We compared to previous differential encoding method asin [11] and two codebooks with various resolution conditions

In this paper, we proposed an adaptive differential
encoding method for high-resolution video. Our proposed
method effectively encodes satisfying two conditions such as
minimization to decrease compression ratio andmaximization to tolerate large magnitude values. And
decoding process also achieves compact hardware
architecture with processing in real-time. Compression ratio
is approximately from 10.8% to 26.1% depending on
resolution with no distortion. And the higher resolution
shows the lower compression ratio which is suitable forimage processing with high-resolution.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 6, November 2014

Jung-kyun Oh received the B.S degree inInformation Communication Engineering from
Myongji University, Yongin, Korea, in 2009.
He is a M.S candidate in the Human ICT
Convergence Department at Sungkyunkwan
University, Suwon, Korea.
His research interests include SoC for 3D Image
Processing Applications and 3D visual fatigue

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Reconfigurable Computing and FPGAs, pp. 37-42, Dec, 2008.
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272-277, Oct, 2013.
[13] Ayman F. Habib, Eui-Myoung Kim and Chang-Jae Kim, “New
Methodologies for True Orthophoto Generation,” PhotogrammetricEngineering & Remote Sensing, Vol. 73, No. 1, pp. 25-36, Jan, 2007.
[14] W. Karel, M. Doneus, G. Verhoeven, C. Briese, C. Ressl, N. Pfeifer,“Oriental – Automatic Geo-referencing and Ortho-rectification ofArchaeological Aerial Photographs,” ISPRS Annals of the
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561

[1]

reduction.
Seong-muk Kang received the B.S degreefrom the Department information
communication from Baekseok University,
Korea, in 2013. And he is studying for amaster’s degree in Electrical Engineering at
Sungkyunkwan University, Suwon, Korea.
His current research interests include image
stabilization and stereo vision.
Jun-Dong Cho received the B.S. degree from the
Department
ofElectronic
Engineering,
Sungkyunkwan University, Suwon, Korea, in 1980,
the M.S. degree from the Department of Computer
Science, Polytechnic University Brooklyn, New
York, in 1989, and the Ph.D. degree from the
Department of Computer Science, Northwestern
University, Evanston, in 1993.
He was working for Samsung Electronics Company
for 6 years. He joined the Department of Electrical and Computer
Engineering, Sungkyunkwan University (SKKU), Suwon, Korea, since
1995, where he is currently chair of graduate school of Human ICT
Convergence.
His research interests include Low Power Design, 3-D Image Processor,
Embedded Multimedia, and Human ICT Application.
Prof. Cho is an IEEE Senior Member.

Jong-hak Kim received the B.S. degree in radio

communicationengineeringfromtheKyunghee University, Suwon, Korea, in 2009,
the M.S. degree from the Department ofElectrical and Computer Engineering,
Sungkyunkwan University, in 2012, and he isstudying for a Ph. D degree at Sungkyunkwan
University.
He is interested in the efficient low power hardware implementation
for a real-time image processing system in mobile equipment. He
currently studies a visual fatigue reduction scheme for a stereo
vision system.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)