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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
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Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
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Prof. Junhui Zhao, Beijing Jiaotong University, China
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Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 5

6J

1 Ontology Modification Using Ontological-Semantic Rules

902

Anastasia Mochalova*, Victor Zacharov**, Vladimir Mochalov** Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS , Mirnaia str. 7, 684034
Paratunka, Kamchatka region, Russia, **Petersburg State University, Universitetskaya emb. 7-9., 199034 St
Petersburg, Russia

2 A performance analysis of optimized semi-blind channel estimation method in OFDM systems

907

Sangirov Gulomjon*, Fu Yongqing*, Jamshid Sangirov**, Fang Ye* and Ahmad Olmasov****Information and Communication Engineering College, Harbin Engineering University, Harbin, 150001 China**Samsung Electronics, South Korea, ***Samarkand branch of Tashkent University of Information
Technologies, Uzbekistan

3 Terminal-based Energy-Efficient Resource Allocation in OFDMA-Based Wireless Multicast

913

Systems
Jun Liu
Research and Application Innovation Center for Big Data Technology in Railway, Institute of Computing
Technologies, China Academy of Railway Science, Beijing, China

4 Innovation, Convergence and the Disenfranchised: Investigating the Inclusiveness of921

Convergence in Malaysia
Kamarulzaman Ab. Aziz**Faculty of Management, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia

5 Performances of Polar Codes in Steganographic Embedding Impact Minimization

927

Birahime Diouf, Idy Diop, Sidi Mohamed Farssi
Department of Computer Science, Polytechnic Institute (ESP) / Cheikh Anta Diop University (UCAD), Dakar,
SenegalCopyright ⓒ 2016 GiRI (Global IT Research Institute)

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

902

Ontology Modification Using
Ontological-Semantic Rules
Anastasia Mochalova*, Victor Zacharov**, Vladimir Mochalov** Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS , Mirnaia str. 7, 684034 Paratunka,
Kamchatka region, Russia**Petersburg State University, Universitetskaya emb. 7-9., 199034 St Petersburg, Russia
stark345@gmail.com, v.zakharov@spbu.ru, sensorlife@mail.ru

Abstract—In this work we consider different types ofsemantic dictionaries and describe the problems of theirconstruction. We also describe the ontological-semantic rules
proposed for ontology modification. We provide examples ofsuch rules and describe the process to generate them. The
software implementation of ontology modification using
ontological-semantic rules is employed as a component of aquestion answering system integrated with the ontology.
Keyword— ontology modification, semantic analyzer, basic
ontological-semantic rules.

I. INTRODUCTION

W

development of information technologies, thechallenges of automated processing of natural
language text become more and more urgent. One of the
problems of automated text processing is the problem oforganizing data storage in a structured way, with various
additional information about stored elements — such as
relations between these elements, names of these relations,hierarchical dependencies between them, qualificative
semantic information etc. Ontologies that are used in many
problems of automated text processing (for example, indevelopment of question answering systems ([1-3]) andinformation retrieval systems ([4]), in classification systems([5]) and estimation of texts resemblance ([6], [7]), in
plagiarism detection ([8]) and disambiguation ([9]), in
problems of Semantic Matching in Search [10] andinformation retrieval [11] etc.), are storages of such kind.
Also, ontologies are essential components of Semantic Web
technology [12] that becomes more and more popular lately.
Ontology creation and modification is a separate problem
ITH———————————————————————
Manuscript received June 10, 2016. The work was implemented withfinancial support from the Russian Foundation for the Humanities as part ofresearch project No.15-04-12029 ― Software development of an electronic
resource with an online version of a Russian-language question answeringsystem.
Mochalova A. V., is with the Institute of Cosmophysical Researchand Radio Wave Propagation FEB RAS, Russia (corresponding author
to provide phone:+7-924-794-21-82; fax: 8-(41531)-33718; e-mail:stark345@gmail.com).
Zakharov V. P, is with the Saint-Petersburg State University, Russia(e-mail: v.zakharov@spbu.ru).
Mochalov V. A., is with the Institute of Cosmophysical Research andRadio Wave Propagation FEB RAS, Russia (e-mail: sensorlife@mail.ru).

that does not have a universal solution in our days. One candistinguish two approaches: manual input (which is a very
effort-consuming task requiring contribution from highly
qualified specialists who know the domain of the ontologywell) and automated input. The authors of the work [13]divide automated data input to the ontology into two stages:
1) automatic or automated input using conventional
lexicographic information (encyclopaedic, definition andother dictionaries as well as databases);2) automatic or automated input using analysis of
distributional vocabulary characteristics in corpus oftexts.
In this work we propose a method for automatic of an
object-oriented
ontology
modification
using
ontological-semantic rules of the latter type.
In this work, by ontology modification we mean any of thelisted below changes of the ontology:
1) Name change of a Class, Object or relation of the
ontology;2) Removal of a Class, Object or relation from the ontology;3) Addition of a new Class, Object or relation into the
ontology;
We propose the following scheme of ontology modification(see Fig. 1):
1) User feeds the analyzed text in natural language into thesystem's input;
2) The text proceeds into the input of the
ontological-semantic analyzer (the detailed work
description of such analyzer is provided in work [2]). The
result of the analyzer's work is an ontological-semantic
graph (a semantic graph, each node of which has acorresponding class or an object of the ontology).
3) The resulting ontological-semantic graph is fed into the
input of the module of automated ontology supplement,
which, using special ontological-semantic rules,
constructs a request for ontology modification (thedetails of such request construction and of the
ontological-semantic rules will be provided further).
In its work, the ontological-semantic analyzer uses datafrom the ontology (see [2] for details), together with themodule of automated ontology supplement (theontological-semantic rules lying in the base of the module use
information from the ontology).
In our work, we developed and implemented in software a

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

prototype of an ontology modification system using
ontological-semantic rules. This system is employed as acomponent in a question answering system integrated with the
ontology. The software implementation of the ontology
modification system is based on an expert system (theprogram [14] has been registered) and a semantic analyzer(the program [15] has been registered).

903

Which relationships are described in a thesaurus? As a rule,
the following:1) AKO relationship (see examples in Fig. 2)2) POF relationship3) synonymy/antonomy4) associative relationships.
Relationships stored in ontologies are much more numerous
and various.

Fig. 2. Example of an AKO relationship

These are paradigmatic relationships (stable relationships
between words in a language or in a text). Syntagmatic(textual) relationships are not presented in the thesaurus in an
explicit form.

Fig. 1. Workflow of the system implementing automated ontology
supplement.

II. SEMANTIC DICTIONARIESThe work quality of a question answering system strongly
depends on completeness and accuracy of the data stored insemantic dictionaries used by the system. These dictionaries
are employed in different modules of the QAS, including thesyntactic analyzer [16]. In this section we will consider
approaches to construct the thesaurus and role models
dictionaries; all of them are kinds of semantic dictionaries.
A. A general-purpose thesaurus
A thesaurus in its general sense is a dictionary with semantic
relations between dictionary units. Since the end of 1950s,
thesauri have been used in systems for machine translationand information retrieval systems (IRS).
In contrast with semantic dictionaries that are intended for
detailed description of general lexis, thesauri are created tostore and classify the ultimately concrete words andcollocations. For example, the word вещество [substance] isin the RGPSD (Russian General-Purpose Semantic
Dictionary), while all names of chemical substances are
stored in a thesaurus.

B. Role models dictionaries
Let us employ such approach to semantic text analysis thata sentence is considered as some predicate and a set ofarguments. Usually a verb (or another predicate word, e.g., averbal noun) describing an action acts as a predicate, while
actants are the arguments.
When one has constructed a dictionary of verbal rolemodels basing on usage of syntactic and morphologicalinformation, it is possible to define roles of nominal groups(arguments) by the predicate, as well as relations between
them. For example, one can employ information about apreposition used with the nominal group and the case of themain word of the group. Nevertheless, syntactic information
is not always sufficient. Consider an example: “Мы прибылина автобусе на конференцию на пять дней” [We arrived bybus to a conference for five days]. An example of semantic
parsing of this sentence is presented in Figure 3.

Fig.3. Example of semantic analysis.

The formal attributes (preposition+case) of some nominalgroups coincide, so for correct interpretation of suchsentence, one additionally needs a thesaurus. In such way, we
obtain the following possible appearance of a dictionary entry
presented in Table 1. (We assume the occurrence of athesaurus containing categories “means of transportation” and“time interval”).
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

III. SEMANTIC RELATIONSIn this work we use the term semantic relation (defined
below) as the relation defining a link of type “Class-Class”,
TABLE IAN EXAMPLE OF A DICTIONARY ENTRY FOR THE VERB “ARRIVE”.
Role of the verb
Preposition + case ofClass of the nominal
the nominal group
group
Mean
На [by] +Means ofPrepositional casetransportation
DurationНа [for] +Time interval
Accusative caseObject localization
На [to] + Accusative Eventcase“Class-Object”, “Object-Class” or “Object-Object” in theconsidered object-oriented ontology.
By semantic relation we mean a certain universal relationthat a native speaker beholds in the language. This connection
is binary: it connects two semantic nodes (each of which is aClass or an Object of the ontology) with each other [17]. By
semantic nodes we mean syntaxems (syntaxem is anirreducible semantic-syntactic unit conveying primitive
categorical meaning and acting as a structural component of amore complicated syntactic composition [18]). Let us say,that two different semantic nodes α and β are connected by thesemantic relations R (R(α, β)) if there is a universal binaryconnection between α and β [17]. Direction of the connection
is defined so that the formula R(α, β) would be equivalent to
one of the following statements:
1) “β is R for α”;
2) “question R can be asked from α to β“.
Below you can find examples of the semantic relations
equivalent to the first statement:
1) Description(вечер [evening], теплый [warm]);2) Action(дети [children], пошли купаться [went for aswim);
3) Characteristic_of_action(разоделись [dressed], в пух ипрах [to kill]);4) Time(опоздать [be late], на час [for an hour]).
Below you can find examples of the semantic relations
equivalent to the second statement:
1) With_who(прийти [come], с другом [with a friend]);2) What_for(уронил [drop], нарочно [on purpose]);3) Whose(мамин [mother's], шарф [scarf]).
It is obvious that these two types of relations areinterdependent.
IV. ONTOLOGICAL-SEMANTIC RULES
By an ontological-semantic rule (OSR) we mean the rule ofthe form «if A, then B» according to which the expert system
performs actions described in the right side of the rule in casethe conditions described in the left side are held.
Let us put a fact of the expert system into correspondencewith each syntaxeme allocated in the analyzed text. By thefact fi of the expert system we mean a set of six elements:(1) the class or the object of the ontology the syntaxeme
belongs to; (2) morphological characteristics of thesyntaxeme; (3) ontological characteristics of the syntaxeme;(4) syntaxeme position in the analyzed text; (5) link to theprevious fact of the expert system (prev); (6) link to the next
fact of the expert system (next). The syntaxeme with theminimal position in the analyzed text (f0) corresponds to the904

fact that has link to NULL as the link to the previous fact. The
fact with the maximal position in the analyzed text (fn) has
link to NULL as the link to the next fact.
In such way, the analyzed text may be presented as a doubly
linked list of facts F={ f0, f1, f2, …, fn} (see Fig. 4).

Fig. 4. Doubly linked list of facts of the expert system.

The left side of the rule can contain a doubly linked list offacts and/or Boolean functions having these facts as theirarguments.
The right side of the rule contains the list of the actions eachof which can modify the ontology of elements corresponding
to syntaxemes allocated in the analyzed text. A particular caseof the actions performed in the right side of the rule are thefunctions for ontology modification. The full description ofthese functions as well as the functions used in the left side ofthe expert system rules is provided in work [2].
V. ONTOLOGY MODIFICATION USINGONTOLOGICAL-SEMANTIC RULES
In the Explanatory Dictionary of the Russian Language by
Ozhegov [19], the verb is defined as “the part of speechdefining an action or a state, expressing this definition informs of tense, person, number (in the present tense), gender(in the past tense) and forming participles and adverbial
participles”. The examples from this section of ontology
modification are taken from work [20]. We suggest to create
OSRs with left sides containing the facts corresponding to thesyntaxemes which are verbs. In Fig. 5 we show somefunctions for work with the ontology (the arrow drawn fromthe ontology to a function means that the function extracts
information from the ontology; the arrow drawn in the
opposite direction means that the function modifies datastored in the ontology).
Below we consider an example of modification of an
ontology part formed using the following functions (in square
brackets we provide functions explanation):
1) CreateClass(172, "страна [country]"); [Create a class“country” with id = 172]
2) CreateClass(1023, "город [city]"); [Create a class “city”
with id = 1023]
3) CreateObject(462, 172, "Россия [Russia]"); [Create an
object “Russia” with id = 462 belonging to the class with
id = 172 (i. e. the class “country”)]
4) CreateObject(4017, 1023, "Санкт-Петербург [SaintPetersburg]"); [Create an object “ Saint Petersburg” with
id = 4017, belonging to the class with id = 10234 (i. e. theclass “city”)]
5) CreateRelation(7, "Принадлежит [belongs]"); [Create a
relation “Belongs” with id = 7]
6) CreateRelation(1023, 172, 7); [Create a relation with id =7 (i. e. the relation “Belongs” linking the class with id =1023 (i. e. the class “city”) and the class with id = 172 (i.
e. the class “country”))]
7) CreateRelation(4017, 462, 7); [Create a relation with id =7 (i. e. the relation “Belongs” linking the object with id =Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

4017 (i. e. the object “Saint Petersburg”) and the object
with id = 462 (i. e. the object “Russia”))]

Fig. 5. Functions for work with the ontology.

