Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
Dr. Chun-Hsin Wang, Chung Hua University, Taiwan
Prof. Wilaiporn Lee, King Mongkut’s University of Technology North, Thailand
Dr. Zhi-Qiang Yao, XiangTan University, China
Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
Prof. Yeonseung Ryu, Myongji University, Korea
Dr. Kyuchang Kang, ETRI, Korea
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Prof. CheonShik Kim, Sejong University, Korea
Dr. Anna bruno, University of Salento, Italy
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Dr. Saba Mahmood, Air University Islamabad Pakistan, Pakistan
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Mr. Jose Gutierrez, Aalborg University, Denmark

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Mr. Anup Thapa, Chosun University, korea
Dr. Vo Nguyen Quoc Bao, Posts and Telecommunications Institute of Technology, Vietnam
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Dr. Jin REN, North china university of technology, China
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Dr. Mohamed M. A. Moustafa, Arab Information Union (AIU), Egypt
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Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
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. 2

1

Globalization and ICT Innovation Policy: Absorption Capacity in developing Countries

6J

409

Ayesha SALEEM, Kiyohide HIGUCHI
Graduate School of Global Information and Telecommunication Studies, Waseda University, Tokyo, Japan

2

Robust Digital Image Cryptosystem Based on Nonlinear Dynamics of Compound Sine and Cosine Chaotic Maps forPrivate Data Protection

418

Sarun Maksuanpan, Tanachard Veerawadtanapong, Wimol San-Um
Intelligent Electronics Systems Research Laboratory Faculty of Engineering, Thai-Nichi Institute of Technology, Patthanakarn Rd., Suanlaung,
Bangkok, Thailand

3

DVB-RCS: Efficiently Quantized Turbo Decoder
Sherif Welsen Shaker
University of Nottingham, Ningbo Campus, P.R. China

426

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

409

Globalization and ICT Innovation Policy:
Absorption Capacity in developing Countries
Ayesha SALEEM, Kiyohide HIGUCHI
Graduate School of Global Information and Telecommunication Studies, Waseda University, Tokyo, Japan
dua@fuji.waseda.jp, higuchi@waseda.jp

Abstract— This paper is about the new role of academic
institutions in the economic development of developing countries.
Educational institutes are significant in propelling economic
development as they are the powerful drivers, technology centres,developers and investors. Universities can affect the economic
growth of developing countries. The purpose of this new role ofacademic institutions is to touch virtually every aspect of daily
lives and the prosperity of the communities living in rural areas.
At a larger scale it aims to initiate a process of policy learning,exchange between countries in different stages of economic
development and knowledge sharing with their universities
research students. The study identifies the current technology
innovation in the field of education and analyzes the case ofdeveloping and developed countries. To demonstrate, we do an
in-depth study of Pakistan’s education system.
This paper explores strategic decision by using gametheoretical analysis. The paper constructs a game model subject topreferential policy between Government and Universities. It
offers three games that give the overview of the role of thegovernment to promote the quality of education. The paper finds
the equilibrium of the game under three specific conditions. The
result shows that better policies lead to quality education that
foster the development of the country. It clearly shows that having
an autonomous body to regulate the education policies canpromote the innovation and technology adoption. This study is
two-fold; it provides the insight of university-government
interaction as well universities’ interaction among themselves.

areas (74%) than in rural areas (49%), Public spending on
education as percentage of GDP is 2.1 %. According to Labour
Force Survey of 2010-11, 44.9 % of the total workforce is
employed in agriculture sector, 13.02 % in manufacturing,
6.62% in Construction, 5.23% in Transport, 13.66 % in services
and 0.1 % in others. Whatever progress Pakistan has made,most of its gain is not shared in the rural area. In rural area,
structural changes and improvement in factor of production isvery low. Human Development Indicators are also low. [1][2]Most of the rural areas of Pakistan are similar with respect totheir infra structure, economy and resources and are diverse innature at the same time by their socio cultural conditions. Rural
areas are generally backward in every aspect. There is a need toeducate people, improve their healthcare services and to create
job opportunities; there are many fields to develop like aconsiderable number of developmental project are being
implemented by World Bank, ADB and NGOs. However all
such projects have adopted the progressive approach fordevelopment which takes time. If we follow the same patternthat has been adopted by the OECD countries then it will takethe same time which they took to be in the present position.
They follow the same traditional pattern. If we start to educate
all the children from a village, it will take 30 years for thatvillage for all children to be educated and use their knowledge
for the growth of their village. The other point is to provide
health care services which also have a long term affect on theeconomic development.

Keyword— ICT, Innovation Policy, Role of Universities, ICT
education, Technology catch-up/transfer, Game Theory

I. INTRODUCTIONT

HIS research focus on Pakistan, officially the Islamic
Republic of Pakistan covers an area of 796,096 square
kilometres. Pakistan has a very high growth rate and is the sixth
most populous country in the world with an estimatedpopulation of 184.3 million in 2012-13. Almost 70% of the its
population (i.e. 110 million) lives in rural areas. Adult literacy
rate (10 year and above) is 58 %, literacy is higher in urban

Manuscript received September 27, 2013.
Ayesha Saleem is PhD student in Graduate School of Global Information
and Telecommunication Studies, Waseda University, Tokyo, Japan(corresponding author phone: +81-80-3240-5875;+33-758-200-164 e-mail:dua@fuji.waseda.jp; Ayesha.saleem@OECD.org).
Kiyohide Higuchi is Professor of Graduate School of Global Information
and Telecommunication Studies, Tokyo, Japan (email: higuchi@waseda.jp)

Figure1: Public Expenditure on education (as %) of GDP [2]Many Universities around the globe have started
comprehensive reviews of the curricula and introduced new
strategies to transform the university learning and teachingapproach. However, the quality of higher education in Pakistan
is way below. Pakistan has 138 Universities including six new
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

universities established in 2010-11. There is a pressing need tore-assess the current curriculum with the hope of adjusting its
standard. Many universities have regarded understanding
Information Technology (IT) and mastering the basic skills andconcepts of IT as part of the core of education, alongsidereading, writing and numeracy. In order to enhance the global
competence by universities is the latest proposal for thedeveloping countries like Pakistan.[3][4][5]Figure2: Universities/Degree Awarding Institutions in Pakistan [3]II. LITERATURE REVIEWInternationalization is not only about learning elsewhere, but
it is also about learning to solve local problems or address local
issues within an international context; or learning to addressinternational or global issues within a local context.
Globalization is constantly changing the employment prospectsof our workforce. Universities can do this by providing
employers with graduates who are not only technicallyknowledgeable, but globally competent, as well.
Now the question arise who is responsible to take initiativeand how it is possible. According to OECD share of private
expenditure on educational institution is highest in Chile,
United Kingdom, Korea, Japan and United States respectively.
It is important policy aspect to balance the share of public andprivate financing of education and currently it is an important
issue in many OECD countries.
There we discuss some challenges to overcome forpromoting the technology adoption capacity with the help of
universities.
A. Globalization and Change of Technology
The world has changed, it has become flat, more facilities areavailable, new techniques has developed. Thomas L.
Friedmans’s in his book discuss with the help of examples that
how and why globalization has now shifted into wrap drive [6].
Growth is not the process of simple replication. It reflects anever ending flow of inventions, innovations and technologicaladvancements leading to improvement in the production
possibilities. Technology and the process of production havechanged, and new products and services has introduced.
Innovation has enabled doing things in different yet more
efficient and cost effective ways.
Before 20 years, there was no computer, no concept of theinternet no search engine, telecommunication was notavailable. These technologies were not used for the economic
development. There are new conditions for future prospects,

410

hyper growth is possible, and mobility of the resources is also
possible. Use of new techniques changed the life and suddenchange towards modernization can be possible.
The author discussed the entry condition in the world market
for catching up has changed and is entirely different from 50 or
100 years ago. He grouped the countries on the basis of thepatterns of economic growth as innovator, 19th century
followers, 19th century cases of stumbling back,underdeveloped and stay behind, Learner or 20th century
followers, 20th century cases of stumbling back. Pakistan isincluded in Group IV that refers to the country“underdeveloped and staying behind”. [7][8]In the era of global competition it is recognized that changes
in technology and competition have diminished many of the
traditional roles of location. Yet clusters, or interconnectedcompanies with in the same area, are a striking feature ofvirtually every national, regional, state, and even metropolitan
economy, especially in more advanced nations. The prevalenceof clusters shows important insights about the microeconomics
of competition and the role of location in competitive
advantage. In the globalized world the old purposes forclustering have diminished, it has new impacts of clusters on
knowledge-based and dynamic economy. Clusters represent a
new way of thinking about national, state, and local economies,
and they necessitate new roles for companies, government, andother institutions in enhancing competitiveness [9].
B. Internationalization of Higher Education“Innovation involves getting new ideas accepted and newtechnologies adopted and used. The introduction of newtechnologies, methodologies or content into a university IT
course can thus be considered as an example ofinnovation.”[10].
“Internationalization of Higher Education is the process of
integrating an international/intercultural dimension into theteaching, research and service functions of the institution”.[11]With the economic rationale as a backdrop to internationalizing
the higher education, the term “internationalizing thecurriculum” is proposed as a strategy for internationalization.
Internationalization has been discussed as the relevance andimportance of student outcomes, teaching strategies andknowledge
content
andinclusive
curriculaandpedagogy.[12][13][14]
Numerous researchers emphasize the centrality of thecurriculum and the internationalization of the curriculum,
teaching and learning processes as critical elements ofinternationalization. Knight describes the curriculum as "thebackbone of the internationalization process". Other
researchers concur, emphasizing the importance of aninternationalized curriculum in providing a student-centeredlearning experience for all students and in preparing students tobe successful in today's increasingly interdependent global
society.
Indian IT industry has advantage on the other Ireland
because of focused on English language skills for engineers andhigher quantities of quality engineers. [15]Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

C. University–industry links
University links with the industry can enhance theperformance of the faculty [16]. Institutional policy regarding
University Industries links can build institutional direction as
well give clear and explicit regulations for understanding
relationships [17].
In the post war period Japan University Industry cooperation
was established. Industries contacted the universities for hiring
the skilled labors. As a result Japan industries achieved worldclass status in 1980’s. The Ministry of International Trade andIndustry (MITI) was mostly responsible for policies related tothe manufacturing sector and financially supported a number of
R & D projects.
A legal framework was established in Japan to promote
university- industry technology transfer [18][19].
In Pakistan during 1990s first time Government formulate
the trade liberalization and privatization policies. Scientists at
the Pakistan Council for Scientific and Industrial Research and
the Pakistan Atomic Energy Commission collaborated with the
Universities. But there were no collaboration between Public R&D institutions and private industry. The GDP growth rate was
not sufficient for large population growth rate [20].

411

exploitation. Also it will facilitate in commercialization of
R&D results and innovation. This includes the process
involving business strategies and techno-entrepreneur’sdevelopment that need to be implemented so as to reap best
possible returns from R&D investment. Most importantly it
will address significant issues as how to obtain financing fordevelopment of innovations via government and private
venture capital funds for the commercialization of new andindigenous technology”[22].
Therefore, there should be new role of universities. The
knowledge sharing by the universities is a way to achieve
development, because the World has changed it is not the same

III. UNIVERSITIES ROLE
In Japan the higher education sector R & D expenditure is
growing fast. University has new role in the science andtechnology policies. Universities reforms considered the partof transformation of Japanese research and innovation systemsince 1990s. Legal government structure of the national
universities changed to “corporate status” for better efficiency
and increased independence. From 2001 to 2004 educational
reform were accelerated towards economic and industrial
policy objectives [21].
The universities financial system was also reformed byintroducing the “black grant system”. The budget for theuniversities substantially increased in the past few years withthe objective of having world top level of research universities.
Ministry of Economy, Trade and Industry (MITI) in Japanproposed a plan for reforming universities as part of thenational industries policy. After that MEXT released “Toyama
Plan” for basic principles for structural reforms on Universities.
In the plan three changes i.e. reorganization of national
universities including merger of some institutions; introductionof business methods to national universities through the processof “incorporatization” and introduction of competitive
mechanism into the university sector, including national, public
and private universities.
In developing countries Industry University and ResearchInstitution coordination can reduce the gap and create better
environment for the research and development of thetechnology and its adoption.
A value chain model is suggested in which… “a central
agency will play the role of helping in national intellectual
property (IP) system in promotion and encouragement of local
inventions and innovation. It will facilitate creation andmanagement of IP assets in Institutions of Higher Learning,
especially in assisting researchers on how to identify patentable
inventions and take appropriate action to safeguard them from

Figure3: Proposed Modelas it was 20 years earlier. A strategy should be evolved withcollaboration of research universities and stakeholders of rural
area.
A. Globalized Trend of Internationalization of Higher
Education
As espoused in the abovementioned literature,internalization of higher education curriculum is vital towards
economic development of a country to prepare students forcareers that are potentially international and contribute to local
economic development and competitiveness. Recent years haveseen a tremendous expansion of ways in which higher
education goes ‘international”. As well, international trends
and developments taking place beyond national boundaries
impact more easily on higher education policy at institutionaland national levels, thus creating additional inter-connections
between various changes. Consequently, it is not only difficult
to keep track of the various concepts and terms used to describe
new processes in the international aspects of higher education,
it is also difficult to capture these interconnections. An
additional challenge comes from the fact that innovations andchanges are on-going and thus the field is evolving constantly.
University curricula in Information Communication
Technology (ICT) necessarily require frequent changes,
updating and even complete revision due to advancements intechnologies and changes in how people and organizations
make use of computers. The increased focus on achieving
economic benefits and the technological advancement demands
higher education policies on internationalization of ICT
education. The most important reasons for integratinginternational topics into the ICT curriculum are:Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

1. To identify key issues, trends and area of growth and toelaborate policy statement that call for change andimprovement.
2. Results in a more global orientation on part of students.
3. Sensitizes students to differentiate between countries intechnologies.
4. Results in more interesting class discussions, and
5. Makes students more desirable in the marketplace. [11]In effect, countries can be able to match other countries, such
as India, in producing this ‘raw material’ of university-educatedprofessionals in high enough quality and quantity it can becomea ‘Services-export Tiger’ rivaling the growth rates of the fastest
growing Asian economies. This, however, should not be seen
as a case of ‘picking the latest fad’ but rather an example of one
opportunity where investment in higher education could yieldvery high economic returns.
Acknowledging the growing needs to better prepare students
for living and working in an increasingly culturally diverse andsocially complex world, universities should develop newteaching and learning strategies to attain an internationalized
curricula. Likewise, there is a need for a customized national
curriculum in order to cope with the global world that could
ultimately lead to the future production of quality graduates forjob opportunities within and outside of the country, and be atpar with other universities.
B. Model Universities
In this respect the University may introduce an internshipprogram for the students for duration of six months. This should
be made as a requirement of their study program. The student
will have to spend that time in rural area of countries and thenwork to solve the rural area problems. They will be working as“Brain Circulation”. That will affect the living status of thevillage. People of the rural area don’t have facilities so even
their own person after getting education don’t want to comeback to the place. The highly qualified people from different
universities will work there, share their knowledge and will see
the real situation to solve their problem. Education should
begin from home.
People of the rural area of the developing countries should
provided with free internet and mobile phone. That will not be ahigh cost. After implementing this strategy the social inclusion,
gender equity, reaching remote areas and regional
imbalances/disparities will be eliminated. The experiment andprototype manufacturing and the use of that prototype in thelive environment will change the economic condition.
In many analyses it proves that universities play an importantrole for innovation because R & D is the key aspect of theinnovation. So doing this initiative it is the knowledge
investment in the rural area. Universities, public research
institution and Science Park play important role in theinnovation. Economic Development of the rural sector isstrongly related to technological and scientific specializationand innovation. From the traditional process the developing
country has to face painful transition process towards techno
economic system.

412

C. Innovation Universities Example
An example of how a university can undertake its new role indifferent ways to enhance innovation explained in RMIT’s
initiatives in the East Gippsland region. In consultation
between RMIT and the local community, RMIT questioned
about the issues confronting the region and find out the way
how best it might assist. They had a report on degradation of theecological systems which had identified problems but there had
been little follow-up. They recruited nineteen post-graduate
students to follow up the initial report and identify solutions.
Their research is being conducted in the region, and with thoseaffected by the problems they seek to solve. They will reportprogressively on their results, reducing the normal
dissemination time-cycle. They will have access to local, often
informal or undocumented data, available only on the spot. This
exemplifies a new way of working in collaboration andpartnership with others and identification of research agendas
by others.
A research university is one of the necessary conditions for
economic restructuring. Research universities play a importantrole to provide scientific knowledge, technical expertise, andskilled workforce for implementation in the field. While inindustrial growth we have successful examples of Silicon
Valley in California, Route 128 in Massachusetts, and Research
Triangle area in North Carolina are all close to major research
universities, and this fact has been perceived as instrumental in
positioning these areas on new growth trajectory.
John Hopkins Institute for Policy Studies in Baltimore offers
service to the community through graduate and undergraduate
student internships, seminars and briefings, and volunteer
activities, each year the Institute’s first-year students research a
policy issue of particular interest to the City of Baltimore, andpresent their findings and recommendations to city leaders,
community activists, local business owners, and concerned
citizens, among others. [23]Massachusetts Institute of Technology (MIT) has a programfor International Development “D-Lab”. The purpose is tofoster the development of appropriate technologies andsustainable solutions within the framework of international
development. In India M S Swaminathan Research Foundation(MSSRF) a Centre for Research on Sustainable Agriculture andRural Development has established to harness the power of ICT
in the knowledge, skill, economic and social empowerment ofrural families based on the principle of reaching the unreached
and voicing the voiceless. The MSSRF has collaboration with
the D-Lab program. [24][25]D. Initiative in Pakistan
Namal college is the initiative to trigger innovation for the
benefit of the society with collaboration of University ofBradford, UK and LUMS, Pakistan. Namal Knowledge City isbuilt in Mianwali a small district of Pakistan. Project building
consisting of Computer Science, Software Engineering andElectrical Engineering departments with their laboratories has
now been completed.
The development of Namal Knowledge City (NKC) is
planned in three phases. The Phase-1 short certificate courses,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

diploma in higher education programmes and undergraduate
degree programmes has established, after that it will offer
postgraduate degree programme in various disciplines. One of
the main planned activities of Phase-2 is Namal College staff
development and establishment of links with industrial partners
to promote knowledge transfer at local and national level.
Phase 3 will establish Technology Park, enterprises andcommercial. [26].
In Pakistan Higher Education Commission is the primary
regulator. Higher education commission is the autonomous
body and is responsible for higher education policy, quality
assurance, degree recognition, development of new institutions
and uplift of existing institutions. Higher educationcommission program “Program for Collaborative Research”(PPCR). The objective of this program to involve the experts
from foreign universities in developing curriculum, updating
laboratory techniques, and provide guidance to post graduates
students in universities of the Pakistan. This program
encourages the community of foreign experts to develop
joint-research programs with local faculty and to enhance
expertise for the overall development of concerned
departments. On the other hand Higher education commission
is building the Nation Innovation policy for the university
industry collaboration to promote the innovation. Nation
Innovation policy preparation has started with the creation of an
Innovation Strategy Working Group (I-SWOG) in May 2009.
The members of the working group were the government,
private sector and academia. Two workshops also organized in
Karachi and Lahore. The CSF Karachi workshop released a
Draft Strategy document “Pakistan Innovation Initiative:
Towards Strategy and Implementation”. This document hassound base for making the innovation policy.
IV. ANALYSIS
Game theory has become a cross- disciplinary study of great
importance for the mathematical social sciences. It offers thetool- kit applicable to decision problems in which theconsequences of one decision may depend on the decisions ofothers, previous decisions creating the conditions for current
decisions, simultaneous and subsequent decision. Game theory
analysis is used to analyze that where the structural flaws lie
and begs the question, "Can fix those flaws? The reasons tothink where rules don't apply in particular case?"
Understanding of game theory is vital to technology
prioritization. It is also an important thing to evaluate for the
think tank that was their predictions correct?
It has been argued that “operational characteristics of
economic models, and in particular stability considerations,point strongly toward an equilibrium concept for dynamic
dominant player models which implies that the players
determine their best decisions depending on the current state of
the system and the decisions of the other players, and rationally
expecting that equilibrium decisions will be chosen in the
future. This solution is called the feedback solution. It has theproperty that the original plan is consistent under replanning.
The difference between the solutions in this regard does notdepend on the presence of uncertainty. Because of this property,
the feedback solution is the only one that seems likely to be413

stable in the sense that decision makers grouping forequilibrium decision rules will converge on these decisionrules” [25].
As Aumann has observed “those two aspects of game theory
are really not two separate disciplines; they are part of the same
whole”. For the purpose of the public policy, though it is notenough that cooperative and non- cooperative analysis arecomplementary, as Aumann observes. Rather we need analysisof given models that are linked, drawing on cooperative andnon- cooperative approaches. This reflects the different roles ofcooperative and non- cooperative models in the pragmatic
project of the public policy, in that it is commonly the
non-cooperative models that identify the problems, so thatcooperative analysis of the same example is necessary in orderto propose solutions [27][28].
Game theory concept has been used in solving problem of
education [29][30][31]. However, this paper is using a new
approach between Government and University role for
adopting the technology.
Therefore, this paper uses this tool “Game Theory” for thesolution of issue “How do the Government interact with theuniversity to promote and adopt the technology and innovative
integral process for the development of the country”.
Now we construct the following game between thegovernment and University. The game is simultaneous game.
There are two players, Government and university. The players
also have two strategies.
Players:
Government and University.
Sequence of events:
Sequential decision by government and then University
Strategies:(1) Government don’t take any action in case when
Universities are self aware.
(2) Government strategy is to take action and provide
latest technology in the form of machinery/equipment, staff
and investment.
(3) Government strategy is to make integral policies forthe industry university collaboration.
Payoffs:
C1 = Government’s cost to do action.
C2 = University’s Investment cost
α = University extra cost to do action for adopt oftechnology the in the absence of Government support.
Pg = Government probability to provide subsidy
Pg- 1= Government probability for no subsidy
Pu = University probability to do action
Pu-1 = University probability not to do action.
S= Social Benefit
K = Technology
K1= New Technology
Y= Output Y=y(K+K1)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

Figure 4 : Government University Game

If
Y-(C2+α)≥0 then the University should do action
Y-(C2+α) ≤0 then Government should do action or better
policy.
Government Pay off:
Government provide technology /integrated policy=[Pg.Pu(S.Y-C1) ]+[Pg(1-Pu)(-C1) ]=[PgPu.SY-PgPu.C1]+[Pg-PgPu(-C1)]=Pg.Pu.SY-PgPu.C1-Pg.C1+PgPu.C1=PgPu.Sy-Pg.C1=Pg(Pu.SY-C1)
Government do not provide technology /integrated policy=[(1-Pg)Pu S.Y]+[(1-pg)(1-Pu)(0)]=[(1-Pg).Pu.SY]
University Pay off:
Government provide technology /integrated policy=[Pg.Pu(Y-C2) ]+pg(1-Pu)(0)=[PgPu.Y-PgPu.C2]=[PgPu(Y-C2)]
Government do not provide technology /integrated policy=[(1-Pg).Pu(Y-(C2+α) ]+ [(1-Pg)(1-Pu)(0)]=(1-Pg).Pu.Y-(1-Pg)Pu(C2+α)=(1-Pg).Pu(Y-(C2+α)
It is clear that Government payoff is greater if don’t doactionand university do it by them self.
Pg(Pu.SY-C1)<[(1-Pg).Pu.SY]
In other case
PgPu(Y-C2)>(1-pg).Pc(Y-(C2+α))
Now the decision of action base on the probability of the
University if the probability of the university to do action Pu isnear to zero such as Pu= 0.01, the government should provide
the technology in the first stage of the game. The University’s
probability will increase. We construct the repeated game. In
the second stage when the University’s probability will
increase to take action such as near to 1 Pu= 0.9 thengovernment should not do action.