In Fig. 6 we present the ontology part using theabove-described functions. Let us consider the modification
process for the ontology a part of which is presented in Fig. 6.

Fig. 6. An example of an ontology part.

Ontological-semantic rules will be applied to the analyzed
text presented in one sentence “В августе 1914 годаНиколай II переименовал Санкт-Петербурга в Петроград
[In August 1914, Nikolay II renamed Saint Petersburg into
Petrograd]”. With use of the ontological-semantic analyzer
described in work [21], we construct an ontological-semantic
graph of this sentence (see Fig. 7). Each node of the
ontological-semantic graph is a syntaxeme of the analyzed
text, written in the lemmatized form. In curly brackets weprovide the key ontological information about thecorresponding graph node.

Fig. 7. Ontological-semantic graph.

As a result of the expert system operation using OSRs,
modification of the above-decribed ontology will take place:upon expert system performing the OSR containing in its left

905

part the verb “rename”, the name of the object formed
previously by the function CreateObject(4017, 1023,"Санкт-Петербург [Saint Petersburg]") will be replaced with“Petrograd”. The information about exact modifications
performed, together with their date, will be stored in a log file.
Let us consider another example of the work of the expert
system which takes the sentence “В 1878 году по
Сан-Стефанскому мирному договору город Батум
перешел от Османской Империи к России [In 1878, by theTreaty of San Stefano, the Batum city was seded from
Ottoman Empire to Russia]” as its input. Using theontological-semantic analyzer, we will construct thecorresponding ontological-semantic graph on this sentence
and modify the existing ontology using the following
functions:
1) RemoveRelation(552, 7645, 7); [Remove the relation
with id = 7 (i. e. the relation “Belongs”, linking the object
with id = 552 (i. e. the object “Batum”) and the object
with id = 7645 ( i. e. the object “Ottoman Empire”))]
2) CreateRelation(552, 462, 7); [Create a relation with id =7 (i. e. the relation “Belongs”, linking the object with id =552 (i. e. object “Batum”) and the object with id = 462 (i.
e. the object “Russia”))]
Prepositions play a special role when constructing
ontological-semantic rules for ontology modification. A
significant part of semantic relations of verbs with other partsof speech is formed using prepositions. Though prepositions
have abstract meaning, they manage to organize meaningfulcontext when connecting meaningful parts of speech.
Prepositional constructions used to be described from thegrammatical point of view and their semantics used to beneglected. One can hardly mention any corpus-based works
dedicated to the Russian prepositions except for the paper by
Klyshinsky [22], and a couple of others. It is also difficult totransform a set of constructions into a construction-based
dictionary or grammar. To solve this task, one should pay
attention to synonymy and variability of the constructions,variability of their grammatical features, and so on. For
example, different constructions with the verb прятаться [tohide] differ in dynamical-statical aspect (in Russian meaningsof such constructions would depend on the preposition chosen
and on the case of the dependent component), while different
constructions with the verb ударять [to strike] differ inmanner of action (you can strike someone or you can strike
the bell: in Russian, these constructions would include
different prepositions). Treating constructions this way, we
can grasp and describe normal “behavior” of constructions aswell as abnormal cases (like the classical Goldberg's example
to sneeze the napkin off the table [23]).
Below we provide an example showing how, depending on
the context in two different sentences, two different semantic
dependencies could be discovered (which means the ontology
modification should also differ in the first and in the secondcase) with equal arguments:
1) Из-за огромных сугробов, наметенных в последнюю
метель, экспедиция вышла на неделю позже. [Due tothe huge snowbanks drifted by the recent blizzard, theexpedition started a week later.] → REASON(выходить
[start], из-за сугроб [due to snowbank]))2) Из-за сугробов вышла маленькая девочка в сером
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016пальтишке. [A little girl in a gray coat appeared frombehind the snowbanks] PLACE(выходить [appear],из-за сугроб [from behind snowbank])
VI. CONCLUSION
In this work we developed and implemented in software a
prototype of a system for ontology modification using a
database of ontological-semantic rules. This system is
employed as a component of a question answering systemusing data from the ontology. The ontological-semantic rules
for ontology modification were constructed, primarily,
considering peculiarities of verbs and prepositions of theRussian language. In their description the rules use bothmorphological and ontological information about describedobjects. In the future we plan to extend the base of
ontological-semantic rules, and employ context information
as well as morphological and ontological information about
syntaxemes.
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R. Sun, J. Jiang, Y. Fan, T. Hang, C. Tat-seng, C. M. Yen Kan, “Using
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[3] A. Mochalova, “Search for answers in ontological-semantic graph”,
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[4] M.-H. Hsu, M.-F. Tsai, H.-H. Chen, “Query expansion with conceptnet
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[5] A. Panchenko, R. Beaufort, H. Naets, C. Fairon, “Towards Detectionof Child Sexual Abuse Media: Classification of the AssociatedFilenames”, In Proceedings of the 35th European Conference on
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[6] R. Mihalcea, C. Corley, C. Strapparava, “Corpus-based andknowledge-based measures of text semantic similarity”, In AAAI’06,
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Queries”, Foundations and Trends in Information Retrieval, vol. 9,
No. 3-4, pp. 209 – 354, 2015.
[12] G. Antoniu, P. Groth, F. Harmelen, R. Hoekstra, A Semantic Web
Primer, The MIT Press, Cambridge, Massachusetts, London, England,
288 p., 2012.
[13] V.Sh. Roubashkin, V.A. Kapustin, “Usage of term definitions inencyclopaedic dictionaries for automated ontologies supplement”, XI
All-Russian united conference “Internet and the modern society”,
Saint Petersburg, 2008.
[14] A.V. Mochalova, Certificate of registration of a computer program‘Expert system for search of semantic relations in a Russian-language
text using basic semantic rules with removal’, No. 2016612038,
Russia, 17.02.2016
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[15] A.V. Mochalova, Certificate of registration of a computer program‘Program for semantic text analysis basing on basic semantic patterns
with removal’, No. 2015613430, Russia, 28.01.2015.
[16] V.P. Zakharov, A.V. Mochalova, V.A. Mochalov, Question answeringsystems. Some problems of automated text processing, Petrozavodsk:
PIN, 40 p., 2015.
[17] A.V. Sokirko, Semantic Dictionaries and Natural Language
Processing, PhD thesis, Moscow, Russia, 2001.
[18] G.A. Zolotova, Syntactic Dictionary. The repertoire of the elementary
units of Russian syntax, Moscow, Russia: Nauka, 1988.
[19] S. Ozhegov, N. Shvedova, Definition dictionary of the Russian
language: 80 000 words and phraseological collocations, Russian
Academy of Sciences. Institute of the Russian language named after
V.V. Vinogradov. 4th edition, enlarged, Moscow, Azbukovnik, 944 p.,
1999.
[20] V.A. Mochalov, A.V. Mochalova, “Ontology modification using basic
ontological-semantic rules containing verbs”, Theory and practice ofmodern humanitarian and natural sciences. Issue 6: collection ofresearch papers of the annual transregional research and application
conference, Petropavlovsk-Kamchatsky, 08-12 February 2016,
pp.192-195, 2016.
[21] A.V. Mochalova, “Algorithm for semantic text analysis based on basic
semantic patterns with removel”, Scientific and Technical Journal of
Information Technologies, Mechanics and Optics, No. 5. pp. 126-132,
2014.
[22] E.S. Klyshinsky, N.A. Kochetkova, M.I. Litvinov, Maximov V.Yu.,“Automatic construction of collocation database on the base of the big
corpus. Computational Linguistics and Intellectual Technologies”,
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[23] A.E. Goldberg, Constructions at Work: the Nature of Generalizationin Language, Oxford, England: Oxford University Press, 2006.
Anastasia Mochalova was born in Petrozavodsk, Russia,
in 1987. She received the bachelor's degree atPetrozavodsk State University, the master's degree in St.
Petersburg State University of Aerospace Instrumentation.
She is an external PhD student in technical sciences at
Petrozavodsk State University. Her research interests
include automated processing of natural language texts,
development of question-answering systems, automation of ontologies
creation, and development of the semantic analyzer.
Victor Zakharov – born Leningradskaya region, USSR,
17.07.1947. Graduated from Leningrad State University(Specialist in Structural and Applied Lingustics, 1970).
PhD (Saint-Petersburg State University, Applied andMathematical Liguistics, 1997). Major field of scientificresearch is Corpus Linguistics.
He is an Associate Professor, Saint-Petersburg State University. Previous
positions ncluded Deputy Director of the Leningrad Center for Scientific andTechnical Information, Automation Department Chief in th Russian
Academy of Sciences Library. The main publications are as follows:“Corpora of the Russian Language”, Text, Speech and Dialogue:
Proceedings of the 16th International Conference (TSD 2013, Plzen, Czech
Republic), Springer-Verlag (Lecture Notes in Artificial Intelligence, 8082),
Berlin-Heidelberg, pp. 1-13, 2013. “Set phrases: a view through corpora”,
Computational Linguistics and Intellectual Technologies: Proceedings of theInternational Conference “Dialog 2009” , vol. 14 (21). Moscow, pp. 667-682,
June 2015. Current and previous research interests include information
retrieval, natural language processing, and computational lexicography.
Dr. Zakharov is a member of the Russian Society of Information Specialists
and a member of the Special Interest Group on Slavic Natural Language
Processing.
Vladimir Mochalov was born in Lyubertsy, Russia in
1985. He received the Ph.D. degree in electronicengineering from Moscow Technical University ofCommunications and Informatics. His research interests
include networks structure synthesis, artificial
intelligence, bio-inspired algorithms, query answeringsystems, and Big Data.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016907

A performance analysis of optimized semi-blindchannel estimation method in OFDM systems
Sangirov Gulomjon*, Fu Yongqing*, Jamshid Sangirov**, Fang Ye* and Ahmad Olmasov****Information and Communication Engineering College, Harbin Engineering University, Harbin, 150001 China**Samsung Electronics, South Korea***Samarkand branch of Tashkent University of Information Technologies, Uzbekistan
gulomjons@hrbeu.edu.cn


Abstract — Nowadays, one of the effectively used technique inwireless communication area is an orthogonal frequency
division multiplexing (OFDM). In OFDM systems, channel
impairments due to multipath dispersive wireless channels cancause deep fades in wireless channels. Therefore, an accurate
and computationally eﬃcient channel state informationnecessary when coherent detection is involved in the OFDM
receiver. Hence, it is essential to have a good channel estimation
method for OFDM systems in wireless communication. And
normally one of the good channel estimation methods is asemi-blind channel estimation. On the other hand, thesemi-blind method requires a large number of processing
operations. In order to avoid the high complexity of the existing
method, the semi-blind channel estimation has been optimized.
At the receiver side, we calculate subspace decomposition forblind channel estimation and further to improve channel
estimation we use training based technique to estimate channel
state information. Next, we combine these channel estimations
as semi-blind channel estimation methods and we optimized
semi-blind channel estimation by choosing an optimal technique
for training based channel estimation.
Keyword—Semi-blind channel estimation, OFDM, leastsquare and scaled LS

I. INTRODUCTIONn wideband digital communications the orthogonalfrequency division multiplexing (OFDM) is used for
splitting a high-rate datastream into number of lower rate

I———————————————————————
Manuscript received on February 19, 2016. This work is a follow-up of
the invited journal to the accepted out-standing conference paper of the 17th
International Conference on Advanced Communication Technology(ICACT2015).
Gulomjon Sangirov is with Modern Communication and Information
Systems Research group in the school of Information and Communication
Engineering, Harbin Engineering University, Harbin, 150001, China(Corresponding author phone: +86-451-82568266; fax: +86-451-82530010
e-mail: gulomjons@hrbeu.edu.cn).
Yongqing Fu is with Modern Communication and Information Systems
Research group in the school of Information and Communication
Engineering, Harbin Engineering University, Harbin, 150001, China(e-mail: yongqingfu @hrbeu.edu.cn).
Jamshid Sangirov is with Samsung Electronics, South Korea (e-mail:jamshid@kaist.ac.kr).
Fang Ye is with Modern Communication and Information Systems
Research group in the school of Information and Communication
Engineering, Harbin Engineering University, Harbin, 150001, China(e-mail: fangye@hrbeu.edu.cn).
Ahmad Olmasov is with telecommunication engineering research groupat Samarkand branch of Tashkent University of Information Technologies.

streams that are transmitted simultaneously over a number ofsubcarriers for easy transmission. The OFDM technique isapplicable in digital terrestrial multimedia broadcast (DTMB)[1], digital subscriber line (DSL) broadband internet access,wireless network, long term evolution (LTE) [2-3], and 4G
the transmitter modulates the message bit sequences into
phase shift keying (PSK) / quadrature amplitude modulation(QAM) symbol. And then it performs inverse discrete fouriertransform (IDFT) on the symbols for conversion them fromthe frequency domain to time-domain signals. Usually, next
step is the insertion of cyclic prefix (CP) in OFDM system.
The reason for the CP is to avoid intercarrier interference(ICI) which occurs by a multipath channel. And it also
provides good bandwidth efficiency on the receiver side. In
our OFDM system, we use zero padding (ZP) instead of CP.
Generally, the ZP replaces nonzero CP by zeros. The
ZP-OFDM system has the same spectral efficiency as
CP-OFDM system by the condition of the number of zero
symbols equals the CP length. Lastly, the transmitter sends thetime-domain signals out through a wireless channel.
In OFDM system, a wireless channel plays a big role forthe transmission performance. That is why estimating thechannel has a significant impact on the efficiency of thetransmission performance. We observe one of efficientchannel estimation methods that are widely utilized in OFDM
systems is called a semi-blind channel estimation. The
importance of using a semi-blind channel estimation method
is a tradeoff between computational complexity and spectral
efficiency. To accomplish channel estimation numerous
works subspace decomposition methods have been proposed[4-6]. However, these methods use complex computational
schemes that may also reduce spectral efficiency. To improve
the spectral efficiency of the channel a subspace pursuit
algorithm has been proposed estimation in [7]. This algorithm
uses a combination of two algorithms to work for low pilot
density, which makes the implementation complicated. A
method improving the channel estimation with lower
computational difficulty has been introduced in [8].
Nevertheless, it works well when there are few OFDM
symbols. Consequently, for working out all symbols thecomputation amount will be increased. Another method
proposed to decrease channel estimation error by subspaceestimation bases on a block matrix [9]. But, the calculationand formation of a burst of stacked OFDM symbols create
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016s( N ) S/P &
IDFT

1: N

ZP &
P/S
1: D

sZP ( D) Channel rZP ( D) Rem ZP
&
& S/P
AWGN
1: D

DFT & r ( N )
P/S
1: N

908

ChannelEstimator r ( N )andEqualization

Fig. 1 SISO ZP-OFDM system model

extra complexity by increasing the computational power inchannel estimation. A redundant linear precoding based
semi-blind channel estimation is developed in [10].
Moreover, it is complex to construct a matrix for semi-blindchannel estimation. Therefore, we improved the spectral
efficiency and reduced computation complexity of semi-blindchannel estimation in single input single output (SISO)
OFDM systems. With the intention to evaluate the efficiencyof the modified semi-blind channel estimation technique, we
compared the simulation results with conventional channel
estimation techniques.
II. DESCRIPTION FOR SYSTEM MODEL
In this part, we overview OFDM system model. We
consider the system model with a total number of N size of
OFDM subcarriers. Hence, the mth transmitted block of
OFDM
symbols
canbestatedas

S N  m    S1  m  ,, S N  m  , and here []T is the transpose
T

operator. First, we apply IDFT process and the signal will be(1)s N (m)  [ s1 (m), , sN (m)]T  FNH S N  m here FN  (1N )e j 2 kn N is IDFT matrix with size N, where k  0, 1, 2.., are discrete Fourier series coefficients. Then

we add ZP to s N (m) , and after that the OFDM symbol
becomes s ZP (m)  [ s1ZP (m), , sDZP (m)]T , here is D  N  P ,and P is the ZP length.
The FIR filter models channel impulse response
as h D  [h0 , , hD 1 ]T . Normally, the channel order size islonger than ZP size which is
h 0i L(2)
hD  i.
0 elseHence according to (2) channel parameter vector isassumed as h  [h0 , , hL ]T . And in consequence of eachtransmitting OFDM symbol size, the channel vector could be
expressed as following h D  [h0 , , hL , 0, , 0]T .



After transmitting s ZP (m) through the SISO channels, thediscrete-time signal from the receiver antenna is given byLr(t)   h(l )s(t )  n(t )where h(l )   h1 (l ),

(3)

l 0

, hD (l ) and n(t ) is additive gaussian

white noise (AWGN) with zero-mean and variance  n2 .
In the receiver block, the received stacking signal
becomes r(t ), t  mD  1,..., mD  D . Hence, the mthintersymbol interference (ISI) free vector can be obtained by
the insertion of ZP in the transmitted signal as follows(4)
rm  Hs m  n m
where H is filtering matrix with D  D lower triangular

toeplitz matrix with the first column [h0 ,the first row [h0 ,0,, hL , 0,, 0]T and,0] . The mth received block ofsymbols rZP (m) can be expressed as(5)rZP (m)  Hs ZP (m)  n(m).
Supposing frequency synchronization and perfect timing,
the OFDM demodulator eliminates the ZP and then transformthe rest received signals to the frequency domain by DFT for
obtaining the frequency domain ZP free received signals.
After ZP removal from expression (4), the simplified systemmathematical model can be described as
R(m)  H N S N (m)  N(m)(6)
where HN  N FN hN is the channel frequency response
and N(m) is AWGN vector after transformation of DFT.
Now let us consider the transmitted mth received block
block of OFDM symbols passed through multipath fading
channel and mth received signal block became as(7)r(m)  ASZP (m)  n(m)where A = WH , where W is OFDM modulation matrix
which consists of  Wdn WN( N 1 d ) n , n  0,...,N  1 ,d  0,...,D 1 and WNe  j 2 / N and n(m) is gaussian white

noise with variance  2 . And the problem statement here is
the estimation of H channel accurately by using fewer pilot
signals which contained in received signal.
III. THE CHANNEL ESTIMATION
A. Semi-blind channel estimation
First, we estimate the channel in second order statistics
blindly because we do not have any information abouttransmitted signals. The satisfying situation here H can be
identified blindly up to ambiguity matrix A. For subspacechannel estimation we calculate singular value decomposition(SVD) of received stacking signal matrix r (m) according to(7). Or also we can calculate an eigenvalue decomposition(EVD) of autocorrelation matrix of received stacking signalr (m) which we have
R rr  HR ss H H  R nn .

(8)And here R rr , Rss and R nn notations are the autocorrelation
matrix of r (m) , s N (m) , n(m) and R nn   2 I D , and I D is aunit matrix. And the autocorrelation matrices R rr and Rssstand as full rank. Now we get D eigenvalues 1 , , D , after
performing EVD of R rr . As the result we have
1   N  N 1   D   2 in descending order, here

1 , , N is an area for the signal subspace, also N 1 , , D
is an area for the noise subspace. Suppose we can notate the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016noise subspace vector as g i ( 0  i  L  1 ) and due to theorthogonal subspace signal to g i , we can note thatg iH H  0(9)here the channel matrix H has a similar structure withchannel matrix used in (3). Now we can transform (9)equivalently as given(10)
Gih  0here g iH vector is transformed into G i a matrix which
consists of D  D dimension lower triangular toeplitz matrix
with the first column [g 0H , , g HL , 0, , 0]T and the firstrow [g 0H , 0, , 0] and h is same as described in (2).
The calculation of (10) will take more computation,
therefore, computation of G i can be eliminated bytransforming it toL-1

Q =  GiH Gi .

(11)

here h is a true channel with the same size pilot signal. And
the difference of pilot symbols alone are not enough for theestimation of the channel, therefore, the channel estimation ĥ
can be approximated as1hˆ   Q  IQ  h pil  αh pil

In the other hand minimization of q(h) can cause h=0 for thatreason, minimization should be focused on the selection ofproper constraint. Normally natural constraint would beh  1 because in the receiver side the received signal poweris roughly constant in practice. Therefore estimating hchannel is unit-norm eigenvector related to the smallest
eigenvalue of Q which ish H Qh = 0.

(13)
The calculation of blind methods is slow in convergence
rate that is why it is better to use a few pilots for getting thechannel knowledge and get initial channel estimation to
decrease complication of the channel estimation. Assume wehave N pil pilots as frequency domain signal in the transmitter
which is S   S pil (1),frequency, S pil ( N pil )  . At the receiver, received

domainpilot

signals

and α is a relational factor between h pil and ĥ , on the other
hand, α can also be interpreted as optimization matrix whichis actual channel parameter can be approached by h pil .
B. Optimization in semi-blind channel estimation
To obtain the signal and noise subspaces for semi-blindchannelestimation weneedtheactual true
R rr autocorrelation matrix. In the real practice, the R rr isestimated by dividing to M blocks bysquare (LS) channel estimation of pilot signal is Rpil (1)Rpil ( N pil ) (14)
H LS  , ,.
S pil ( N pil )  S pil (1)Now we can write as H pil  H LS and it can be described as
the time domain training based channel estimationHh pil  Fpil
H pil

Rrr 

1 M 1
 rZP (m)rZP (m)H
M m0

(19)

here M is the number of the received signals. The R rr closes
to R rr unlimitedly when M inclines to infinite. And theessential condition for performing EVD decomposition
the R rr must be full-rank. For this reason, we must ensure that
there have to be enough received symbols in the receiver. We
can also describe (19) in the following way(20)
Rrr  HRss HH  Rnn
which is here
1 N 1(21)
Rss   s M (k )s M (k ) H
N k 0
Now we perform EVD of R rr and we obtain new
Q matrix, which we rewrite as Q . Subsequently, the initialtime domain blind channel estimation is an unit-norm
eigenvector related to the smallest eigenvalue of Q which canalso be described by h Q . Thus we can rewrite (13) as follows
hQH QhQ  0.

are

R   Rpil (1), , R pil ( N pil )  . Now we can calculate a least(18)

here I Q is a unit matrix which is equivalent to Q the matrix

i=0

And further calculating it in the quadratic equation q(h)  0
which isq(h)  h H Qh = 0.
(12)

909

(22)

Let us also try to choose optimal computation of LS
method by applying the Scaled LS (SLS) channel estimationfrom [11]HSLS 

tr R HH 

 2 tr  SS H 

1 tr R HH 

H LS

(23)

here R HH is autocorrelation matrix of H， S is N pil pilots(15)

here Fpil is DFT matrix of the pilot signals. This condition istrue when Fpil consists of first L+1 columns of FN . And anumber of selected rows matches with the pilot signals
number.
Let us bring up the system of the equations as
h H Qh  0(16).
Fpil h pil  H pil
Meanwhile, the system of equations are calculated in the leastsquare sense, therefore quadratic equation can be minimizedasq(h)  h H Qh || h  h pil ||2(17)frequency domain signal and tr  is a trace of a matrix.

The tr RHH  trace is not a limiting factor than knowingof R HH , which also less limiting than even knowing of H

itself. In simulation, the condition of knowing tr RHH  canbe replaced by LS estimator which by using the LS-based
consistent instead of H as

  



H
ˆ
tr R
HH  tr H LS H LS .

(24)

Now we can use (24) instead of tr RHH  in (23). A resulting
training based channel estimator become as the LS-SLS
estimator H LS  SLS and we can change to H pil  H LS  SLS .

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

910

Fig.2 MSE results by using QPSK modulation in OFDM system.

Fig.4 MSE results by using16 QAM modulation in OFDM system.

Fig.3 BER vs. SNR results on the basis of QPSK in OFDM system.

Fig.5 BER vs. SNR results on the basis of 16 QAM in OFDM system.

Correspondingly, the channel information in (18) can be
modified as
1
(25)
hˆ
 QI
h  αh .
modified



Q



pil

pil

But, here it is difficult to satisfy the equation (22) because thenoise channel, the Q matrix contain the noise power.
For this reason, let error of (22) be as follows
e  hQ H QhQ .

(26)

Henceforth e  0 , now the orthogonality condition of initialtime domain blind channel estimate and noise subspace isdamaged due to the interference of noise. We consider thesystem estimation error e have been initiated by noise. As aresult, one favorable way for improving the system estimation
performance is removing the estimation error from Q matrix
as follows(27)
Qrecomposed  Q  eIQ .
So a new relational factor coefficient defined by β such asβ  Qrecomposed  IQ

 .
1

(28)

Corresponding to (25), we can achieve optimized semi-blindchannel estimation as follows
hˆ opt  βhˆ pil(29)
Targeting to achieve an improved channel estimation
performance, we can calculate α as in (18) and dip thisrelational factor to (29), as givenhˆ opt  βαh pil .