414

The government’s aim is to provide preferential support to
University’s for the taking action and encourages them. As aresult the government’s pay off revenue increase in the form of
increased social benefit of countries.
The game process on adoption of the technology andgovernment role to provide preferential support to the
Universities is explained in game tree and with the help ofequation. The game has three strategies for government andUniversities. Government takes action to provide preferential
support for adoption of technology. The government will
manage some expert to evaluate the technology for its
effectiveness and make policy and process to adopt thattechnology.
These expenses are shown as “C1”. The
Government provides new technology or implements a
integrated policy which is the investment cost. Then University
adopts new technology or in other case follow the integrated
policy as a result the increased output in the form of humancapital. The government social welfare increases. If
government does not take any action then University will not beable to do action due to risk factor, less intensive and funds. The
University has two strategies to adopt the technology and not toadopt. We use the Backward Induction game to take thedecision of government bases on Univesity’s strategies. As thisgame has both cases symmetric and asymmetric information
game, in symmetric information both players clearly
understand their strategies and asymmetric Information when
only government aware of the strategy.
A. Case 1: Government University Non Cooperation GameThe institution is the backbone of the country, credibility ofthe education system is very important. Therefore, to keep thehigh standard in education, government should haveinformation. Government should evaluate the education systemand their capability. In case Government gets information that
Universities has capability to do action and University is self
aware and decides to do action by investing cost C2, University
has more resources than government. In this case government
plays not to do action as government has constrained to play A(action) because government has limited fund and theuniversity plays action. The government gets 3 and theuniversity 2. We assume in this case that both players arerational and the structure of the game is common knowledge.
The university self awareness is the way to achieve to quality ofeducation and improve the role of university. University hirecompetent faculty, improve the research and development
facilities and collaborate with the industry. The government islikely to choose N with other players being better off andgovernment save the fund for the other development projects.
The payoffs are intuitive. This is the government best
strategy, as the government plays no action to the university
and saves costs. The government knows that the university willtake the right approach for the greater good by drawing upon its
comparative advantage in the creation and transmission ofknowledge. The government gets worst payoff of 0, as theuniversity could not produce skilled human resource in all
fields and that will not be able to cope with the current
economic situation.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014415

off. It is also the case that the university would find its way to
do action what the government says under (A, A). The self
awareness is a strategic move, a move that induces the otherplayer to choose in one’s favor. It constrains the other player’s
choice by affecting his expectations. So in this game as theGovernment strategic move to do action and change its payoffs
encourage universities to do action government’s expectationthat the university would also play A.

Figure 5: Government University Non Cooperative Game

While at (2, 3) the university obtains its best payoff of 3 inthis one-shot game. For the government, (2, 3) is ranked next tothe best, with a payoff of 2, as the government is playing role.
On the point (1, 1) the university gets q. In the game theuniversity has dominant strategy (2, 3) if the government plays
A then university is better off playing A as well, and if thegovernment plays N, the university is better off playing A.
Thus, no matter what the government plays, the university is
better off playing A. So the government would play N to get 3
instead of A and get payoff 2. The strategy pair (N, A) emerges
as the unique equilibrium of the game yielding a payoff of (3,2). In the first phase the universities do actions and in thesecond phase it revised simultaneous game is played. The
strategy is (N, A) is a good outcome.

C. Case 3: Government University Cooperation Game with
PolicyIn this case universities assume that there are integration
policies that stimulate the university to play A. The policy
imposes rule and is a game changer. The new game isrepresented in Figure. Now university has a dominant strategy
to play A and (A, A) is the dominant strategy equilibrium. The
policy induces cooperation such that the universities andGovernment are better off.
In this case all other payoffs remain the same. The new
equilibrium is also Pareto optimal in the sense that no player
can be made better off without making the other worse off.
They are both better off because they play (A, A) and get 3
each. Therefore, it is perfect cooperation because Government
does effort in the form of policy and University also use it
resources to follow the policy.

B. Government University Cooperation Game

Figure 7: Government University Non Cooperative Game with policy

Figure 6: Government University Cooperative Game

In this case we assume that Universities don’t have capability
to do action and don’t have information and resources. Then the
new game is mentioned in Figure. If government unable to do
this practices that result the failure of the expected outcome.
This case assumes that the government has fund, therefore able
to do actions and adopt the strategy A. The university’s new
dominant strategy is to follow that action. The equilibrium of
the revised game would be (3,3) at which the government getsits best payoff of 3, and the university gets its payoff of 3. The
new equilibrium is also Pareto optimal in the sense that noplayer can be made better off without making the other worse

The following conclusion seems evident. Universities withthe ability to compete and having the means to play A while thegovernment implement and enforce policies. It is obvious thatonce the policies are implemented, the universities follow therule and will be better off. The policy will change the role ofuniversity; universities with the new role will be part of theGovernment towards the development. The government of thedeveloping countries does not have funds and do not want toimplement such policies. Therefore, is still gearing up to
implement policies. Credible commitment to swift andeffective implementation of the policies is thus critical. There ispressing need to improve the policies for the quality ofeducation and to make well defined policies for thedevelopment of the country.
V. CONCLUSION
In essence, this study is proposing a new role of universities
for economic development of the country. It is concluded that

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

to transfer or catch up technology to developing countries, newuniversities should be established in the rural areas with theInternationalization of ICT curriculum. The highly qualified
people from different universities will work there, share theirknowledge and will see the real situation to solve theirproblems. This study would not only be significant to thedeveloping countries but also to universities in other developed
countries that need to upgrade higher education system tocompete with the globalized world.
The role of the government is to initiate and promote thetechnology by the effective policies and rules. Government cando action by providing the technology or by effective integral
policies for the better control of Universities. There is need todesign the mechanism that specify the institutions, procedures
and the rules of the game with a desired outcome in mind. The
mechanism should consider the all the stake holders and thecommunication system. The desired outcome can be achieved
if the players of the game have full information about the game.
While in education system it would be fatal to relay on theuniversities only because moral hazard is a serious issue and islikely to be misused. It also does not seem wise to keep theeducation in the government sector under direct control of thepoliticians. The wise option is education system should beunder the autonomous body like Higher education commission(HEC) in Pakistan to deal with policy formulation as well as
policy implementation. The commission should formulate
policy on a continuing basis, analyze real-time data for
mid-course correction, and prosecute cases against
unscrupulous providers of education.
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Ayesha Saleem has done Masters of Science inInformation Technology. She had been awarded with theGold Medal in Master of Information Technology after
securing 1st Position. She has done Masters in Public
Policy as well, from National Graduate Institute for
Policy Studies (GRIPS), Japan. She is also certified
Project Management Professional by ProjectManagement Institute USA. She is doing PhD in GITS
(Graduate School of Global Information and Telecommunication Studies),
Waseda University, Tokyo, Japan.
She had been working in a World Bank’s Project titled “Land Records
Management Information Systems” (LRMIS) in the capacity of ProjectManager. She also has experience to work as section leader & team leader ofconfiguration management/Software quality assurance Department in
multinational software houses. These days, she is working as trainee in the
Directorate for Science, Technology and Industry (DSTI), Economic Analysis
and Statistics (EAS), Organization for Economic Co-operation andDevelopment (OECD), Paris.
Kiyohide Higuchi is PhD in Economics from Graduate
School of Economics, Waseda University. He had been
Professor of School of Science & Engineering ofWaseda University and Meikai University, Japan.
Currently he is Professor of Graduate School of Global
Information and Telecommunication Studies of Waseda
University and Graduate School of International Culture
and Communication of Waseda University, Japan.
He has published many books and research paper as
author and co-author. His latest publications are: (1)"The way of new
information and communications market-status and trends of information andcommunication in major countries and international organizations",
International Telecommunication Union and the World Trade Organization(WTO) Sixth chapter PP.299-305(co-author)(foundation), International
Communication Economic Research Institute Reviews, February 2004. (2)“The IT business strategy in open- platform era” second chapter representative“public policy outsourcing as IT Business” PP.12-22(co-author)(foundation),International Communication Economic Research Institution Reviews. March
2005. (3) “Fairness and effect of tolls”, Japan Transport Policy study Group
Series A-418, 2006.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

418

Robust Digital Image Cryptosystem Based on
Nonlinear Dynamics of Compound Sine andCosine Chaotic Maps for Private Data Protection
Sarun Maksuanpan, Tanachard Veerawadtanapong, Wimol San-Um
Intelligent Electronics Systems Research Laboratory
Faculty of Engineering, Thai-Nichi Institute of Technology, Patthanakarn Rd., Suanlaung, Bangkok, Thailand
sarun.maksuanpan@gmail.com, tanachad.p@gmail.com, and wimol@tni.ac.th
Abstract—this paper presents a digital image cryptosystem
based on nonlinear dynamics of a compound sine and cosine
chaotic map. The compound sine and cosine chaotic map is
proposed for high-degree of chaos over most regions of parameter
spaces in order to increase high-entropy random-bit sources.
Image diffusion is performed through pixel shuffling and bit-plane
separations prior to XOR operations in order to achieve a fastencryption process. Security key conversions from ASCII code to
floating number for use as initial conditions and control
parameters are also presented in order to enhance key-space andkey-sensitivity performances. Experiments have been performed
in MATLAB using standard color images. Nonlinear dynamics ofthe chaotic maps were initially investigated in terms of Cobweb
map, chaotic attractor, Lyapunov exponent spectrum, bifurcation
diagram, and 2-dimensional parameter spaces. Encryption
qualitative performances are evaluated through pixel density
histograms, 2-dimensional power spectral density, key space
analysis, key sensitivity, vertical, horizontal, and diagonalcorrelation plots. Encryption quantitative performances areevaluated through correlation coefficients, NPCR and UACI.
Demonstrations of wrong-key decrypted image are also included.
Keyword— Digital Image Processing, Cryptosystem, Chaotic
Map, Encryption, Decryption, Nonlinear Dynamics

Manuscript received March 7, 2013. This work was
financially supported by Research and Academic Services
Division, Thai-Nichi Institute of Technology.
Sarun Maksuanpan is with computer engineering program,
Faculty of Engineering, Thai-Nichi Institute of technology,
Bangkok, Thailand. (E-mail: sarun.maksuanpan@gmail.com).
Tanachard Veerawadtanapong is with Computer Engineering
Program, Faculty of Engineering, Thai-Nichi Institute oftechnology, Thailand. (E-mail: tanachad.pong@gmail.com).
Wimol San-Um is with the Intelligent Electronics Systems
Research Laboratory, Faculty of Engineering, Thai-Nichi
Institute of Technology, Bangkok, Thailand, Fax: (+662)
7632700, Tel: (+662) 7632600 Ext. 2926, (E-mail:wimol@tni.ac.th).

I. INTRODUCTIONR

ECENT advances in communications have led to great

demand for secured image transmissions for a variety of
applications such as medical, industrial and militaryimaging systems. The secured image transmissions greatly
require reliable, fast and robust security systems, and can beachieved through cryptography, which is a technique of
information privacy protection under hostile conditions [1].
Image cryptography may be classified into two categories, i.e.
(1) pixel value substitution which focuses on the change in pixel
values so that original pixel information cannot be read, and (2)pixel location scrambling which focuses on the change in pixel
position. Conventional cryptography such as Data Encryption
Standard (DES), International Data Encryption Algorithm(IDEA), Advanced Encryption Standard (AES), and RSA
algorithm may not be applicable in real-time image encryption
due to large computational time and high computing power,
especially for the images with large data capacity and highcorrelation among pixels [2].
Recently, the utilization of chaotic systems has extensively
been suggested as one of a potential alternative cryptography insecured image transmissions. As compared to those of
conventional encryption algorithms, chaos-based encryptions
are sensitive to initial conditions and parameters whilst
conventional algorithms are sensitive to designated keys.
Furthermore, chaos-based encryptions spread the initial region
over the entire phase space, but cryptographic algorithms
shuffle and diffuse data by rounds of encryption [3]. Therefore,
the security of chaos-based encryptions is defined on real
numbers through mathematical models of nonlinear dynamics
while conventional encryption operations are defined on finite
sets. Such chaos-based encryption aspects consequently offer
high flexibility in encryption design processes and acceptableprivacy due to vast numbers of chaotic system variants andnumerous possible encryption keys.
Chaos-based encryption algorithms are performed in twostages, i.e. the confusion stage that permutes the image pixels

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

II. PROPOSED ENCRYPTION ALGORITHMS[p1]

Image to be
encrypted[p0,p1,p2,…,p7][p0]

Separate

Dec2BinGrayscale Image
512x512 Pixels

8-Bit Binary Number
per Pixel

1-Bit Binary Number per Pixel

3 Sets of RGB Image

24 Sets of Binary Number
XOR

16-Characters ASCII
Codes

a1
a2
Initial conditions
a8

Key
Generation
SystemCompoundSine andCosine Mapsb1
b2
Parameters
b8

[q7]
[q1]

[q0,q1,q2,…,q7]

Encrypted
Imageand the diffusion stage that spreads out pixels over the entire
space. Most existing chaos-based encryptions based on such
two-stage operations employ both initial conditions andcontrol parameters of 1-D, 2-D, and 3-D chaotic maps such as
Baker map [4,5], Arnold cat map [6,7], and Standard map [8, 9]
for secret key generations. Furthermore, the combinations of
two or three different maps have been suggested [10, 11] inorder to achieve higher security levels. Despite the fact thatsuch maps offer satisfactory security levels, iterations of maps
require specific conditions of chaotic behaviors through anarrow region of parameters and initial conditions.
Consequently, the use of iteration maps has become typical for
most of proposed ciphers and complicated techniques in pixel
confusion and diffusion are ultimately required.
This paper therefore presents an alternative chaos-based
digital image cryptosystem with three main aspects. First, thecompound sine and cosine chaotic maps, which potentiallyoffers high-degree of chaos over most regions of parameter
spaces, is proposed through nonlinear dynamics analyses and isconsequently exploited as high-entropy random-bit sources forencryption. Second, image confusion and diffusion processes
are performed through uncomplicated pixel shuffling and
bit-plane separations prior to XOR operations in order toachieve a fast encryption process. Last, security key
conversions from ASCII code to floating number for use as
initial conditions and control parameters are also presented inorder to enhance key-space and key-sensitivity performances.

419

Bin2Dec[q0]

Combine8-Bit Binary Number
per Pixel

1-Bit Binary Number per Pixel

3 Sets of RGB Image

24 Sets of Binary Number

Fig. 1. Proposed pncryption algorithms using compound sine and consine
chaotic maps.
TABLE ISUMMARY OF 16-CHARACTERS INPUT ASCII CODES FOR SETTING INITIAL
CONDITIONS AND CONTROL PARAMETERSASCII code Xm for
Setting initial conditions

ASCII code Ym for setting
control parameters

X1: A1A4A7A10A13A16

Y1: A1A3A5A7A9A11

X2: A2A5A8A11A14A1

Y2: A2A4A6A8A10A12

X3 : A3A6A9A12A15A2
Y3 : A3A5A7A9A11A13
A category of trigonometric functions, involving sine andcosine maps, have potentially offered rich dynamic behaviours
X4 : A4A7A10A13A16A3
Y4 : A4A6A8A10A12A14
as described in a simple forms as [12] xn+1=sin(axn) andX5 : A5A8A11A14A1A4
Y5 : A5A7A9A11A13A15
xn+1=cos(bxn) where the constants a and b can be considered as
the parameters associated the frequencies of sine and cosine
X6 : A6A9A12A15A2A5
Y6 : A6A8A10A12A14A16
functions, respectively. Although such sine and cosine maps
X7 : A7A10A13A16A3A6
Y7 : A7A9A11A13A15A1
offers relatively high complexity in terms of nonlinear dynamics,
X8 : A8A11A14A1A4A7
Y8 : A8A10A12A14A16A2
the chaotic regions in the bifurcation diagram is still insufficientdue to periodic characteristics. This paper therefore considers
an enhancement of sine and cosine maps through the original color image with M×N image size is initially converted
combination between sine and cosine maps, i.e.
into three sets of sub-images with RGB components containing
pixels in grey scale levels. Each sub-image will subsequently bexn 1  cos(axn )  sin( bxn ) 
converted into binary matrix in which each pixel is represented
As will be seen later, such a compound sine and cosine map in by 8-bit binary numbers. For example, the pixel p(1,1) contains(1) offers high-degree of chaos over most regions of parameter the binary number p0-p7. Each pixel will then be separated into
spaces. As a nature of chaotic maps, the initial conditions and eight planes corresponding to binary bits p0 to p7. As a result,control parameters can be used as internal security keys that there are 24 sets of bit plane images represented in matrix forms
entirely set the encryption characteristics. The proposed with a single binary number in each pixel, which is ready for
cryptography
techniqueattempts
toachieve further Excusive-OR (XOR) operations.
simple-but-highly-secured image encryption and decryption
Second, the input security keys from users which isalgorithms in a category of chaos-based cryptosystems. Fig.1 represented in ASCII code with arbitrary 16 alphanumericshows the proposed encryption and detection algorithms using characters defined as A=A1A2A3,...,A16 will form two main setscompound sine and cosine maps. Three major procedures are of ASCII codes, i.e Xm and Ym, for setting the initial conditions
summarized as follows;
and the control parameters, respectively, where m = 1, 2, 3,...,8First, the original image is prepared for diffusion. The

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

R Xm  (B X1  2 0  B X1  21  ...  B X 48  2 47 ) / 2 48



RYm  (B Y1  2 0  B Y1  21  ...  BY 48  2 47 ) / 2 48



As a result, the initial conditions and the control parameters canbe achieved byParameter a = (0,10)

Parameter a = (0,1)

(a)

(b)

Fig. 2. Plots of 2-D Lyapunov Exponent bifurcation structure between
parameters a and b over the parameter space; (a) (0,10) and (b) the zoomed inregion (0,1).



bm  ( RYm  RYm 1 ) mod 1



It is apparent that the values of am and bm are in the region of(0,1) and are ready for use as internal security keys in theencryption algorithms. The design algorithm realizes eightchaotic maps based on (1) as follows;

Lyapunov Exponent (LE)am  ( RXm  RYm ) mod 1

420

Parameter b = (0,1)

as summarized in Table 1. Such two sets Xm and Ym will be
converted into 48-bit binary representations denoted by BX1 to
BX48 and BY1 to BY48, respectively. The real numbers aresubsequently formed by converting the binary representation as
follows;

Parameter b = (0,10)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

Chaotic Behaviors : LE>0
Non-chaotic Behaviors : LE<0

It is seen in (4) that the constant 10π has been include in order tosustain the parameters a and b described in (1) in the region of(0, 10) which is sufficient to acquire chaos. The values of m arecircularly shifted with 1 to 8, i.e. if the operation round reaches
m+1=8 then the next value is 1. As results, a total 16 keys are
employed as security keys in the encryption process. Such keys
are used to generate chaotic signal from the compound sine andcosine chaotic maps. The output signals are adjusted to thebinary number through the zero thresholds for the subsequent
XOR operations.
Last, the XOR operations diffuse the generated chaotic bit
and the 24 binary images in parallel process. The XOR
operation yields bit “1” if the two input bits are different, but
yields bits “0” if the two inputs are similar. The results obtained
from such XOR operations are 24 matrices with single binary
number in each pixel. All the 24 matrices are combined into
three RGB matrices of a single 8-bit matrix in which each pixel
is represented by [b0-b7]. As a result, the encrypted image can beachieved. The decryption process also follows the encryption
process in a backward algorithms as long as the security keys
are known. It is seen in (4) that the constant 10π has been
include in order to sustain the parameters a and b described in(1) in the region of (0, 10) which is sufficient to acquire chaos.
The values of m are circularly shifted with 1 to 8, i.e. if theoperation round reaches m+1=8 then the next value is 1. As
results, a total 16 keys are employed as security keys in theencryption process. Such keys are used to generate chaotic

Iterated Values Xn

x m, n 1  cos(bm 10 πx n )  sin( bm 110 πx n ) 

Parameter a

(b)

(b)

Fig. 3. Plots of the Lyapunov exponent spectrum and the bifurcation diagramof parameters a over the parameter space (0,10) when the parameter b is fixed
at 0.5.

signal from the compound sine and cosine chaotic maps. The
output signals are adjusted to the binary number through thezero thresholds for the subsequent XOR operations.
Last, the XOR operations diffuse the generated chaotic bit
and the 24 binary images in parallel process. The XOR
operation yields bit “1” if the two input bits are different, but
yields bits “0” if the two inputs are similar. The results obtained
from such XOR operations are 24 matrices with single binary
number in each pixel. All the 24 matrices are combined into

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Number of the initial condition a1

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

N=1000 iterations

Values of the initial condition a1

(c)

(b)

Fig. 4. Histrogram of numbers of initial conditions a1.

three RGB matrices of a single 8-bit matrix in which each pixel
is represented by [b0-b7]. As a result, the encrypted image can beachieved. The decryption process also follows the encryption
process in a backward algorithms as long as the security keys
are known.
III. EXPERIMENTAL RESULTS
Experimental results have been performed in a computer–aid
design tool MATLAB. Nonlinear dynamics of a compound sineand cosine map was initially simulated and encryption anddecryption security performances were subsequently evaluated.

421

parameters a over the parameter space (0, 10) when theparameter b is fixed at 0.5. It is apparent in Fig.2 that the LE
spectrum is greater than zero and growing to infinity. In
addition, the bifurcation diagram shows dense area of the
maximum values of Xn over the entire range. As for a particular
example, Fig.3 shows the histograms of the numbers of thesecret key a1 for 1,000 iterations. It can be seen from Fig.3 thatthe nonlinear dynamics of the compound sine and cosine maps
provide the random secret keys that distribute over the region(0, 1) randomly. Such characteristics have also found in othersecret keys. The simulations have been ensured that the
proposed compound the nonlinear dynamics of the compoundsine and cosine maps and the key generation systems canpotentially provide truly random values for diffusion process inthe proposed cryptosystem.
B. Key Space Analysis
The encryption and decryption realizes the 16-character
ASCII code “ABCDEFG012345678” as an input key and thewrong key changes the last character to 5. The resulting eight
initial conditions and eight parameters, i.e. a total of 16 keys,
are represented by 8-digit floating-point numbers. Considering
each key in the form S×2E where S is a significand and E is anexponent, the keys that represented by 8 digits of afloating-point number (~3.4028×1038) results in 128 uncertain
digits, which is greater than the minimum requirement of the56-bit data (~7.2057×1016) encryption standard (DES)
algorithm [23].