(30)

IV. SIMULATION RESULTS
In the simulation results for improving the performance ofthe system, we decreased the noise interference and also
modified the channel estimate in proposed semi-blindestimation. During simulation process, we used for each
OFDM symbol N=64 subcarriers and the ZP with the lengthof 16. For the channel, we used the Rayleigh multipath fading
channel. In the receiver part, we used zero forcing equalizer incorresponding to the result of channel estimation on thetransmission channel. And we denoted the channel estimationas (CE) in the figures. Also for getting results in all simulation
runs, we averaged the results by over 4000 Monte-Carlo runs.
In Fig. 2 QPSK modulation is used to compare the MSE of
our proposed semi-blind channel estimation with LS
estimation and conventional semi-blind channel estimation.
The proposed semi-blind channel estimation is optimized by
the LS-SLS channel estimation. We can see from thesimulation results that the optimized semi-blind channel
estimation outperformed the conventional semi-blind channel
estimation by 1.5-2 dB and LS channel estimation by 6-6.5
dB. In Fig. 3 a bit error rate (BER) verses to signal noise ratio(SNR) is calculated and same methods was compared in Fig. 2.
From the result, it is appearing that the proposed semi-blindchannel estimation has a similar result with conventionalsemi-blind channel estimation but still both of them have

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016better results than LS channel estimation. However, they
performed better on 28 dB than 30 dB. Thus we conclude thatsemi-blind and proposed semi-blind CEs performing better inlower 28-30 dB in our system on the basis of QPSK
modulation.
We also applied the QAM 16 modulation in our
simulation for showing our proposed channel estimation
objectively. And we have calculated MSE comparison in
QAM 16 modulation and the simulation result shows that our
proposed semi-blind channel estimation still outperform otherchannel estimation methods. Also, the BER comparison was
calculated in Fig. 5 from the result we can see BER
performance of the proposed semi-blind channel estimation
has also the same result with conventional semi-blind channel
estimation but in higher SNR it is still performing better than
the result of Fig. 3. However, it has less BER performance inlower SNR, the reason for that is 16 QAM modulation scheme.
Because of 16 QAM, modulation scheme consumes moreenergy than QPSK modulation. For instance to transmit four
bits for each 16 QAM symbol rather than the transmitting thetwo bits per QPSK symbol.
In more demonstrating purpose of our proposed method,
we also used normalized estimation mean square errors(NMSE) as expressed in [12]911Fig. 6 NMSE of CEs in OFDM system on the basis of QPSK with Nw =4000.

2ˆ 1
1 Nw h w t  h F
NMSE  min h2t
N w w1(31)

F

where w is a number of iteration,  F is the Frobenius norm
and t taken from ĥ = ht which we called relative coefficient
between estimated and actual channel values. Consequently,
Fig.6 and Fig. 7 have been calculated in NMSE. And theresults show that the proposed method outperforms in NMSE
analysis as well. Therefore, we concluded that the proposed
method is outperforming in both MSE and NMSE cases.

Fig. 7 NMSE of CEs in OFDM system on the basis of 16 QAM with Nw=4000.

REFERENCES

V. CONCLUSION
In this research paper, we simulated a number of estimation
methods for SISO ZP-OFDM system. The purpose of using
ZP features in OFDM is a simplification of the system in theimplementation part. The first condition of the channel is thatit estimated up to ambiguity matrix by using subspacedecomposition. As the simple sequence of the conventionalsemi-blind channel estimation, the second condition was
estimated ambiguity matrix by pilot signals. In the proposedsemi-blind channel estimation method, we eliminatedsubspace decomposition estimation error which is caused bynoise in the channel. Further, we have used training based
LS-SLS channel estimation for optimization of proposedsemi-blind channel estimation. From the simulation results of
MSE and NMSE, we can see that the proposed optimized
semi-blind channel estimation method outperforms
conventional semi-blind channel estimation. And the
proposed optimized semi-blind channel estimation method isapplicable to various modulations such as phase shift keying(PSK) and quadrature amplitude modulations (QAM).

[1]

[2]

[3]
[4]

[5]

[6]

[7]

[8]

ACKNOWLEDGMENT
The authors thank the editors, Prof. Yongqing Fu, Prof.
Fang Ye, and special thanks to Dr. Jamshid Sangirov.

[9]

L. Suyue, J. Xiong, L. Gui, and Y. Xu, “A generalized analyticalsolution to channel estimation with inter-symbol interference
cancelation and co-channel interference cancelation for single input
single output/multiple input single output digital terrestrial multimedia
broadcasting systems”, IEEE Transactions on Broadcasting, vol. 59,
no. 1, pp. 116-128, 2013.
A. Ghosh, R. Ratasuk, B. Mondal, N. Mangalvedhe, and T.
Thomas, ”LTE-advanced: next-generation wireless broadband
technology”, IEEE Wireless Communication, vol. 17, no. 3, pp. 10-22,
2010.
E. Dahlman, S. Parkvall and J.Sköld, 4G LTE/LTE-Advanced for
Mobile Broadband, Elsevier , ISBN: 978-0-12-385489-6, 2011
Buchoux V., Cappe, O., Moulines É., and Gorokhov A., “On theperformance of semi-blind subspace-based channel estimation”, IEEE
Transactions on Signal Processing, 48(6), 1750-1759, 2000.
Zeng Y., Lam W. H., and Ng. T. S., “Semiblind channel estimation andequalization for MIMO space-time coded OFDM”, IEEE Transactions
on Circuits and Systems I, vol. 53, no. 2, pp. 463-474, 2006.
Yu J. L., and Lin, Y. C. “Space–Time-Coded MIMO ZP-OFDM
Systems: Semiblind Channel Estimation and Equalization”, IEEE
Transactions on Circuits and Systems I, 56(7), pp. 1360-1372, 2009.
Wu W. R., Chiueh R. C., and Tseng, F. S., “Channel estimation for
OFDM systems with subspace pursuit algorithm”, IEEE International
Conference on Green Circuits and Systems (ICGCS),
pp. 269-272, 2010.
Fang S. H., Chen J. Y., Shieh M. D., and Lin, J. S. “Modified Subspace
Based Channel Estimation Algorithm for OFDM Systems”, IEEE 69th
Conference on Vehicular Technology, pp. 1-5, 2009.
J. Lang Yu, D.-Y. Hong, “Subspace channel estimation assisted block
matrix scheme for ZP-OFDM systems”, 6th International Conference
on Wireless and Mobile Communications (ICWMC), pp.16 – 20, 2010.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016912

[10] Huo W., Wang Z., and Li S., “A simple subspace-based semi-blindchannel estimator for precoded OFDM systems”, International
Conference on Wireless Communications, Networking and Mobile
Computing (WiCom), pp. 41-44, 2007.
[11] Biguesh Mehrzad and Alex B. Gershman, "Training-based MIMO
channel estimation: a study of estimator tradeoffs and optimal training
signals", IEEE Transactions on Signal Processing , vol. 54, no. 3, pp.
884-893, 2006.
[12] Gao Feifei and A. Nallanathan, “Subspace-based blind channel
estimation for SISO, MISO, and MIMO OFDM systems”, In IEEE
International Conference on. Communications (ICC'06), p.
3025-3030, 2006.
Gulomjon Sangirov received the M.S. degrees inInformation and Communication Engineeringfrom Harbin Engineering University in 2012. And
he is studying Ph.D. in College of Information andCommunication
EngineeringinHarbinEngineering University, China currently. His
research interests are Channel Estimation in
MIMO-OFDM and Quasi-Cyclic-LDPC coding.
Yongqing Fu received the MS degree in electricalengineering from National University of Defense
Technology, Changsha, China in 1985. He held
the posts of Lecturer (1988-1995), Associate
professor (1995-2000) and Professor (since 2000)
in the Electrical Engineering Department, Harbin
Engineering University. He was a visiting
Professor of Electrical and Computer Engineering
Department, University of Manitoba, Canada in2002 and also Electrical and Computer Engineering Department, Universityof California, San Diego, the USA in 2009. He is the author of four books,
more than 100 articles, and more than 10 patents. His research interests
include the weak signal detection, chaotic communication and signal
processing, cognitive radio system, image coding, marching and feature
extraction and circuit and electronic system. Prof. Fu is the Vice director of
the Northeast Electric Theory Association (since 2003), and he is a reviewer
for the Journal of China Universities of Posts and Telecommunications(since 2006).
Jamshid Sangirov received the M.S. degrees inInformation and Communication Engineeringfrom Yeungnam University in 2006. He got his
Ph.D. in Information and Communication
Engineering at Korean Advanced Institute of
Science and Technology (KAIST) at 2013. Heworked with the RFIC Design Team, at Teltron
Inc., Korea, from 2010 to 2011. From 2013 he iswith Samsung Electronics. His research interests are analog/RF/VLSI andhigh-speed Integrated Circuit design.
Fang Ye received her Ph.D. degree in
Communication and Information System from
Harbin Engineering University in 2006. From
November 2007 to November 2008, she has been
as a visiting scholar in the school of electronics
andcomputerscience,
University
Of
Southampton, U.K. Since December 2008, she is
an associate professor of the school of Information
and Communication Engineering in Harbin Engineering University, China.
Her research interests include LTE technologies, adaptive radio resource
allocation technology, and UWB signal processing. She is a member of IEEEand Association for Computing Machinery (ACM).
Ahmad Olmasov received the M.S. degrees in
Tashkent University of Information Technologies,
Tashkent, Uzbekistan in 2007. And he started
working with Samarkand branch of Tashkent
University of Information Technologies.
Currently, he is a lecturer in the department oftelecommunication engineering at Samarkand
branch of Tashkent University of Information
Technologies. His research interests are Mobile communication, channel
coding in high throughput wireless communication and MIMO-OFDM.

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

913

Terminal-based Energy-Efficient Resource
Allocation in OFDMA-Based Wireless Multicast
Systems
Jun Liu
Research and Application Innovation Center for Big Data Technology in Railway, Institute of Computing
Technologies, China Academy of Railway Science, Beijing, China
junliu04@163.com

Abstract²At present, the user experience provided by smart
mobile terminals is limited to the battery capacity. This paper
focuses on how to improve the energy efficiency of terminals in
OFDMA-based
wireless
multicastsystemswithfrequency-selective channels. We assume that multicast
terminals can switch to sleep mode during the transmission ofsome OFDM symbols according to their OFDMA frame-level
quality of service (QoS) requirements. Based on it, we combine
resource allocation with terminal sleeping mechanism, andpropose a new resource allocation problem model. The task is
to minimize the total time when terminals are in receive modethrough jointly optimizing the subcarrier allocation fordifferent multicast terminals and the power allocation between
different subcarriers, which is a NP-hard problem. To adapt to
the needs of real-time applications, we separate subcarrier andpower allocation, and propose a low-complexity suboptimalalgorithm for this problem. Performance evaluations are
conducted in homogenous and heterogeneous networks
respectively. Simulation results show that compared withtraditional multicast and unicast, our proposed method reduce
the total energy consumption of terminals significantly with thesame QoS requirements of terminals guaranteed. Additionally,
the advantage of our proposed method over traditional
multicast diminishes with the increase of the maximum
transmission power, and increase with the number of multicast
terminals and OFDM symbols in an OFDMA frame.
Keyword²Energy Consumption,
Allocation, OFDMA, Multicast

Terminal,

Resource

I. INTRODUCTIONW

ith the development of smart mobile devices andcommunication technology, the mobile traffic is
growing significantly in recent years, which stimulates theresearch of green communication. Since base stations occupy
the most of energy consumption, the majority of the related
research works were conducted from the perspective of basestations. Actually, the development of mobile applications²²²²²²²²²²²²²²²²²²²²²²²
Manuscript received March 5, 2016. This work is a follow-up of the invitedjournal to the accepted conference paper of the 17th International Conference on
Advanced Communication Technology.
Jun Liu is with Research and Application Innovation Center for Big Data
Technology in Railway, Institute of Computing Technologies, China Academyof Railway Science, Beijing, 100081 China (corresponding author phone:+86-158- 014-999-63; e-mail: junliu04@163.com).

makes user experience more and more limited to the battery
capacity. Therefore, it makes sense to improve the energy
efficiency of terminals.
Both multicasting and orthogonal frequency divisionmultiple access (OFDMA) are identified as efficienttechniques to address the challenge of limited system
resources. In OFDMA-based wireless multicast system,
resource allocation is an effective technique that can improve
the spectrum efficiency compared to traditional multicast [1],[2], [3]. In [4] and [5], it is proved that clustering themulticast terminals can increase the spectrum efficiency
further. It should be pointed out that in the clustering schemeno subcarrier can be shared by multiple clusters, which isreferred to as Scheme I. Reference [6] assumed that eachsubcarrier can be allocated to all the multicast terminals,
which is referred to as Scheme II. Scheme I means that aterminal that does not belong to the allocated terminals of a
subcarrier may have a better channel condition on thissubcarrier. If the allocated terminals can receive thetransmitted message on the subcarrier, the terminal with abetter channel condition will definitely receive the samemessage. Accordingly, Scheme II can utilize thetransmission resource more efficiently than Scheme I.
However, [6] did not take the QoS requirements of terminals
into account. And the above research works do not consider
the energy efficiency of multicast terminals. References [7]and [8] studied the energy efficiency of multicast terminals
through resource allocation based on OFDMA system.
Nevertheless, in [7] the channel conditions on all thesubcarriers are assumed the same, which ignores thefrequency-selective channels. And the energy consumption
model of terminals in [8] needs to be improved to adapt to the
OFDMA-based system more effectively.
In this paper, we aim at reducing the energy consumptionof terminals in OFDMA-based multicast system with the
QoS requirements of terminals guaranteed for thefrequency-selective channels. In our system, each subcarrier
can be allocated to all the multicast terminals. We combine
resource allocation with terminal sleep model to reduce theenergy consumption of terminals. The terminals are switchedto sleep mode when their QoS requirements are satisfied
during the transmission of each OFDMA frame. The
formulated optimization problem is solved using a
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016

low-complexity algorithm through optimizing the subcarrier
allocation and power allocation separately.
The rest of this paper is organized as follows. Section II
firstly presents our system model and then formulates theenergy efficiency optimization problem in OFDMA-based
multicast system. Section III proposed a low-complexity
algorithm for the optimization problem. Simulations are
conducted in Section IV followed by the conclusions in
Section V.