A. Nonlinear Dynamics of Compound Sine and Cosine Map
Since chaotic behaviors of the compound sine and cosine
maps determine overall performance of the cryptosystem,
Lyapunov exponent (LE) has been realized as a quantitative
measure of chaoticity. The LE is defined as a quantity thatcharacterizes the rate of separation of infinitesimally close
trajectories and is given by

C. Histograms and 2D Power Spectral Analysis
The image histogram is a graph that illustrates the number ofpixels in an image at different intensity values. In particular, thehistogram of a color image can be separated into threesub-images with Red (R), Green (G), and Blue (B) components.
Each sub-image has 256 different grey intensity levels,
graphically displaying 256 numbers with distribution of pixels
dX n 1  amongst these grayscale values. In addition, the 2D power
1 N
LE  lim  log 2
spectrum that shows the power of image intensity can ben  NdX nn 1obtained through a Discrete Fourier Transform (DFT) analysis
where N is the number of iterations. Typically, the positive LE and the algorithm is given by [24]indicates chaotic behaviors. The larger value of LE results inhigher degree of chaos. Fig.1 shows the plots of 2-imensional
M 1 N 1
Lyapunov Exponent bifurcation structure between parameters a F (u, v)    f ( x, y) exp(  j (2 / M )ux) exp(  j (2 / N )vy) 
x0 y 0and b over the parameter space (0, 10) and the zoomed in region(0, 1) where the chaotic region is represented by the dark blue
color while the non-chaotic region is represented in the white where x and y are a coordinates pair of an image, M and N are
region. It is shown in Fig.1 that the chaotic behaviors of the the size of image, f(x, y) is the image value at the pixel (x, y).
compound sine and cosine map occupy most of parameter Fig.4 shows the histograms of three R, G, B components and 2D
spaces, leading to a very robust chaos for secret key power spectrums of original image, encrypted image, decrypted
generations. Nonetheless, the zoomed in region at small values image, and decrypted image with wrong keys. As for aof parameters a and b contain some non-chaotic regions, which particular demonstration, the original image is Lena image withrepresent quasi-chaotic or periodic behaviors. The proposed 256×256 image size. It can be seen from Figs.4 that the
key generation system has been designed to potentially intensities of all original images in the histogram are
generates secret keys potentially since the nonchaotic signals contributed with different values in a particular shape and thewill ultimately be LE spectrum and the bifurcation diagram of power spectrum is not flat having a peak of intensity in the

Copyright ⓒ 2014 GiRI (Global IT Research Institute)422

Wrong Key
Decryption

Correct Key
Decryption

Encryption

Original ImageICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

ImagesHistograms of RED
Component of Images

Histograms of GREEN
Component of Images

Histograms of BLUE
Component of Images

2D Power Spectrum of
ImagesPixel values on (x+1,y+1)
Pixel values on (x+1,y+1)

Pixel values on (x,y+1)
Pixel values on (x,y+1)

Pixel values on (x+1,y)Pixel values on (x+1,y)Fig.5. Histograms and 2D power spectrums of original image, encrypted image, decrypted image, and decrypted image with wrong keys.

Pixel values on (x,y)

Pixel values on (x,y)

Pixel values on (x,y)

Fig.6. Image correlation tests in original and encrypted images, including horizontally, vertically, and diagonally adjacent pixels.

middle. The encrypted image has a flat histogram and power
spectrum, indicating that the intensity values are equallycontributed over all the intensity range and the original images
are completely diffused and invisible. The decrypted images

with right keys provide similar characteristics of the original
images while the decrypted images with wrong keys are still
diffused and the original images cannot be seen. These results
qualitatively guarantee that the image is secured.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014423

covariance Cv and the correlation coefficient γxy can be obtained
as follows [16-17];
C v ( x, y ) 

1 N
 ( xi  E( x))( yi  E( y))N i1

 xy 
(a) Lena Imagecov( x, y )D( x ) D( y )

where the functions E(x) and D(x) are expressed as
E( x) (b) Jet plane Image(c) Brain Image(d) Brain Image(e) Peppers Image
Fig. 7. Original and cipher images of five images for the experiments.

D. Correlation Coefficient Analysis
In order to quantify the encryption performance and key
sensitivity analysis, correlation between image pairs, which is ameasure of relationships between two pixels intensities of two
images, of the three realized images have been analyzed. The

1 N
 xi andN i1

D( x ) 

2
1 N( x  E ( x))i 1 iN(11)

and the variables x and y are grey-scale values of pixels incorresponding pixels in different images or two adjacent pixels
in the same image. Typically, the value of γxy is in the region [1, 1]. In other words, the values of γxy in the region (-1,0) and(0,1) respectively indicate positive and negative relationships,
while the larger number close to 1 or -1 have stronger
relationships. Using a random selection of 2,048 pairs of pixels,
Fig.5 shows image correlation tests in original and encrypted
images, including horizontally, vertically, and diagonallyadjacent pixels. It can qualitatively be considered from Figs.5
that the adjacent pixels of all encrypted images are highly
uncorrelated as depicted by scatters plots of correlations.
For the quantitative measures, the correlations between
pairs of original images and corresponding encrypted images
through the computation of correlation coefficient between
RGB components of the original images and corresponding
encrypted images have been analyzed. Table 2 summarizes
correlation coefficients of 2,048 pixels of each image pair. It
can be seen in Table 3 that the correlation coefficients are verysmall closing to zero, indicating that each pair of images arecompletely independent of each other. Fig.7 shows the original
and cipher images of five images for the experiments, including
Lena, Jet plane, Brain, Baboon and Peppers images. As forinvestigations of other images with different characteristics,
comparisons of correlation coefficients of four standard images
in MATLAB shown in Fig.7 are also studied. Table 3
summarizes correlation coefficients of 2,048 pixels of each pairof images shown in Fig.7. Apparently, the correlation
coefficients are also very small. These results quantitatively
guarantee that the image is secured.
E. Original Image Sensitivity Analysis
One minor change in the plain image causes significant
changes in the encrypted image then such differential analysis
may become inefficient, and therefore much difference between
encrypted forms is expected in order to maintain high security
level. NPCR (Net Pixel Change Rate) and UACI (Unified
Average Changing Intensity) are two most common measures.
NPCR concentrates on the absolute number of pixels whichchanges value in differential attacks while the UACI focuses on
the averaged difference between two paired encrypted images

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

424

TABLE IICOMPARISONS OF CORRELATION COEFFICIENTS OF LENA IMAGE AT DIFFERENT SIZES.
Image Sizes

CRR

CRG

CRB

CGR

CGG

CGB

CBR

CBG

CBB

256×256

0.003120.00298-0.00406

0.00195

0.00061-0.00267

0.00052-0.00061-0.00419

512×512-0.00306-0.00325-0.00099-0.00421-0.00211-0.00153-0.00367-0.00060-0.001081024×1024

0.00181-0.00081

0.000330.00113-0.00056-0.00053

0.000770.00008-0.00063

TABLE IIICOMPARISONS OF CORRELATION COEFFICIENTS OF DIFFERENT IMAGE WITH 256×256 IMAGE SIZE.
Images

CRR

CRG

CRB

CGR

CGG

CGB

CBR

CBG

CBB

Brain

0.00259-0.00123-0.00270

0.00259-0.00121-0.00271

0.00261-0.00128-0.00269

Mandril-0.00044

0.00735-0.00606

0.00265

0.00657-0.00625

0.003400.00194-0.00613

Peppers0.00429-0.00456-0.002400.00524-0.00076-0.001520.00129-0.00378-0.00152

Jet Plane-0.00111-0.00588-0.00644-0.00087-0.00347-0.00601-0.00126-0.00362-0.00488

TABLE IVSUMMARY OF NPCR AND UACI OF DIFFERENT IMAGE WITH 256×256 IMAGE SIZE.
Images

NPCRR

NPCRG

NPCRB

UACIR

UACIG

UACIB

Lena

99.2020

98.4085

99.202033.410733.430933.5449

Brain

99.2048

98.4956

99.3125

33.4488

33.3952

33.3707

Mandril

99.5102

99.0132

99.412333.5100

33.350733.4846

Peppers

99.4262

99.214499.231433.438733.3085

33.5637

Jet Plane

99.2436

98.948599.3345

33.4456

33.3845

33.4562[17]. For the two encrypted images in which the corresponding
original images have only one pixel difference are denoted by C1
and C2. Label the greyscale values of the pixels at pixel (i,j) in C1
and C2 by C1(i,j) and C2(i,j), respectively. Define a bipolar array
D, with the same size as images C1 and C2. Consequently, D(i,j)
is determined by C1(i,j) and C2(i,j), if C1(i,j) =C2(i,j) thenD(i,j)=1, otherwise, D(i,j)=0. The NPCR [21] is defined as
NPCR 

 D(i, j )  100%
i, j

(12)

T

  D(i, j )  100%UACI   i , j


T



(13)

where T denotes the total number pixels in the encrypted image,F denotes the largest supported pixel value compatible with thecipher image format, and |.| denotes the absolute value function.
Table 4 summarizes the values of NPCR and UACI for different
image with the sizes of 256×256. It can be seen that the NPCR
are relatively close to 100% and the UACI are also in theacceptable region of approximately 33%.
F. Information Entropy Analysis
The entropy H(s) is one of important characteristics of therandomness and can be found by
2 M 1H ( s )   P ( S i ) log 2i 01
P( S i )(12)

where P(Si) represents the probability of symbols i. In the casewhere a purely random source producing 2M symbols, theentropy is given by H(s)=M. If the output of a cipher image
produces the number of symbols with the entropy value of less

TABLE VRESULTS OF INFORMATION ENTROPY OF FIVE STANDARD IMAGES
ImagesEntropy H(s)Lena
7.99915Brain
7.99924
Mandril
7.99910Peppers7.99896
Jet Plane
7.99923

than M, there is a certain degree of predictability which
intimidates its security. Table 5 summarizes the results of
information entropy of those five standard images in Fig.7. The
values obtained are very close to the theoretical value of M=8,
indicating that information leakage during encryption process is
negligible and the encryption system is secure against theentropy attack.
CONCLUSION
A robust digital image cryptosystem based on nonlinear
dynamics of a compound sine and cosine chaotic map has beenpresented. The compound sine and cosine chaotic map has been
proposed for high-degree of chaos over most regions ofparameter spaces in order to increase high-entropy random-bit
sources. Image diffusion has been performed through pixel
shuffling and bit-plane separations prior to XOR operations inorder to achieve a fast encryption process. Security key
conversions from ASCII code to floating number for use as
initial conditions and control parameters were also presented toenhance key-space and key-sensitivity performances. Nonlinear
dynamics of the chaotic maps have been investigated in terms ofchaotic attractor, Lyapunov exponent spectrum, bifurcation
diagram, and 2-dimensional parameter spaces. Encryption
qualitative performances were evaluated through pixel density

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

histograms, 2-dimensional power spectral density, key space
analysis, key sensitivity, vertical, horizontal, and diagonalcorrelation plots. Encryption quantitative performances were
evaluated through correlation coefficients, NPCR and UACI.
Demonstrations of wrong-key decrypted image are also
included. The proposed cryptosystem offers a potential
alternative to private data protection systems.
REFERENCES
M. Philip, “An Enhanced Chaotic Image Encryption” International
Journal of Computer Science, Vol. 1, No. 5, 2011.
[2] G.H. Karimian, B. Rashidi, and A.farmani, “A High Speed and Low
Power Image Encryption with 128- bit AES Algorithm”, International
Journal of Computer and Electrical Engineering, Vol. 4, No. 3, 2012.
[3] G. Chen, Y. Mao, C.K. Chui, “A symmetric image encryption scheme
based on 3D chaotic cat maps”, Chaos, Solitons and Fractals, Vol. 21, pp.
749-761, 2004.
[4] X. Tong, M. Cui, “Image encryption scheme based on 3D baker withdynamical compound chaotic sequence cipher generator”, Signal
Processing, Vol. 89, pp. 480-491, 2009.
[5] J.W. Yoon, H. Kim,“An image encryption scheme with a pseudorandom
permutation based on chaotic maps”, Commun Nonlinear Sci Number
Simulation, Vol. 15, pp. 3998–4006, 2010.
[6] X. Ma, C. Fu, W. Lei, S. Li, “A Novel Chaos-based Image Encryption
Scheme with an Improved Permutation Process”, International Journal ofAdvancements in Computing Technology, Vol. 3, No. 5, 2011.
[7] K. Wang, W. Pei, L. Zou, A. Song, Z. He, “On the security of 3D Cat map
based symmetric image encryption scheme”, Physics Letters A, Vol. 343,
pp. 432–439, 2005.
[8] K. Wong, B. Kwok, and W. Law, “A Fast Image Encryption Scheme
based on Chaotic Standard Map”, Physics Letters A, Vol. 372, pp. 26452652, 2008.
[9] S. Lian, J. Sun, Z. Wang, “A block cipher based on a suitable use of thechaotic standard map”, Chaos, Solitons and Fractals, Vol. 26, pp. 117–
129, 2005.
[10] K. Gupta, S. Silakari, “New Approach for Fast Color Image Encryption
Using Chaotic Map”, Jour. of Information Security, pp. 139-150, 2011[11] F. Huang, Y. Feng, “Security analysis of image encryption based on
two-dimensional chaotic maps and improved algorithm”, Front. Electr.
Electron. Eng. China, Vol. 4, No. 1, pp. 5-9, 2009.
[12] G.Lee and N. H. Farhat , “Parametrically Coupled Sine Map Networks”,Electrical Engineering Department, University of Pennsylvania,
Philadelphia, PA, USA,15 December 2000
[13] F.Sun, S. Liu , Z. Li , Z. Lu , “A novel image encryption scheme based on
spatial chaos map”,College of Control Science and Engineering,
Shandong University, Jinan , PR China , pp. 631–640 , 2008[14] Q. Gong-bin, J. Qing-feng,Q. Shui-sheng, “A new image encryption
scheme based on DES algorithm and Chua's circuit”, Imaging Systems
and Techniques, pp. 168 – 172, 2009.
[15] Z. Peng, T.B. Kirk, “Two-dimensional fast Fourier transform and power
spectrum for wear particle analysis”, Tribology International, Vol. 30,
Issue. 8, pp. 583-590, 1997.
[16] A. Yahya and A. Abdalla, “A Shuffle Image-Encryption Algorithm”, J.
Comput. Sci,pp.999-1002[17] Y.Wu, Joseph P. Noonan, S.Agaian, “NPCR and UACI Randomness
Tests for Image Encryption”, Department of Electrical and Computer
Engineering Tufts UniversityMedford, MA ,USA
[1]

425

Tanachard Veerawadtanapong was born in Bangkok,
Thailand in 1992. He received B.Eng. in Computer
Engineering from Computer Engineering Department,
Faculty of Engineering, Thai-Nichi Institute ofTechnology (TNI). Currently, he is also a research
assistant at Intelligent Electronic Research Laboratory.
His research interests include Ad Hoc mobile network,
cryptosystems, and chaos theory.

Wimol San-Um was born in Nan Province, Thailand in
1981. He received B.Eng. Degree in ElectricalEngineering and M.Sc. Degree in Telecommunicationsin 2003 and 2006, respectively, from Sirindhorn
International
Instituteof Technology (SIIT),
Thammasat University in Thailand. In 2007, he was a
research student at University of Applied Science
Ravensburg-Weingarten in Germany. He received
Ph.D. in mixed-signal very large-scaled integrated
circuit designs in 2010 from the Department ofElectronic and Photonic System Engineering, Kochi University of Technology(KUT) in Japan. He is currently with Computer Engineering Department,
Faculty of Engineering, Thai-Nichi Institute of Technology (TNI). He is also
the head of Intelligent Electronic Systems (IES) Research Laboratory. His areasof research interests are chaos theory, artificial neural networks, control
automations, digital image processing, secure communications, and nonlinear
dynamics of chaotic circuits and systems.

Sarun Maksuanpan was born in Samutsakorn
Province, Thailand in 1991. He received B.Eng. in
Computer Engineering from Computer Engineering
Department, Faculty of Engineering, Thai-Nichi
Institute of Technology (TNI). Currently, he is also a
research assistant at Intelligent Electronic ResearchLaboratory. His research interests include information
security systems, cryptosystems, artificial neuralnetworks, and digital image processing.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

426

DVB-RCS: Efficiently Quantized Turbo
Decoder
Sherif Welsen Shaker
University of Nottingham, Ningbo Campus, P.R. China
welsen@ieee.org

Abstract—Turbo codes have been incorporated into many
important wireless communication standards including thesatellite return channel in DVB-RCS standards. According totheir iterative nature, the computational complexity of turbo
decoder is much higher than that of convolutional FEC decoders.
From the hardware implementation point of view, the complexity
can be reduced by using quantized decoder. For DVB-RCS turbo
coding, there are many block sizes and different code rates. In
order to realize the DVB-RCS turbo decoder efficiently, analgorithm should be developed for the best computation ofquantization range for each code rate and at different
signal-to-noise ratios. This paper investigates the decoder input
quantization of low complexity decoding algorithm and proposes
an algorithm for efficient decoder quantization by introducing ascaling factor into the decoding algorithm, aiming to achievesignificant improvement in the hardware implementation of thedecoder architecture.
Index Terms— DVB-RCS; Max-log-MAP; Quantization;
Turbo Decoder; Reduced Complexity

I. INTRODUCTIONFOUNDED by the European Telecommunications Standards

Institute (ETSI) in 1993, the Digital Video Broadcasting(DVB) project intended to standardize the digital television
services. DVB-S was the initial standard of digital television
with satellite delivery, that used a concatenation of an outer(204,188) byte shortened Reed Solomon code and an innerconstraint length 7, variable rate (r ranges from 1/2 to 7/8)
convolutional code [1]. DVB-S was a widely accepted standard
in the forward link of broadband satellite communications. The
second generation DVB-S2 includes the transmission ofmultimedia contents and a variety of uni-cast and multicast
services. Internet over DVB-S is a natural competitor against
cable modem and DSL technology, and its universal coverage
allows even the most remote areas to be served. Because
DVB-S only provides a downlink, an uplink is also needed toenable interactive applications such as web browsing. The
uplink and downlink need not be symmetric, since manyInternet services require a faster downlink.
Manuscript received July, 27 2013.
S.W. Shaker, is with EEE Dpt., Faculty of Science and Engineering,
University of Nottingham Ningbo, P. R. China (Tel: +86 147 149 175 34;
e-mail: welsen@ieee.org).

Transmitting an uplink signal back to the satellite over thesame antenna used for receiving the downlink signal, rather
than using a telephone modem, became an attractive alternative
for the subscriber equipment. . DVB-RCS standards have been
approved for Return Channel via Satellite; it provides two-way,full IP, asymmetric communications via satellite. In this way
not only the service can be quickly deployed, but the cost of theservice and the quality are independent of the distance between
the terminal and access point. This makes the service provided
via the satellite a strong competitor in those cases where cable
modems are not economically possible. However, given thesmall antenna aperture and requirement for a low-cost,
low-power amplifier, there is very little margin on the uplink.
Therefore, strong FEC coding is desired. Turbo codes haveshown great performance among forward error correction (FEC)
codes; they have been used by many standards like Wideband
CDMA, and Third Generation Partnership Project (3GPP) for
IMT-2000. For its major advancement in channel coding area,
convolutional turbo code are well-suited for mobile satellite
broadcasting applications and it has been chosen for DVB-RCS
standards [2]. The big advantage of turbo codes is their datatransmission reliability within a half decibel of Shannon Limit.
DVB-RCS standards are open to provide interactive
broadband access over satellite. It allows a central gateway or
hub to broadcast IP date on the forward link in the
DVB/MPEG2 format to large number of small terminals withdate rates up to 48 Mbit/s. Satellite terminals can send return
signals to the hub on the forward link. Twelve frame sizes aresupported ranging from 12 bytes to 216 bytes, including 53 byte
frame compatible with ATM and a 188 byte frame compatible
with both MPEG-2 and the original DVB-S standard. The
return link supports data rates from 144 kbps to 2 Mbps and isshared among terminals by using multi-frequency time-divisionmultiple-access(MF-TDMA)
anddemand-assigned
multiple-access (DAMA) techniques. DVB-RCS turbo code
was optimized for short frame sizes and high data rates; it
supports seven code rates, 1/3, 2/5, 1/2, 2/3, 3/4, 4/5, and 6/7.
The outstanding coding performance of those codes requires
the investigation of hardware implementation issues. For
portable radio terminal, low power consumption is a key
implementation issue. Decoding algorithm simplification andquantization are very important issues leading to reduction of
power consumption. In the past, several algorithms have beenused in order to simplify the decoding process of turbo codes.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

The penalty paid for those algorithms which aim to reduce thecomplexity is small error rate performance degradation ascompared to the performance achieved in case of using the
optimal algorithm. An additional correction term is required tobe added in order to minimize the performance degradation.
In this paper, our intention is to apply a simplified decoding
algorithm for DVB-RCS turbo codes. It is our objective toinvestigate the impact of the decoder quantization on therequirement of the decoder performance. The paper proposes
an algorithm for efficient decoder quantization that can beapplied on different DVB-RCS code rates and block sizes inorder to achieve a reduced decoder complexity. The structure of
DVB-RCS turbo encoder followed by a brief review of turbo
decoding algorithms is introduced, for interested reader, insection II. The structure of DVB-RCS turbo decoder is thenhighlighted for the Duobinary case. The quantized decoder isdiscussed in section III. In section IV, simulation results arepresented for different frame lengths and code rates of
DVB-RCS turbo codes. The effect of quantized decoder is theninvestigated, and the algorithm for efficient decoding
quantization is proposed and tested.

427

used to decode the duobinary code; this is in contrast withbinary codes, which lose about 0.3-0.4 dB when decoded with
the Max-log-MAP algorithm [7]. Additionally, duobinary
codes are less impacted by the uncertainty of the starting andending states when using tail biting, and perform better thantheir binary counterparts when punctured to higher rates.
A. DVB-RCS Encoder Structure
The block diagram of the turbo encoder that is used by
DVB-RCS is shown in Figure 1, and the CRSC constituent
encoder is shown in Figure 2 as described in the standardized
DVB-RCS. The encoder is fed blocks of k message bits which
are grouped into N = k/2 couples. The number of couples per
block can be N ∈ {48, 64, 212, 220, 228, 424, 432, 440, 752,
848, 856, 864}. The number of bytes per block is N/4. In Figure
2, A represents the first bit of the couple, and B represents thesecond bit. The two parity bits are denoted W and Y [8].