II. SYSTEM MODEL AND PROBLEM FORMULATION

Additionally, we refer to [7] and assume that the energy
assumption when terminals receive data depends on the time
when they are in receive mode. For the OFDMA frame model
as shown in figure 1, once the QoS requirement of a terminal
is satisfied, this terminal can switch to sleep mode during thetransmission of the remaining OFDM symbols. So theconsumed energy of terminals is in direct proportion to thenumber of OFDM symbols they receive. In this paper, energy
efficiency is defined as the inverse of the energy consumptionof terminals required to satisfy the QoS requirements ofterminals.
The minimal
allocable unit: tile

Frequency domain

A. System Model, M ` where M is the maximum number ofloaded bits. Let R min be the minimum rate required by eachterminal. As is in [12], it can be transformed into theminimal number of bits in an OFDMA frame required byeach terminal H min , referred to as ³QoS requirement´ in thispaper. In the 3GPP technical specification of [9],Discontinuous transmission (DRX) is supported to enablemobile terminal power saving in LTE system. It means thatin order to save power, terminals can almost close thereceiving module and turn to sleep mode when they do notneed to receive information. Moreover, DRX has been
introduced to multimedia broadcast/multicast service [13].

­
°
°
°
®
°
°
°
¯

ck ^0,1,

An OFDMA frame

A subcarrier^^

The resource allocation model in OFDMA-based multicast
system is illustrated in Figure 1. We consider single-cell
multicast systems and frequency-selective channels.
Furthermore, each subcarrier can be shared by all theterminals. In this paper, the channel condition of eachterminal remains constant in an OFDMA frame with block
fading. And we assume that the base station have perfectchannel state information (CSI) of different terminals on all
the subcarriers. In LTE, the CSI can be reported periodicallyto the base station through physical uplink control channel(PUCCH) from terminals. It is essential for the link
adaptation at the base station, including adjusting thetransmission rate with adaptive modulation and coding(AMC) [9]. In this paper, the CSI is used by resource
allocation algorithm to minimize the energy consumption ofterminals with the total transmission power constraint.
Dynamic resource allocation means that the allocatedsubcarriers of each terminal vary in different OFDMA
frames. So each terminal will receive different discontinuous
parts of the original message. Therefore, we assume that thesource content are encoded with erasure coding (e.g.,
fountain coding) or multiple description coding [10]beforehand so that terminals can recover the original content
once the minimum amount of data is received, regardless ofthe specific received sequence of data. An OFDMA frame
consists of symbols in the time domain and subcarriers in thefrequency domain. A symbol and subcarrier combination is
the minimal allocable unit, which is called a ³tile´ [7], [11],[12], and each tile can be coded and modulated individuallyaccording to the subcarrier and power allocation results. Let
ck be the number of loaded bits on subcarrier k with

914

An OFDM symbol

Time domain

Fig. 1. The resource allocation model in OFDMA-based multicast system

B. Problem Formulation
We consider an OFDMA-based multicast system with
K subcarriers and N terminals which request the same dataservice. There are S symbols in an OFDMA frame. Let hk , n
be the channel gain of terminal n on subcarrier k . To
ensure that terminal n can decode ck loaded bit on
subcarrier k , the required power p k should satisfy

hk ,n 2 . We consider M-ary quadrature amplitude

pk t f ck
modulation(M-QAM),

f ck

then

2
N0
ªQ 1 pe 4 º¼ 2cn  1 where pe is target bit error rate,3 ¬N 0 2 is the variance of additive white Gaussian noise, and

Q1 x
Q x

is
1

the
f

³ e
2S
x

inverset 2 2function

of

Q x

with

[6]. Let Uk , n be a subcarrier

dt

allocation indicator which is 1 if subcarrier k is allocated toterminal n , and 0 otherwise. Note that in our system, theenergy consumption of terminals is in direct proportion to thenumber of OFDMA symbols required to satisfy the QoS
requirements of terminals. Accordingly, our problem isformulated as followsªº« H min »»min ¦ « Kn 1«
U k , n ck »»
«« ¦
k 1
»
N

(1)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016s.t.

Uk , n  ^0,1`, n, k(2)

ck  {0,1,..., M }k

(3)

Hmin

K

¦ U c t S ,n(4)k ,n kk 1

pk t

f ck Uk , n
hk , n 2

, n, k(5)K¦ p d P , p t 0
k

max

(6)

k

k 1

where constraint (4) corresponds to the QoS requirements ofterminals. Pmax represents the maximum transmission
power and constraint (6) corresponds to the totaltransmission power limitation. So our optimization problem
is to minimize the total number of OFDMA symbols needed
to satisfy the QoS requirements of terminals throughoptimizing the subcarrier allocation ^ U k , n ` and the power
allocation^ pk ` /bit allocation ^ck ` . Because

p k iscontinuous and ck is discrete, it is much more convenient tosearch the optimal bit allocation than power allocation. In
traditional multicast, the total transmission power is equally
allocated to all the subcarriers (i.e., pk Pmax K , k ), andeach subcarrier is shared by all the terminals (i.e.,
Un,k =1, n, k ). In this way, the bit rate on each subcarrier islimited by the terminal with the worst channel gain. Thus
traditional multicast needs too many OFDM symbols tosatisfy the QoS requirements of terminals. In the next section
we will investigate how to improve the energy efficiency of
traditional multicast.

915

number of loaded bits allocated to terminal n in an OFDMA
frame. The energy consumption of each terminal can be
expressed as E n = ª« H min Q n º» , where ª« a º» means round a tothe nearest integer no less than a . Thus when the channel
conditions on all the subcarriers are determined, we expect
the longitudinal rate of each terminal to be as large as
possible to reduce the number of OFDMA symbols required
to satisfy the terminals¶ QoS requirements. Considering theconstraint of real-time applications on complexity, we
separate subcarrier and power allocation, and propose a
low-complexity suboptimal algorithm. In this algorithm, thepower allocation remains unchanged during subcarrier
allocation, and the subcarrier allocation remains unchanged
during power allocation. But bit allocation is changeable inboth subcarrier and power allocation.
A. Initial Allocation
Traditional multicast is applied to the initial allocation,i.e., pk Pmax K , k , and Un,k =1, n, k . Since thenumber of loaded bit on subcarrier k is determined by theworst channel condition of U k and the power on thissubcarrier, it can be calculated according to equation (5) as
follows:«§ 3 pk min hk , n 2 · »n^1,..., N `
«
»
2
1
«
¨
¸»
ck d « f
pk min hk , n » = log 2 1+
2 ¸»
«
¨
n
N

1,...,
1^`¬¼¨
N 0 Q Pe / 4 ¸ »
©
¹¼
¬«
(7)
where ¬« a ¼» means round a to the nearest integer no larger
than a . Note that ck d M , k . In this case, U k = ^1,..., N` .
B. Subcarrier Allocation

III. USER-BASED ENERGY-EFFICIENT RESOURCE
ALLOCATION ALGORITHM
The optimization problem of equation (1)-(6) is anonlinear integer programming problem. And it is NP-hard[6]. The optimal solution can be derived by exhaustive search.
When exhaustive search is used, the number of possible
subcarrier allocations is N k and each possible subcarrier
allocation corresponds

M 1

K

possible bit allocations.

The optimal combination of subcarrier and bit allocation is
the one that makes subjective function the smallest.
Therefore, the complexity of exhaustive search is
N k M 1K

, which makes this method unsuitable forreal-time applications. A low-complexity algorithm isnecessary for this dynamic resource allocation problem.
The set of the terminals that are allocated subcarrier kcan be expressed as U k = ^n : U n , k

1, n  ^1, 2,..., N `` . And

the set of the subcarriers that allocated to terminal n can be
expressed as K n = ^k : U n , k

1, k  ^1, 2,..., K `` . We definelongitudinal rate Q n as Q n¦ c , which means the totalkk K nFor simplicity, define the worst terminal of subcarrier kas the terminal with the worst channel condition on
subcarrier k . Set the maximum number of iteration as
Itermax_1 . In each iteration, the following process is conducted
for the subcarriers ^1,..., K` in order. Remove the worst
terminal from U k . On one hand, if terminal nk* is removed
from subcarrier k , according to equation (7) the number ofloaded bit on this subcarrier may increase. This will raise thelongitudinal rate of the terminals in U k and thus reduce theenergy consumption of these terminals. On the other hand,since terminal nk* is removed from subcarrier k , the QoS
requirement of terminal nk* may be no longer satisfied. It
requires us to allocate more tiles on other subcarriers for thisterminal. In the tile adding process the increased energy
consumption (i.e., number of the OFDMA symbols) of
terminal nk* should be kept as small as possible. Let Etotal be
the current energy consumption of terminals. Let Etotal k be
the energy consumption of terminals after removing terminal
nk* from subcarrier k . Next, update ck , the longitudinal

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

rates of each terminals Q n , and Etotal k . It is a valid
removal if the required number of symbols is less than S
after the tile adding process and Etotal k  Etotal . In each
iteration, we search subcarrier k * that minimizes the totalenergy consumption of terminals. The iteration does not stop
until the worst terminal on all the subcarriers cannot be
removed or the number of iteration reaches Itermax_1 .
In each iteration, the number of considered allocations is
K . Since a terminal may be removed from a subcarrier after
each iteration, the maximum number of iterations is KN .
Therefore, the maximum number of possible allocations in
subcarrier allocation is NK 2 . Here the maximum number of
iteration is Itermax_1 which is smaller than KN , so the actual

916

proposed algorithm is summarized in Table I.
TABLE ITHE PSEUDOCODE OF THE PROPOSED ALGORITHM
1) Initializaiton:
0. Let pk Pmax K , k and Un,k =1, n, k1.

Calculate ck according to (7)2.

Kn =^k : Un,k 1, k ^1,2,..., K``3.

Qn¦ c and Eª« H min Q n º»

n

k

kKn

N

4.

¦E

Etotalnn 12) Subcarrier allocation:
5. Let iter_i=1
6. For all k do
7.

nk* = min hk ,n2

8.

U k = Uk  nk* , i.e., Uk ,n9.

Update En* , ck , and Q n for all n

Define the maximum number of iteration as Itermax_ 2 .

10.

If En* > S Etotal k =+Ğ, go to 6; End if

During each iteration, we adjust the number of loaded bit on
subcarrier k from ck to ck +1 and update the required power

11.

on this subcarrier as well as the total transmission

12.

End for

K

13.

If

k 1

14.
15.

Stop
Else ifnumber of possible allocations is Itermax_1 K .
C. Power Allocation

^ `

*

0

k

power Ptotal = ¦ pk . This may violate the transmission power
constraint of Ptotal d Pmax . So we should choose one of theother subcarrier ^i : i  ^1, 2,..., K ` , i z k ` and reduce its
power. Specifically, calculate the excess of the transmission
power 'P = Ptotal  Pmax and reduce the power on subcarrier ifrom pi to pi  'P . Let Etotal k,i be the total energy
consumption of terminals after adjusting ck and pi . Then
update ci and Etotal k,i . It is a valid adjustment only if

Etotal k,i  Etotal . For subcarrier k , we search the subcarrier
i* that achieves the largest decrement of the total energy
consumption with the QoS requirements of terminals
guaranteed,
andreduceits
power.
ThuskN

Etotal k = ¦ En
n 1

min

k^1,2,..., K `( total k> Etotal

16.

Find ¬ªk * , nk* ¼º =arg min

17.

Etotal = Etotal k * and iter_i= iter_i+1

18.

If iter_i= Itermax_1 stop; else, go to 6.

k^1,2,..., K `( total k

19. End if
3) Power allocation
20. Let iter_i=1
21. For all k do
22.

ck = ck +1

23.

update pk and Ptotal

24.

If Ptotal t Pmax

25.

Calculate 'P = Ptotal  Pmax

26.

For all ^i : i ^1,2,..., K`, i z k`27.

pi = pi  'P , and update ci and Q n for all n

28.

For all n do

the subcarrier pair ^k *ˈj * ` that minimizes the total energy

29.

If En > S Etotal k,i =+Ğ,go to 26; End if

consumption and adjust their power allocation according to
the foregoing method. The above process does not stop until
the total energy consumption no longer decreases or thenumber of iteration reaches Itermax_ 2 .

30.
31.

End forCalculate Etotal k,i

32.
33.

End forEnd if34.

Find i* =arg

In power allocation, the maximum number of possible

35.

Etotal k = Etotal k,i*i* =argmini z k , i^1,2,..., K `allocations is

Etotal k,i

M 1

K

. In each iteration, we searchmini^1,2,..., K `,i z k

Etotal k,i. Here the maximum number of36.

End foriteration is Itermax_ 2 , and the actual number of possible

37.

If

allocations in power allocation is Itermax_ 2 K ( K 1) .

38.
39.

Stop
Else if

Accordingly, the maximum number of possible allocations in

40.

Find ª¬k * , j* º¼ =arg min

41.

Etotal = Etotal k * and iter_i= iter_i+1

2

the proposed algorithm is NK  M  1

K, which is usuallymuch smaller than that of exhaustive search , N

k

M 1

K

.

42.