II. DVB-RCS TURBO CODES
DVB-RCS turbo encoder is composed of two identical
Recursive Systematic Convolutional (RSC) encoders alongwith Log-MAP or Max-log-MAP decoding [3]. If the encoder
begins and ends at a known state, such as the all-zeros state, thedecoder for each constituent code performs better. One
alternative to do this is by independently terminating the trellisof each encoder with a tail, which forces the encoder back to the
all-zeros state. However, forcing the encoder to a known stateat the end of the encoding stage by adding tail bits, presents two
major drawbacks: First, the minimum free distance dfree is nolonger equal to the original minimum free distance for all
information data. Second, the spectral efficiency of thetransmission is degraded specially for small frame lengths
supported by DVB-RCS [4]. The other alternative to terminate
the trellis of the code is done by using Circular Recursive
Systematic Convolutional (CRSC) encoding [5]. CRSC start
the encoding at the circular state Sc, and end the encoding in thesame state without the aforementioned drawbacks.
DVB-RCS turbo code uses duobinary constituent encoders
defined over GF(4) instead of using binary encoders definedover GF(2). The double binary turbo codes have several
benefits compared with classical turbo codes, which use RSC
single binary codes, [6]: (a) Reducing the correlation effects
between the component decoders, improves the performance.
(b) Introducing periodic disorder in the symbols increases theminimum free distance. (c) Duobinary codes are less sensitive
to puncturing than the single binary codes; hence puncturing
can be used to increase the code rate and data rate. (d) The
trellis contains half as many states as a binary code of identicalconstraint length (but the same number of edges), and therefore
needs half as much memory, and the decoding hardware can beclocked at half the rate as a binary code. (e) Suboptimal but
efficient Max-log-MAP algorithm at a cost of only about
0.1-0.2 dB relative to the optimal log-MAP algorithm can be

Fig. 1. Block diagram of DVB-RCS Turbo encoder.

Fig. 2. Duobinary CRSC constituent encoder used by DVB-RCS.

The block must be encoded twice by each constituent
encoder because of the tail biting nature of the code. First, theencoder is initialized to the all-zeros state, S0 = [0 0 0]. After theblock is encoded, the final state of the encoder SN is used toderive the circulation state. The circulation state Sc is given by:

where

Sc = (I + GN)-1SN(1)1 0 1G  1 0 0
0 1 0

(2)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

In practice, the circulation state Sc can be found from SN byusing a lookup table [2]. Once the circulation state is found, thedata is encoded again. This time, the encoder is set to start instate Sc and will be guaranteed to also end in state Sc.
B. Turbo Decoding Algorithms
In a typical turbo decoding system, two decoders operate
iteratively and pass their decisions to each other after each
iteration. These decoders should produce soft-outputs toimprove the decoding performance. Such a decoder is called asoft-input soft-output (SISO) decoder [9]. Each decoder
operates not only on its own input, but also on the otherdecoder’s incompletely decoded output, which resembles theoperation principle of turbo engines. Generally, we assume thatthe encoded information sequence, Xk, is transmitted over an
additive white gaussian noise (AWGN) channel, and a noisy
received sequence, Yk, is obtained. In binary case, each decoder
calculates the log-likelihood ratio (LLR) for the kth data bit dk,
as follows: P(d k  1 | Y ) L(d k )  log  P(d k  0 | Y ) 428

1 k ( S k )    k 1 ( S k 1 ) j ( S k , S k 1 )

(7)

j 0

The branch metric for each possible transition can be calculated
as:
 2

( y kS x kS (i)  y kp x kp (i, S k 1 , S k )) (8)N o i ( S k 1 , S k )  Ak P( S k | S k 1 ) exp where Ak is a constant, x kS and x kp are the transmitted systematicSpdata and parity bits at the transmitter side, and yk , yk are thereceived noisy bits at the receiver side, respectively.

2) Log-MAP Algorithm
It is a simplified version of MAP algorithm to avoid themathematical computations complexity. Log-MAP performs
the calculations in the logarithmic domain by replacing the
exponential and logarithm by the max* operator as follows:
max* ( x, y)  ln(e x  e y )  max( x, y)  log(1  e | y  x| )

(9)

(3)

where the term log (1+e-|y-x|) is a correction function that can becalculated by using look-up table.

The LLR can be decomposed into three independent terms as:3) Max-Log-MAP algorithm
It approximates the computation of max* operator in
Log-MAP algorithm for the sake of simplicity by omitting thecorrection term, log (1+e-|y-x|) to become as follows:

L(dk) = Lapri(dk) + Lc(dk) + Le(dk)(4)

where Lapri(dk) is the a priori information of dk, Lc(dk) is thechannel measurement, and Le(dk) is the extrinsic informationexchanged between the constituent decoders. LLR
computations can be performed by using one of the two main
turbo decoding algorithms: Soft Output Viterbi Algorithm(SOVA) [10] and Maximum A posteriori Probability (MAP)[3]. The difference between the two algorithms is that, MAP
algorithm seeks for the most likely data sequence, while SOVA
seeks for the most likely connected trellis path. MAP algorithm
is superior to SOVA specially at low SNR at the expense of
implementation complexity.
1) MAP Algorithm
MAP is the optimal but computationally complex algorithm.
According to this algorithm, LLR values for each information
bit can be calculated as:    1 ( S k 1 , S k ) ( S k 1 )  ( S k ) S SL(d k )  ln  k k 1
  0 (S k 1 , S k ) (S k 1 ) (S k ) 
 S k S k 1
(5)

where  is the forward state metric,  is the backward state
metric,  is the branch metric, and Sk is the trellis state at time
instant k. At state k, the forward state metric, k(Sk) is given by:1 k ( S k )    k 1 ( S k 1 ) j ( S k 1 , S k )j 0

The backward state metric, k (Sk) is given by:(6)ln(e x  e y )  max( x, y)(10)

From the hardware implementation point of view, thecomplexity is reduced at the expense of decoder performancedegradation [7]C. DVB-RCS Decoder Structure
The decoding process of turbo codes involves the iterative
exchange of extrinsic information between the two component
decoders. In binary case, only two types of transmitted symbols
are possible, 0 or 1. In the decoding process of DVB-RCS code,
the case is duobinary which is more complicated. In this case,four types of transmitted symbols are possible, (00, 01, 10, or
11). The corresponding likelihood ratios are as follows:P(d k  00 / y) P(d k  00 / y )  P(d k  00)
P(d k  00 / y) P(d k  00 / y )  P(d k  00)
P(d k  01 / y ) P(d k  01 / y )  P(d k  01)
P(d k  00 / y) P(d k  00 / y )  P(d k  00)

(11)
(12)

P(d k  10 / y ) P(d k  10 / y )  P(d k  10)
P(d k  00 / y) P(d k  00 / y )  P(d k  00)(13)

P(d k  11 / y ) P(d k  11 / y )  P(d k  11)P(d k  00 / y) P(d k  00 / y )  P(d k  00)(14)

where, dk represents the transmitted symbol at time instant k,
and y is the received continuous valued noisy symbol.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

Performing the decoding in the log-domain is more preferredthan in the probability domain since the low complexity
Max-log-MAP algorithm can then be applied [3]. Unlike thedecoder for a binary turbo code, which can represent eachbinary symbol as a single log-likelihood ratio, the decoder for aduobinary code requires three log-likelihood ratios. For
example, the likelihood ratios for message couple (Ak, Bk) canbe represented in the form:
 a ,b ( Ak , Bk )  log

P( Ak  a, Bk  b)
P( Ak  0, Bk  0)

(15)

429

trellis stage k and state Si, while α’k+1(Sj) is the forward metricat trellis stage k + 1 and state Sj prior to normalization. The
forward recursion is given by: k' 1 (S j )  max *{ k (S i )   k ( S i  S j )}Si S j(17)

The forward metrics are normalized with respect to themetric stored in state zero after computing the forward
recursion for all Sj at time k+1as follows: k 1(S j )   k' 1 (S j )   k' 1 (S 0 )(18)

Again, let ’k+1(Sj) denote the normalized backward metric at
where (a, b) can be (0, 1), (1, 0), or (1, 1).
trellis state k+1 and state Sj and ’k(Si) denote the backward
Figure 3 shows the iterative decoder that can be used to metric at trellis state k and state Si prior to normalization. The
decode the DVB-RCS turbo code. {Λ a,b(i) (Ak, Bk)} denotes the backward recursion is given by:
set of LLRs corresponding to the message couple at the input ofthe decoder and {Λ a,b(o) (Ak, Bk)}is the set of LLRs at the output(19)
 k' (S i )  max *{ k 1 ( S j )   k (S i  S j )}S Sof the decoder. The input LLR values are provided to each
decoder along with the received values of the parity bits
The backward metrics are normalized with respect to thegenerated by the corresponding encoder (in LLR form). The metric stored in state zero after computing the backward
decoder can produce the updated LLRs {Λ a,b(o) (Ak, Bk)} at its recursion for all Si at time k as follows:
output by using these inputs and the knowledge of the code
constraints. As with binary turbo codes, extrinsic information is(20)
 k (S i )   k' (S i )   k' (S 0 )passed to the other constituent decoder instead of the raw LLRs.
This prevents the positive feedback of previously resolved
The sets of forward and backward metrics are then stored andinformation. Extrinsic information is found by simply used to find the LLR values according to (15). For each branch
subtracting the appropriate input LLR from each output LLR, the likelihood ration can be computed as follows:
as indicated in Figure 3.
It is fairly straightforward to extend the log-MAP and(21)
Z k (S i  S j )   k (S i )   k (S i  S j )   k 1 (S j )max-log-MAP algorithms [3] to the duobinary case. Eachbranch must be labeled with the log-likelihood ratios
For message pair (Ak, Bk) = (a, b) the likelihood is calculated
corresponding to the systematic and parity couples associated as:with that branch. Because QPSK modulation is orthogonal, the
LLR of message couple (A,B) can be initialized prior to being(22)t k (a, b)  max* {Z k }Si S j :( a ,b )fed into the first decoder as Λ a,b(i) (Ak, Bk) = a Λ(Ak) + b Λ(Bk),
where Λ(C) = log[P(C = 1)/P(C = 0)]. Since the extrinsic
And the possible values for (a,b) are 01, 10, or 11. At the
information about the parity bits is not exchanged, the parity decoder output, the LLR value is given by:bits can always be decomposed in a similar manner. However,for the systematic bits, the three likelihood ratios defined in (15)(23)
(ao,)b ( Ak , Bk )  t k (a, b)  t k (0,0)
must be calculated during each iteration and exchanged
between the decoders.
After the iteration process is completed, either by fixed
Now let γk (Si → Sj) denote the branch metric corresponding number of iterations or based on some convergence criterion,
to state transition Si → Sj at time k. The branch metric depends the LLR of each bit in the couple (Ak, Bk) is computed to takeon the message and parity couples that label the branch along the final decision by comparing them to threshold:with the channel observation and extrinsic information at the
decoder input. In particular, if transition Si → Sj is labelled by ( Ak )  max* (1o,0) ( Ak , Bk ), (1o,1) ( Ak , Bk ) max* (0o,)0 ( Ak , Bk ), (0o,1) ( Ak , Bk )
(Ak,Bk,Wk, Yk) = (a, b,w, y) then the branch metric γk (Si → Sj) is(24)
given by:( Bk )  max* (0o,1) ( Ak , Bk ), (1o,1) ( Ak , Bk ) max* (0o,)0 ( Ak , Bk ), (1o,0) ( Ak , Bk )
(25)
 k (S i  S j )  (ai ),b ( Ak , Bk )  w(Wk )  y(Yk )(16)
where Λ 0,0(o) (Ak, Bk) = 0.
ij

Now Let αk(Si) denote the normalized forward metric at

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

Duobinary
CRSC
Decoder1 Λ a,b(o) (Ak, Bk)-

Interleaver

Λ(Wk,1) Λ(Yk,1)
Λ a,b(i) (A’k, B’k)

Λ(Wk,2) Λ(Yk,2)Duobinary
CRSC
Decoder2

Λ a,b(o) (A’k, B’k)

De-Interleaver

Λ a,b (Ak, Bk)
(i)

430

Convertto Bitwise LLR

Λ(Ak)
Λ(Bk)

Fig. 3. A decoder for the DVB-RCS code.