And the actual number of possible allocations is
Itermax_1 K + Itermax_ 2 K ( K 1) . The pseudocode of the

43.

min

k^1,2,..., K `( total k> Etotalk^1,2,..., K `Etotal k

If iter_i= Itermax_ 2 stop; else, go to 21.
End ifCopyright ⓒ 2016 GiRI (Global IT Research Institute)

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

PERFORMANCE EVALUATIONIn the simulations, we use ITU Pedestrian-B channel
model [14] to evaluate our proposed method. The path loss
model is L(dB) 148  40log10 d , where d is in units ofmeters and stands for the distance to the base station.
Rayleigh fading channel is considered. Some systemparameters are listed in Table II. The QoS requirements of all
the multicast terminals are set to 2500bits/OFDMA frame.
TABLE IISYSTEM PARAMETERS
Parameter type

Value

Channel bandwidth
Carrier frequency
Number of subcarriers
Subcarrier bandwidth
Doppler shift

1MHz
2000MHz
32
15kHz
18Hz

Traditional multicast and unicast method are considered
for comparison. Furthermore, to have a more thoroughevaluation of our proposed method, we considered the
combination of traditional multicast and the power allocationof our proposed method, which is denoted as ³Traditional
multicast+PAO´. In unicast each subcarrier can only beallocated to one terminal. Specifically, each subcarrier isonly allocated to the terminals with the best channel gain on
the subcarrier. To ensure fair comparison, the power
allocation of the proposed algorithm is applied in unicast
method. In this paper it is called an ³outage´ event that the
QoS requirement of a terminal is not satisfied in an OFDMA
frame. The QoS requirements of terminals are satisfied only
if the average outage probability of all the terminals is notlarger than 5% [15]. In the proposed method, the maximum
number of iteration is set to 20.
Beforehand the available number of OFDM symbols in an
OFDMA frame is denoted by S . As is analyzed in Section
II.A, the energy consumption of terminals can be assumed to
be proportion to the number of OFDM symbols they received.
So S is the maximal one among all the possible number of
OFDM symbols a terminal receives in an OFDMA frame,
which corresponds to the maximum energy consumption.
For the convenience of comparison, in the following
simulations we normalize the simulated energy consumption
with respect to the maximum one.
A. Homogeneous Networks
Firstly, we consider homogeneous network where all themulticast terminals experience independent and identically
distributed fading with the distance to the base station
d =200m. Here we discuss the effects of the maximum
transmission power, the number of terminals and the numberof OFDM symbols in an OFDMA frame on the total energy
consumption of terminals. In the first scenario, the number ofterminals N is set to 10 and the available number of OFDM
symbols in an OFDMA frame S is set to 60 [7]. The
corresponding simulation results are plotted in Fig. 2 where
both the proposed multicast and traditional multicast satisfy
the QoS requirements of terminals. It can be seen that the

proposed multicast achieves much lower energy
consumption of terminals. And the average energy
consumption decreases as the maximum transmission powerincreases. Note that in the considered simulation
environment, unicast cannot satisfy the QoS requirements ofterminals although it consumes the maximum energy.
Moreover, it shows that the energy efficiency gain of the
proposed method over traditional multicast diminishes with
the increase of the maximum transmission power. It can be
explained as follows. It is known that the increase oftransmission power can improve the average channel
condition of terminals. However, the maximum number ofloaded bits of each tile M is fixed beforehand, which limits
the increase of the terminal rate and thus slowdown the
reduction of energy consumption of terminals. Because theproposed method utilizes the relatively better channel gains
in a more effective way compared to traditional multicast, it
will suffer from the above-mentioned limitation earlier than
traditional multicast.
Average normalized energy consumption (%)

IV.

917

100
Traditional multicast
Traditional multicast + PAO
Proposed multicast
Unicast

90
80
70
60
50
40
30

1

1.2

1.4
1.6
1.8
Maximum transmission power (w)2

Fig. 2. Energy consumption comparison with different maximum transmission
power in homogeneous networks

Specifically, the quantitative relationship between the
maximum transmission power and the terminal energy
consumption decrement of the proposed method overtraditional multicast is listed in Table III. It can be seen thatwith the maximum transmission power increases graduallyin steps of 0.2w starting from 1w, the energy consumption
decreasing ratio of the proposed method over the traditional
method ranges from 13.829% to 21.898%. And thedecreasing ratio diminishes with the increase of thetransmission power, which is in accord with Fig. 2.
TABLE IIITHE TERMINAL ENERGY CONSUMPTION DECREMENT OF THE PROPOSEDMETHOD OVER TRADITIONAL MULTICAST IN HOMOGENEOUS NETWORKS

Maximum transmission
power (w)
1
1.2
1.4
1.6
1.8
2

The terminal energy
consumption decrement
21.898%20.184%18.377%16.640%15.109%13.829%Furthermore, we analyze the marginal terminal energy

Copyright ⓒ 2016 GiRI (Global IT Research Institute)TABLE IV
THE MARGINAL TERMINAL ENERGY CONSUMPTION INCREMENT OF THE
PROPOSED METHOD IN HOMOGENEOUS NETWORKS

The marginal increment of the
maximum transmission power
0
20%
20%
20%
20%
20%

The marginal terminal energy
consumption decrement06.2884%4.4599%3.3913%2.6993%2.4110%In the second scenario, the maximum transmission power
is set to 1.4W with S =60. We evaluate the effects of thenumber of terminals. The corresponding simulation results
are plotted in Fig. 3 where the QoS requirements of terminals
are satisfied in both traditional and proposed multicast. In
unicast the QoS requirements of terminals are not satisfied
when the number of terminals N t 5 . The simulation results
show that the advantage of the proposed multicast overtraditional multicast increases with the number of terminals.
The reason is as follows. For each subcarrier, the more thenumber of terminals, the higher the probability that thechannel condition of a terminal on this subcarrier is bad. As
is mentioned before, the transmission rate of traditional
multicast is limited to the worst terminal. Therefore, theincrease of the number of terminals will prolong the timeneeded to satisfy the QoS requirements of all the terminals.
In comparison, the proposed multicast utilizes both multiuser
diversity and frequency-selective fading to allocate theterminals with better channel gains for each subcarrier,which alleviates the constraint of the worst terminal.
In the third scenario, the maximum transmission power isalso set to 1.4W and the number of terminals N is set to 10.
Here we evaluate the effects of S on the total energy
consumption of terminals. The corresponding simulation
results are plotted in Fig. 4, where the QoS requirements ofterminals are not satisfied only in unicast. The simulation
results show that the advantage of the proposed multicast
over traditional multicast increases with the number of
OFDM symbols S in an OFDMA frame. The reason is thatthe increase of S means the expansion of optimization zone,
which results in better resource allocation.

918

100
90

Traditional multicast
Traditional multicast + PAO
Proposed multicast
Unicast

80
70
60
50
40
30
20

1

3

5

7
9
Number of terminals

11

13

15

Fig. 3. Energy consumption comparison with different maximum transmission
power in homogeneous networks
Average normalized energy consumption (%)

consumption decrement with successive and fixed increase of
the transmission power for the proposed method. The
corresponding results are listed in Table IV. It can be seenthat the terminal energy consumption decreases
2.4110%~6.2884% every time the maximum transmission
power increases 20% (i. e., increases 20% compared to theinitial transmission power of 1w each time) in the considered
range. Moreover, with the transmission power increases
gradually, the marginal terminal energy consumption
decrement of the proposed method phases down. In otherwords, from the perspective of terminal energy consumption,
the marginal effect brought by the maximum transmission
power gets smaller with the increase of the maximum
transmission power.

Average normalized energy consumption (%)

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

100
90

Traditional multicast
Traditional multicast + PAO
Proposed multicast
Unicast

80
70
60
50
40
30
30

40

50
60
Number of symbols

70

80

Fig. 4. Energy consumption comparison with different number of OFDM
symbols in an OFDMA frame in homogeneous networks

B. Heterogeneous Networks
Then we turn to heterogeneous networks where themulticastterminals
experience
independentbut
non-identically distributed fading with the distance to thebase station uniformly distributed between 0 and 500m. We
also investigate the effects of the maximum transmission
power and number of terminals on the energy consumption ofterminals. In the first scenario, we consider a multicast
system with 10 terminals and S is set to 60. The
corresponding simulation results are plotted in Fig. 5. Bothtraditional and proposed multicast satisfy the QoS
requirements of terminals, which unicast fails to satisfy. It
can be observed that our proposed method is more energy
efficient than other considered methods. And the energy
efficiency is improved with the increase of the maximum
transmission power. Similar to homogeneous networks, here
we also observe from Fig. 5 that the increment of the
maximum transmission power is not equivalent to thedecrement of the energy consumption of terminals.
Specifically, the quantitative relationship between the
maximum transmission power and the terminal energy
consumption decrement of the proposed method overtraditional multicast is listed in Table V. It can be seen thatwith the maximum transmission power increases gradually
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

100
Traditional multicast
Traditional multicast + PAO
Proposed multicast
Unicast

90
80
70
60
50
40
30
12

14

16
18
20
Maximum transmission power (w)22

Fig. 5. Energy consumption comparison with different maximum transmission
power in heterogeneous networks

TABLE VTHE TERMINAL ENERGY CONSUMPTION DECREMENT OF THE PROPOSEDMETHOD OVER TRADITIONAL MULTICAST IN HETEROGENEOUS NETWORKS

Maximum transmission
power (w)
12
14
16
18
20
22

The terminal energy
consumption decrement
19.543%18.286%17.087%16.000%14.861%13.795%

terminals are investigated. The corresponding simulation
results are plotted in Fig. 6. Here unicast also fails to satisfy
the QoS requirements of terminals when N t 5 . And we canderive the same conclusions as homogeneous networks about
the effect of the number of terminals on the performance ofthe proposed method. In the third scenario, the maximum
transmission power is also set to 16W and the number ofterminals N is set to 10. The effects of S on the energy
consumption of terminals are plotted in Fig. 7, where unicast
fails to satisfy the QoS requirements of terminals during theconsidered numbers of symbols. Here it can be also observedthat the advantage of the proposed multicast over traditional
multicast increases with S , which is in accordance with thatin homogeneous networks.
Note that compared to the proposed multicast, there is no
optimization on the subcarrier allocation in ³traditional
multicast+PAO´. The simulation results of Fig. 2~7 revealthat subcarrier allocation provides much more contribution
than power allocation in reducing the energy consumption ofterminals.

Average normalized energy consumption (%)

Average normalized energy consumption (%)

in steps of 2w starting from 12w, the energy consumption
decreasing ratio of the proposed method over the traditional
method ranges from 13.795% to 19.543%. And thedecreasing ratio diminishes with the increase of the
maximum transmission power, which is in accord with Fig.
5.

100
90

70
60
50
40

The marginal increment of the
maximum transmission power0
16.667%
16.667%
16.667%
16.667%
16.667%The marginal terminal energy
consumption decrement05.1681%
4.0960%3.3645%2.6682%2.2209%

1

3

5

7
9
Number of terminals

11

13

15

Fig. 6. Energy consumption comparison with different number of terminals inheterogeneous networks
Average normalized energy consumption (%)

TABLE VI
THE MARGINAL TERMINAL ENERGY CONSUMPTION INCREMENT OF THE
PROPOSED METHOD IN HETEROGENEOUS NETWORKS

Traditional multicast
Traditional multicast + PAO
Proposed multicast
Unicast80

30

In this scenario, we also calculate the marginal terminal
energy consumption decrement with successive and fixed
increase of the transmission power for the proposed method,which is listed in Table VI. It can be seen that the terminal
energy consumption decreases 2.2209%~5.1681% every
time the maximum transmission power increases 16.667% in
the considered range. Here it is also obvious that from theperspective of terminal energy consumption, the marginal
effect brought by the maximum transmission power getssmaller with the increase of the maximum transmission
power.

919

100
90

Traditional multicast
Traditional multicast + PAO
Proposed multicast
Unicast

80
70
60
50
40
30
30

40

50
60
Number of symbols

70

80

Fig. 7. Energy consumption comparison with different number of OFDM
symbols in an OFDMA frame in heterogeneous networks

In the second scenario, the maximum transmission power
is set to 16W and S is set to 60. The effects of the number of
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

V. CONCLUSIONSIn this paper, we investigate the energy efficiency of
OFDMA-based multicast system from the perspective of
terminals in frequency-selective channels. The energy
efficiency of terminals is optimized through combining
resource allocation with terminal sleep model. A
low-complexity algorithm is proposed to solve theformulated optimization problem. The simulation results
show that in both homogeneous and heterogeneous networks,
the combination of resource allocation and terminal sleep
model can reduce the energy consumption of terminals
compared to traditional multicast and unicast significantly.
Specifically, the terminal energy consumption decreases
2.4110%~6.2884% / 2.2209%~5.1681% every time themaximum transmission power increases 20% / 16.667%
compared to the initial value of 1w / 12w in the consideredhomogeneous / heterogeneous networks. And in the two
scenarios, from the perspective of terminal energy
consumption, the marginal effect brought by the maximum
transmission power gets smaller with the increase of the
maximum transmission power. Moreover, the advantage ofthe proposed multicast over traditional multicast increases
with the number of multicast terminals as well as the numberof OFDM symbols in an OFDMA frame. Additionally, it
indicates that subcarrier allocation contributes much more to
the reduction of the energy consumption than power
allocation.

920[12] 44XDQG8&.R]DW³2QWKH2SSRUWXQLVWLF0XOWLFDVWLQJLQ2)'0
Based Cellular Networks," in Proc. IEEE Int. Conf. Commun. (ICC¶08),
Beijing, China, pp.3708-3714, May 2008.
[13] 3GPP TS 25.346 V11.0.0,´,QWURGXFWLRQ RI the Multimedia
Broadcast/Multicast Service (MBMS) in the Radio Access Network
5$1 6WDJH´6HS
[14] ITU-R Recommendation M.1225, "Guidelines for evaluation of radiotransmission technologies for IMT-2000," 1997[15] J. Liu, Y. Zhang, and J. Song, ³(QHUJ\ Saving Multicast Mechanism for
Scalable Video Service Using Opportunistic Scheduling,´ IEEE Trans.
Broadcasting, vol. 60, no. 3, pp.464-473, Sep. 2014.

Jun Liu received the B. E. and M. S. degrees incommunication engineering from Harbin Institute ofTechnology, China, in 2008 and 2010, respectively, and
the Ph.D. degree in information and communicationengineering from Tsinghua University, China, in 2014.
From 2014 to 2015, he worked as an engineer at State
Grid Information & Telecommunication Branch, Beijng,
China. Now he is working as a Postdoctoral researcher at
China Academy of Railway Science, Beijing, China. His
current research interests mainly include wireless multimedia transmission,energy-efficient scheduling and resource allocation, and big data. He has
published more than 10 peer-reviewed journal and conference papers.

REFERENCES
R. O. Afolabi, A. Dadlani, K. Kim, ³Multicast Scheduling and Resource
Allocation Algorithms for OFDMA-Based Systems: A Survey,´ IEEE
Communications Surveys & Tutorials, vol.15, no.1, pp.240-254, 2013.
[2] . %DNDQRJOX : 0LQJTXDQ / +DQJ DQG 0 6DXUDEK ³$GDSWLYH
resource allocation in multiFDVW 2)'0$ V\VWHPV´ in Proc. IEEE
:LUHOHVV&RPPXQLFDWLRQVDQG1HWZRUNLQJ&RQIHUHQFH :&1&¶ , pp.
1±6, Apr.2010.
[3] J. Liu, W. Chen, Z. Cao, and K. B. Letaief, ³'\QDPLF 3RZHU DQGSub-Carrier Allocation for OFDMA-Based Wireless Multicast Systems,´in Proc. IEEE International Conference on Communications (ICC¶08),
pp. 2607-2611, May 2008.
[4] X. Zhao and S. Jha, ³)OH[LEOH UHVRXUFH DOORFDWLRQ IRU PXOWLFDVW LQ
OFDMA based wireless networks," in Proc. IEEE Conference on Local
Computer Networks (LCN¶12), pp. 445-452, Oct. 2012.
[5] C. Tan, T. Chuah, S. Tan, et al., "Efficient clustering scheme for
OFDMA-based multicast wireless systems using grouping genetic
algorithm,´ . Electronics letters, vol. 48, no. 3, pp. 184-186, 2012.
[6] & 6XK DQG - 0R ³5HVRXUFH DOORFDWLRQ IRU PXOticast services in
PXOWLFDUULHU ZLUHOHVV FRPPXQLFDWLRQV´ IEEE Trans. Wirel. Commun.,vol.7, no. 1, pp. 27-31, Jan. 2008.
[7] <<X3+VLXDQG$3DQJ³(QHUJ\-Efficient Video Multicast in 4G:LUHOHVV 6\VWHPV´ IEEE Trans. Mobile Computing, vol.11, pp.
1508-1522, Oct. 2012.
[8] A. Lina and D. =DKHU ³(QHUJ\-aware resource allocation in OFDMAZLUHOHVV PXOWLFDVWLQJ QHWZRUNV´ in Proc. IEEE International
Conference on Telecommunications (,&7¶), pp. 1±5, April 2012.
[9] 3GPP TS 36.300 v.11.5.0, ³(YROYHG8QLYHUVDO7HUrestrial Radio Access(E-UTRA) and Evolved Universal Terrestrial Radio Access Network(E-UTRAN); Overall description; Stage 2,´$SULO
[10] <0D./HWDLHI=:DQJ50XUFKDQG=:X³0XOWLSOHGHVFULSWLRQ
coding-based optimal resource allocation for 2)'0$PXOWLFDVWVHUYLFH´
LQ3URF,(((*OREDO&RPPXQLFDWLRQV&RQIHUHQFH */2%(&20¶ ,pp. 1±5, Dec. 2010.
[11] S. Deb, S. Jaiswal, and K. Nagaraj, ³5HDO-Time Video Multicast in
WiMAX Networks,´ in Proc. IEEE International Conference on
Computer Communications ,1)2&20¶08), pp. 2252-2260, April
2008.
[1]

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

921

Innovation, Convergence and the
Disenfranchised: Investigating the Inclusivenessof Convergence in Malaysia
Kamarulzaman Ab. Aziz*,*Faculty of Management, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia
kamarulzaman.aziz@mmu.edu.my
Abstract—This study proposes to explore the level of inclusive
innovation adoption in converged telecommunications asperceived by industry players in Malaysia as well as theirattitudes towards the idea using Ajzen’s theory of planned
behavior (TPB). The paper shares the findings of the pilot study
conducted in a focus group of Malaysian telecommunicationsindustry players. Discussions on the robustness of the tool
developed, some descriptive statistics, preliminary findings andrecommendations for the communications sector are given as
well as implications for next stages of the work. In general, thefindings indicated that the Malaysian communications industry
players moderately agreed on the inclusiveness of theconvergence goods currently available in the markets. However,the findings also indicated more can be done to enhance the levelof inclusiveness. Furthermore, there appear to be certain
differences in attitude towards inclusive innovation according togender and generation.
Keyword—Convergence, Disenfranchised, ICACT2016,
Inclusive, Innovation

I. INTRODUCTION

R

ecent years have seen increasing pressure for better ways
for tackling poverty problems around the globe beyond
aids and charity [1, 2, 3]. The discussions also havehighlighted the need for the roles of the torch bearers to go
beyond government agencies and NGOs [1 – 7]. Studies by[7] and [8] underlined the significance as 4 billion of the
global population was those at the base of the economic
pyramid (BOP) and together they represent a potential marketof tremendous purchasing power; around $5 trillion.
Furthermore, the UN System Task Team formed in
September 2011 to support the UN system-wide preparations
for the post-2015 UN development agenda, proposed
bridging the technological divide by promoting inclusive
innovation [9]. These imperatives had underlined theimportance and relevance of inclusive innovations towards
inclusive growth and the achievement of inclusive societies.
In [10] (p.663) inclusive innovation defined as “the———————————————————————
Manuscript received March 15, 2016. This work was supported in part by
the Malaysian Ministry of Higher Education under the Fundamental
Research Grant Scheme 2014.
Kamarulzaman Ab. Aziz is with the Faculty of Management, Multimedia
University, Cyberjaya 63100, Selangor, MALAYSIA (phone:603-8312-5686; fax: 603-8312-5590; e-mail: kamarulzaman.aziz@mmu.edu.my).

development and implementation of new ideas which aspireto create opportunities that enhance social and economic
wellbeing for disenfranchised members of society.”
According to [11], disenfranchisement or marginalization canbe due to economy, state and family as well as along
dimensions such as gender, race, disability, knowledge andpoverty. Meanwhile, the Global Research Alliance defined
inclusive innovation as “any innovation that leads toaffordable access of quality goods and services creating
livelihood opportunities for the excluded population,
primarily at the base of the pyramid, and on a long term
sustainable basis with a significant outreach” [12].
Studies of inclusive innovation have previously exploredtypes of organizations and their success strategies [1, 13 –15]; roles of individuals in organizations [16]; roles ofregulation, policy and infrastructures [17]. There seems to belimited studies in terms of inclusive innovation adoption
among academic researchers.
In line with the seminal work by [7]; recognizing the sheer
size of the market at the bottom of the pyramid, there had been
numerous examples of inclusive innovation initiatives by theindustries, including Nestle [18], Nokia and ABB [19],Unilever and Tata Motors [10]. In terms of the geography of
the inclusive movement the coverage is largely from theAfrican continent [10, 20], South America [21], India [22 –24], Europe [22, 25], China and North America [22]. A gap
clearly exists in the body of knowledge relating to inclusive
innovation movement in the Malaysian context.
Wu highlight the importance for inclusive growth towards
ensuring ASEAN countries economic development [26], andthe Malaysian government specifically recognized theimportance of inclusive growth with both innovation andinclusive development identified among the “10 Big Ideas”
listed in the 10th Malaysia Plan. In the 10th Malaysia Plan,
the government spelled out the move towards inclusive
socio-economic development by elevating the livelihoods ofthe bottom 40% households, strengthening bumiputera
entrepreneurs, improving infrastructure, etc. According to theWorld Bank’s country overview report on Malaysia [27],"pockets of poverty exist and income inequality remains highrelative to the developed countries Malaysia aspires toemulate". Furthermore, the Malaysian government allocated
MYR 5.9 billion in 2016 from MYR 4.6 billion in the 2014
budget for the financial assistance scheme (BR1M) targetingthe low income groups [28, 29]. Perhaps the scheme would be

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

more effective if there are inclusive innovation goods andservices available in the markets for the recipients topurchase.
Another major trend observed currently, is in thecommunication
sector,where
computing,
ICTs,
communication networks, and media content are becoming
increasingly highly interlinked. This trend has led to all
aspects of life increasingly being conducted over interactive
digital media environment and multitude of networked
devices.
[30] (p.18-19) highlighted; “Convergence requires media
companies to rethink old assumptions about what it means to
consume media, assumptions that shape both programmingand marketing decisions. If old consumers were assumed tobe passive, the new consumers are active. If old consumers
were predictable and stayed where you told them to stay, thennew consumers are migratory, showing a declining loyalty to
networks or media. If old consumers were isolated
individuals, the new consumers are more socially connected.
If the work of media consumers was once silent and invisible,
the new consumers are now noisy and public.”
According to [31], convergence can be view from four
dimensions;Fig. 1. Four Dimensions of Convergence.

Caution is needed in terms of how the trend is framed digital lifestyle, modern family, the new consumer, etc. - asthere is a real risk of the convergence evolution being
non-inclusive. If convergence ends up a privilege enjoyed by
the sophisticated, urban and IT haves, the effects oftechnological/digital divide will be more severe,
disenfranchising more among the society and placing anation’s development as a whole at risk.
Thus, this study propose to explore the level of inclusive
innovation adoption in converged telecommunications asperceived by industry players in Malaysia as well as theirattitudes towards the idea using Ajzen’s theory of planned
behavior (TPB). TPB was developed by Ajzen in 1988 andlater refined in subsequent works [32 – 34]. The theory ofplanned behavior is a theory which predicts deliberate
behavior, because behavior can be deliberative and planned.
The theory proposes a model which can measure how humanactions are guided. It predicts the occurrence of a particularbehavior, provided that behavior is intentional. The model
outlines three variables which the theory suggests will predict
the intention to perform behavior. The variables are attitudes(Att) - the respondents’ attitudes towards inclusive

922

innovation, subjective norms (SN) - the respondents own
estimate of the social pressure to adopting inclusive
innovation, specifically; beliefs about how other people, who
may be in some way important to them, would like them tobehave, and perceived behavioral controls (PBC) - is theextent to which the respondents feel able to enact the adopting
inclusive innovation behavior. The intentions (Int) are theprecursors of behavior, in other words, it is the cognitive
representation of a respondents' readiness to adopting
inclusive innovation, and it is considered to be the immediate
antecedent of behavior.