In the design phase of turbo codes, good results can beachieved through floating-point software simulations. On theother hand, the efficient hardware implementation of
DVB-RCS turbo decoder means, achieving the best
performance in terms of area, speed, and low power
consumption without loss of error correction capability. The
decoding of turbo codes is an iterative process and each
iteration needs a lot of processing and calculations. This adds
challenge for hardware implementation including hugeprocessing and storage requirements. There is always a tradeoff
between hardware complexity and decoder capability in orderto achieve the most efficient implementation of the decoder. At
different abstraction levels, some design modifications have to
be done including algorithmic, architecture, and circuit levels.
Fixed point representation might be realizable and preferable
for this reason, during hardware implementation phase; circuits
then can process the data in finite precision. Finding a fixed
point model that has all bit-widths as small as possible under
the condition of an acceptable degradation in coding
performance is the primary goal when implementing quantized
decoder [11]. In terms of speed, area and power consumption
the smaller the bit-width of quantization, the better is the
decoder performance. In the same time the performance of thedecoder is also affected, for this reason, the quantization shouldbe optimized to control the complexity of the implementation.
For the decoding of turbo codes as it was mentioned before,
at the algorithmic level also, a lot of modifications have been
done already on decoding algorithms [7]. Avoiding thenumerical problems of MAP decoding algorithm, additions
instead of multiplications can be used in calculating theextrinsic metric of Log-MAP decoding algorithm. For the sakeof more complexity reduction, quantized Max-Log-MAP
decoding algorithm can be implemented.
IV. SIMULATIONS AND RESULTS
The performance of the floating-point simulation of
DVB-RCS turbo codes is first evaluated using matlab computer
based simulation. The performance has been evaluated in termsof bit error rate (BER) against bit energy Eb in an additive white

Gaussian noise (AWGN) channel, having single-sided power
spectral density No. All the block lengths stated by thestandards ranging from 48 to 864 message couples have been
included in this simulation. The modulation type is quadraturephase shift keying (QPSK). At the receiver side, a maximum often decoding iterations have been performed. Figure 4 shows
the influence of the block size on the BER curve at code rate 1/3
while Figure 5 and Figure 6 show the influence of the code rateon the BER curve for two different frame lengths of 48, 212
message couples on the BER. Results are shown for all sevencode rates when the block sizes are N = 48 and 212 message
couples, respectively, and ten iterations of Max-Log-MAP
decoding are performed.

BER for DVB-RCS, rate 1/3 Turbo codes

010

N = 48 couples
N = 64 couples
N = 212 couples
N = 220 couples
N = 228 couples
N = 424 couples
N = 752 couples
N = 864 couples

-1

10

-2

10

BER

III. DVB-RCS FINITE PRECISION TURBO DECODER

-3

10

-4

10

-5

10

-6

10

0

0.5

1

1.5
2
Eb/No in dB

2.5

3

3.5

Fig. 4. Influence of block size on the BER performance.
To evaluate the performance of the quantized decoder,another matlab computer based simulation has been driven tocompare the performance of the quantized decoder with thefloating point representation. The simulation was run on
different ranges of 4-bit quantized decoder’s LLR input fordifferent coding rates. Results showed that for 4-bit
quantization, the performance is getting worst as the range isincreased. The maximum and minimum of input LLR values

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

are observed at different points of SNR, and it was observedthat the input LLR values are linearly increasing with the SNR.
This observation helped in the estimation of the quantization
range. Better estimate of the quantization range can be madebased on the SNR values using that observation by introducing
SNR-dependent variable factor and multiplying that factor with
the LLR input values before the decoding process. As the SNR
increases, that multiplier factor should be smaller. The
decoding process is then applied on the input LLR values after
the multiplying process.

routine are scaled by multiplying them with that scaling factor
first. The max* operator within the Log-MAP decoding scheme
is then computed by introducing a constant correction factor. It
should be noticed that the computation of the max* operator in
the decoding algorithm constitute a significant portion of thedecoding complexity [12].
The estimate of the correction factor in our simulation isbased on [16]. The computation of the max* operator
considering that correction factor is given by:
TABLE I
REDUCED COMPLEXITY ALGORITHMS FOR TURBO DECODING

BER for DVB-RCS Turbo codes, 48 message couples

0

431

10

r = 1/3
r = 2/5

Decoding Algorithm

Correction factor, fc(|y-x|)r = 1/2-1

10

r = 2/3

MAX-Log-MAP [3]r = 3/4
r = 4/5-2

100Constant Log-MAP [13]BER

r = 6/7

Linear Log-MAP [14]-310

Average Log-MAP [15]
-4

10

-5

10

0

1

2

3
4
Eb/No in dB

56max*(x,y) = max(x,y) + fc(|y-x|)

7Fig. 5. Influence of block size on the BER performance.
BER for DVB-RCS Turbo codes, 212 message couples

0

10

where x and y, are the LLR input values, and fc(|y-x|), is thecorrection function. For Log-MAP algorithm, the max*
operator can be computed as: 0, if | y  x | T
max* ( x, y )  max( x, y)  C , if | y  x | T

-1

(26)

(27)

10

where C is the correction factor and T is a threshold value, C =0.5, T = 1.5 based on [16].

-2

BER

10

-3

10

r = 1/3
r = 2/5
r = 1/2
r = 2/3
r = 3/4
r = 4/5
r = 6/7

-4

10

-5

10

0

0.5

1

1.5

2

2.5
3
Eb/No in dB

3.5

4

4.5

5

Fig. 6. Influence of block size on the BER performance.
As it was mentioned before, Max-Log-MAP simplifies the
Log-MAP algorithm by omitting the correction factor. A lot of
researches have been done aiming to reduce the complexity ofthe decoding algorithm by approximating the correction factor
with different methods. Table I presents the most important
reduced complexity turbo decoding algorithms. In our research,
we have tried also another technique to estimate the appropriatequantization range by introducing a scaling factor in the
decoding process. The input LLR values fed to the decoding

To measure the performance of the decoding algorithm withconstant correction factor for the scaled LLR input values,
another matlab code was built to study the effect of the scaling.
It was observed that a better performance for the decoding
process can be achieved when multiplying the correction factor
with the same scaling factor in the decoding process.
Accordingly, we propose a change in the approximation of themax* operator computed by (26), to become as follows: 0, if | y  x | T
max* ( x, y )  max( x, y )  K , if | y  x | T

(28)

where, K = k . C.
The same operation has been done but this time on different
code rate. Figure 7 shows the performance of the quantized 212
blocks of rate 6/7 compared with the floating-point BER. It was
found that as SNR increases, the performance of the quantizedcode worsen. It has also been observed from the calculations
that the input LLR values for the code rate 6/7 are higher thanthose of rate 1/3. This is an indication that varying the input

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014

LLR range as a function of SNR gives good results, but the best
performance could be obtained when the code rate is also
considered. Therefore, the quantization range should be scaled
by a factor including both the SNR and code rate. In the sametime, fixed quantization range couldn’t give the best
performance for all code rates.
BER for DVB-RCS Turbo codes, 212 message couples

010

floating-point
quantized
-1

BER

10

-2

10

-3

10

possible different codes rates supported by DVB-RCS
standards, ranging from r = 1/3 to r = 6/7. For each code rate,
the maximum and minimum LLR input values have been
calculated at different SNR points. Uniform quantization has
been made by using 4-bits. After that, we applied the modified
MAX-Log-MAP decoding algorithm based on the proposed
modification in this paper. A fixed scaling factor of 0.75 wasused to perform this simulation. The performance has been
evaluated in terms of bit error rate (BER) against bit energy Eb
in an additive white Gaussian noise (AWGN) channel, having
single-sided power spectral density No. The modulation type is
QPSK. At the receiver side, a maximum of ten decoding
iterations have been performed. It can be noticed that thedifference in the performance between the floating-point codes
and the quantized codes using the proposed algorithm in thispaper is less than 0.1 dB. This gives better performancecompared with [17] in which the authors obtained 0.4 dB
improvement over the standard Max-Log-Map algorithm at
BER of 10-4 for code rate 1/3.
BER for DVB-RCS turbo codes, 212 message couples

0

1

1.5

2

2.5

3
Eb/No dB

3.5

4

10

4.5

Fig. 7. Floating-point vs. quantized decoder at rate 6/7.

r = 1/3 Floatingr = 1/3 Quantizedr = 2/5 Floatingr = 2/5 Quantizedr = 1/2 Floatingr = 1/2 Quantizedr = 2/3 Floatingr = 2/3 Quantizedr = 3/4 Floatingr = 3/4 Quantizedr = 4/5 Floatingr = 4/5 Quantizedr = 6/7 Floatingr = 6/7 Quantized-1

10

From the hardware implementation point of view, there is atradeoff between the performance and resolution. For a given
code rate, smaller decoder input range affects the decoder
performance, while higher range affects the resolution. Basedon the fact that the decoder performance of the quantized codeis affected by both of the code rates and the value of SNR, wedevelop an algorithm to estimate the efficient quantization of
DVB-RCS turbo decoder. Figure 8 shows a flow-chart of our
proposed algorithm for the decoder quantization.

-2

BER

10

-3

10

-4

10

-5

10

Code rate=r
Block size=b
SNR

432

1

2

3

4
Eb/No

5

6

7

Fig. 9. Influence of quantization on variable rate DVB-RCS Turbo
codes.

V. CONCLUSION
Find LLR Max&Min

Make the quantization basedon linearly spaced steps

Start the decoding
max*(x,y) = max*(x,y) + K (|x,y|)

Increment SNR

Fig. 8. Proposed algorithm for efficient decoder quantization.
The performance comparison between variable-rate
floating-point codes and quantized version of those codes isshown in Figure 9. The simulation was run on blocks of 212
message couples for both of floating point codes and quantizedcodes using our proposed algorithm. We considered all theIn this paper, an algorithm that computes the quantization
range for an efficient decoder quantization has been proposed.
The algorithm efficiently estimates decoder quantization of
DVB-RCS turbo coding that reduces the complexity of thedecoder and overall power consumption in the realization of
DVB-RCS radios. The algorithm is based on the modificationthat improves the BER performance of Max-Log-MAP for
fixed point decoder. Performance simulation for different
supported code rates of such codes has been presented, andresults showed that the quantized decoder essentially matches
the performance of the floating point decoder.

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 2, March 2014[2]

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microprocessors, FPGAs, and wireless communications. He is involved in
Cognitive radios, SDRs, Coding Theory, Digital VLSI, VHDL, FPGA and
Digital Signal Processing research.

Dr. Sherif Welsen Shaker, university of
Nottingham Ningbo, China, Faculty of Science andEngineering. Sherif Welsen is currently a teaching
fellow in the department of Electrical and Electronics
Engineering. He obtained his Masters degree ofscience in Electronics and Telecommunications
Engineering from the Arab Academy for Science andTechnology and Maritime (AAST), Cairo, Egypt. He
then joined Ain-Shams University, where, he got his
PhD in Electronics and Electrical Communications
engineering in Jan 2010. His research was focusing on
Software Defined Radios in wireless communication and in particular on"generic low power FPGA implementation of coding schemes for emerging
wireless standards". Dr. Welsen worked as research fellow at Kuang-Chi
Institute of Advanced Technology, Shenzhen, Guangdong, China from 2011 to2013, and as assistant professor at Modern Academy of Engineering andTechnology from 2004 to 2010. He was part-time research professor at
Ain-Shams University, Cairo, Egypt from 2010 to 2011 in the project“DVBT-2 Systems and Cognitive Radio Networks Sharing the Same
Frequency Band”. He was the founder of many labs in the academia including

Copyright ⓒ 2014 GiRI (Global IT Research Institute)