II. RESEARCH DESIGNThis study comprised of several stages. First a group of
executives from major telecommunication companies andagencies were solicited to take part in the study. Next, thegroup was given an exposure to the various aspects of
convergence via a series of masterclasses. They were thenintroduced to the concept of inclusive innovation. Next, eachparticipant was asked to consider the various convergenceproducts/services being offered in the markets and evaluate its
inclusiveness. The measure for this is the inclusive innovationindex (III), developed from operationalizing the definition ofinclusive innovation by the Global Research Alliance.
Inclusive innovations are defined along the lines of fivedimensions [12]:• Affordable Access (AA) – “Such inclusive innovation
will have to be aimed at `extreme reduction’ in both the costsof production as well as the distribution.” Key elements forthis dimensions are; l) significant reduction of production
costs to enable affordable price, and ll) significant reductionof distribution costs to enable affordable price.
• Sustainable Business (SB) – “This means that in the longterm, the ‘affordable access’ must not depend on thegovernment subsidies or generous government procurement
support systems but should work by retaining the market
principles with which the private sector works comfortably.”Thus the key elements are; l) not dependent on government
subsidies, ll) not dependent on significant government
procurement, lll) not dependent on charity and CSR, and lV) asustainable business model.
• High Quality (HQ) – “It is because we have to recognize
the basic rights of the people at the base of the pyramid, whoshould be enjoying the more or less the same level of qualityof basic services as people at the top of the pyramid.” Theelements are; l) meeting quality standards, ll) do not sacrificequality to bring down the costs, and lll) comparable quality
level with those of similar products available in the market• Excluded Population (EP) – “The excluded populationor the disenfranchised or commonly marginalized groups
which could include the poor, the disabled, the migrants, thewomen, the elderly, certain ethnic group, and so on.” Theelements; l) designed for the poor, the disabled, the migrants,
the women, the elderly, certain ethnic group, and so on, ll)priced with the poor in mind, and lll) distribution designed toensure accessibility for the poor, the disabled, the migrants,
the women, the elderly, certain ethnic group, and so on.
• Massive Outreach (MO) – “If the `true inclusion’ has to

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

happen then the benefits of inclusive innovation should reacha large scale, i.e. a significant portion of population, and notjust a small section of the population (in many cases, the total
target population may only be a few hundreds of thousands ora few million- and not necessarily hundreds of million).” The
key elements; l) large market size, ll) large market share, andlll) reached sizeable percentage of the target market.
From the five dimensions, based on the definitions and the
key elements identified from the definitions, 19 survey items
were developed for the III. Following table provide the list ofitems.
TABLE IITEMS FOR INCLUSIVE INNOVATION INDEX
Dimension

AA

SB

HQ

EP

MO

Item
 The production costs are significantly low
compared to established industrial standard The distribution costs are significantly low
compared to established industrial standard The products/services are priced affordably low
compared to established industrial standard The businesses offering the products/services are
not dependent on government subsidies
 The businesses offering the products/services are
not dependent on significant government
procurement
 The businesses offering the products/services are
not dependent on a single major client The businesses offering the products/services are
not dependent on social funding (charity or
corporate donations) There is a long term demand for the
products/services
 Eventhough the products/services are designed
for the commonly marginalized groups it isappealing to the mass market as well
 The businesses offering the products/services
ensures the products/services meet all relevant
quality standards
 The businesses offering the products/services
don’t sacrifice quality to bring down the costs
 The businesses offering the products/services
ensure any cost reduction does not compromise
the quality
 The products/services’ quality is comparable withthose of similar but higher priced products
available in the market
 The products/services are designed for thecommonly marginalized groups such as the poor /bottom 40% / disabled / migrants / women /elderly / minority groups / etc
 The products/services are priced affordably; withthe poor / bottom 40% in mind The distribution channels are designed/selected toensure the products/services are accessible by thecommonly marginalized groups such as the poor /bottom 40% / disabled / migrants / women /elderly / minority groups / etc
 There is a large market potential for the
products/services
 The products/services already have a large shareof the target market
 The products/services have reached more than
50% of the target market

Their intention towards inclusive innovation is then gauged
via a survey develop based on Ajzen’s TPB. The findings arepresented in the following sections.

923

III. FINDINGS
A total of 30 executives took part in the study. Amongthem, 17 (56.7%) were males while the remaining 13 (43.3%)
were females. The age ranged from 20-24 to 45-49 years old.
Out of the 30, 12 (40%) have Master degrees, and 18 (60%)
have Bachelor degrees. The participants had a mean of 9.43
years work experience with their current organization,
ranging from first year to the 22nd year. Furthermore, 29(96.7%) of them claimed that their organization do their own
R&D and product development.
Reliability analysis was conducted to determine the internalreliability of the items used to measure the constructs tested inthis study. According to [35], Cronbach’s Alpha is areliability coefficient that indicates the extent to which theitems are positively correlated to one another. Cronbach’s
Alpha greater than 0.70 is deemed as good [36]. All of theconstructs were considered as reliable and good as the
Cronbach’s Alpha were above 0.70 (see Table II).
TABLE IIRELIABILITY ANALYSIS
Constructs

N

Items
Mean

Std. Dev.

Cronbach’s
Alpha

No. of Items

Att
SN
PBC
Int
III

30
30
30
30
30

4.272
5.258
4.775
5.725
5.174

12.70754
4.55982
4.490224.95044
10.87278

0.982
0.722
0.729
0.926
0.802

6
4
4
4
19

A total of 37 items were used to measure the main
constructs of the study, namely III (19 items), Att (6 items),
SN (4 items), PBC (4 items) and Int (4 items). The items were
measured by itemized rating scale with seven scale categories.
Mean analysis was conducted to determine the average meanof the constructs.
Generally, the respondents agreed with all the items
measuring the constructs with overall Int achieved the highest
level of agreement with an average of 5.7250 and median of
6.000; while overall Att scored the lowest with an average of
4.2722 and median of 4.7500. Meanwhile, overall PBC
achieved a mean of 4.7750 and median at 4.500. Overall SN
achieved the second highest mean of 5.2583 and median at
5.1250. The respondents rated a moderate level of agreement
on inclusiveness of convergence goods currently available inthe markets with overall III of 5.1737 and median at 5.2895,
suggesting that there is still the need to produce more
inclusive convergence goods. The overall Int mean being thehighest gives a good indication of the industry players in
Malaysia intending to adopt inclusive innovation whendeveloping goods and services for their customers in the
future.
The above was further confirmed when each participant
was asked whether they think that it is important for theindustry to develop inclusive convergence goods andservices, with a mean of 6.03 and median at 6.0. The groupalso agrees that such goods or services can also be appealing
to the mass market (mean 5.13 and median 6.0). The group in
general reported being unable to identify specific inclusive
convergence goods or services or policies specificallydesigned to promote the development of such goods (mean
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016

4.73 and 5.13 respectively).
Comparing the means between the male and female
participants shows some marked difference in their overall
attitude towards inclusive innovation with the maleparticipant showing a lower mean than female suggesting lessthan favorable attitude towards inclusive innovation in thecontext of their industry. However, when asked on theirintention to adopt inclusive innovation in their business (theparticipants were asked to assume that they have the authority
to decide), both gender recorded higher mean from theirinitial attitude, with male executives recording higher
intention than their females colleagues.
The respondents were regrouped into two generations –younger (10, 33.3%) and older (20, 66.7%) – with the age 35
years old being the threshold age. Similar comparative
analysis done according to gender earlier was carried out
according to the generations. There are some marked
differences between the two groups with the younger
generation recording higher means for attitude, subjective
norms and intention. On the other hand, the older recorded
higher means for PBC and III. See Table III below for the fullmeans comparison.
TABLE IIICOMPARING MEANS
Constructs

Male

FemaleYounger

Older

Overall III

5.1796

5.1661

5.05265.2343Overall Att

3.75494.94874.43334.1917Overall SN

5.13245.42315.47505.1500Overall PBC

4.85294.67314.67504.8250Overall Int

5.82355.59625.95005.6125The comparative analysis was also conducted in terms of
how the groups responded on the questions on i) whether they
think that it is important for the industry to develop inclusive
convergence goods and services, ii) whether such goods orservices can also be appealing to the mass market, iii) ability
to identify specific inclusive convergence goods or services,
and iv) ability to identify policies specifically designed topromote the development of such goods.
The female respondents in general recorded higher meanson all four questions than their male counterparts. The
younger respondents of the group recorded higher means on
the first two questions than their older colleagues and thepattern was flipped on the last two questions, with the older
group recorded higher means. See following Table IV for thefull means comparison.
TABLE IV
COMPARING MEANS
Constructs

Male

FemaleYounger

Older

Important

6.00006.08006.40005.8500Mass Market

4.94005.38005.50004.9500Goods/Services

4.47005.08004.40004.9000

Policies

5.12005.15005.00005.2000924

IV. DISCUSSIONS
The findings indicated that the Malaysian communicationsindustry players moderately agreed on the inclusiveness of theconvergence goods currently available in the markets.
However, when asked further, the participants largely unable
to identify specific goods and policies for the production ofinclusive convergence goods. Thus, the earlier moderateagreement might be more of an optimistic and hopefulperception on the side of the participants. The findings also
showed positive indications towards inclusive innovations
among the participants where the group showed a good levelof agreement on the importance and potential of inclusive
convergence goods and services. Even though, when
considering inclusive innovations in the context of theirbusinesses, the participants showed poor attitude towards it,they then reported more positive response in terms ofintention to adopt inclusive innovations if the authority to takethe decision is theirs. This suggests that more exposure andpromotions need to be done by the government to increase thelevel of awareness and understanding of inclusive innovations
within the industry. Development of inclusive convergencecommunications goods and services should be incentivized,with success stories being shared and celebrated. The
Inclusive Innovation Index (III) developed for this study
serves as a useful tool that can be used to help get thismovement under way.
The differences observed in the responses between thegenders and generations suggest there most likely different
appreciations and behaviors towards inclusive innovationaccording to gender and age. This hypothesis seems to besupported by some previous reports and researches. In the
Mobile Behavior Report 2014 by Salesforce Inc. [37], interms of gender, females were ahead of males in smartphoneownership. Similar differences were reported in reports by
CEOWORLD Magazine in 2014 [38] and by Intel in 2013[39], where females were reported to lead in;• installing mobile apps
• purchasing apps
• willingness to pay more for the apps
• playing mobile games.
Furthermore, according to a report by Deloitte in 2014[40], the older generations in developed countries showedsignificant growth in smartphone ownership and high mobile
apps download rate. However, in a report by Pew ResearchCentre also in 2014 [41], IT adoption rate among the older
generation was still lagging behind that of the younger
generations. Furthermore, studies have shown that the
younger generations tend to have higher self-efficacy in IT[42].
However, it is not possible to deduce conclusively on the
differences among gender and age groups based on thefindings of this study due to the small sample size.
Next, this study should be implemented with a larger
sample size in order to gain better insights to the true state.
The design of the research project would need to be refinedfor practical purposes. The masterclasses would be hard to be
replicated when dealing with larger sample size. Thus, thecontent from the masterclasses need to be condensed andpresented in formats that would suit a survey study. It is

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

proposed that a short informative tutorial video is produced
which can be included as the introductory portion of theonline survey. A textual version should also be produced andpresented as leaflets to be provided along with thepaper-based survey. The survey participants should include
all stakeholder groups of the communications sector.
Differences in the findings among the different stakeholder
groups should be explored and implications discussed togenerate comprehensive recommendations.
V. CONCLUSIONSThe convergence trend will continue to be the major
driving trend in the communications sector. The trend isfueled by rapid development of various related technologies,
which then spur more innovative sparks that will continue totake the trend into wider aspects of human lives.
Innovation capacity is thus the critical factor fororganizations to ensure their continued performance andcompetitiveness. Malaysian organizations need to develop
their ability to become global trailblazers; not to becomplacent and satisfied to only follow global trends, norbanking on continued government protection of domestic
players.
It is important to recognize the directions the convergenceevolution is taking and to plan for the various infrastructure aswell as policy needs. Readiness is crucial in order to be in stepwith the technological progress and market expectations.
The findings from this study suggest differences in attitude
towards inclusive innovation may exist due to gender and age.
However, the findings from this study are not sufficient for aconclusive argument. What is clear is that such lines ofinvestigations are worth exploring and may bear someinteresting findings. Comparison between the generations
should be expanded beyond the simplistic division of young
and old. Generation X, Y and millennials may behave andperceive inclusive innovation differently.
Furthermore, caution is needed in terms of how we frame
the trend. Digital lifestyle, modern family, the new consumer,etc.; there is a risk of making the convergence evolution
non-inclusive.
If convergence ends up a privilege enjoyed by thesophisticated, urban and IT haves, the effects oftechnological/digital divide will be more severe,
disenfranchising more among the society and may put thenation’s development as a whole at risk.
Some may still argue that ensuring inclusion is not the
responsibility of the private sector. However, earlier studies
have argued it otherwise. Armed with insights provided fromstudies such as this, ensuring the inclusiveness of
convergence should be an agenda promoted by thegovernment and implemented by the industry.
ACKNOWLEDGMENT
This study was made possible by the Fundamental
Research Grant Scheme (FRGS) from the Ministry of Higher
Education, Malaysia.

925

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Kamarulzaman Ab. Aziz is currently an associate
professor and the Director of the Entrepreneur
Development Centre at Multimedia University,
member of the Centre of Excellence for Business
Performance (CeBP) and the Centre for Knowledge &Innovation Management (CEKIM), and was the
Deputy Dean (R&D) of the Faculty of Management,
Multimedia University. He was also the founding
president of the AKEPT Young Researchers Circle(AYRC). His research interest includes Cluster Development, Technology
and Innovation Management, Entrepreneurship and Commercialization.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016927

Performances of Polar Codes in Steganographic
Embedding Impact Minimization
Birahime Diouf, Idy Diop, Sidi Mohamed Farssi
Department of Computer Science, Polytechnic Institute (ESP) / Cheikh Anta Diop University (UCAD), Dakar,
Senegaldioufbira11@yahoo.fr, idydiop@yahoo.fr, farsism@yahoo.com

Abstract—Syndrome coding is used in practice by many
authors to define steganographic schemes that minimize
embedding impact. Polar Codes, recently introduced, are thefirst capacity-achieving codes with low complexity of encoding
and decoding. In this paper we propose a new practical polarcoding methodology for constructing steganographic scheme.
We use syndrome coding with binary embedding operation. The
approach exploits the form of the syndrome, calculated fromcover and secret message. A connection between the syndrome
decimal value and the embedding changes position is establishedand enables defining a new steganographic algorithm. The wet
paper codes can also be implemented using this method.
Experimental results prove that the scheme minimizes theembedding impact with a reduced time complexity compared to
the first Polar Coding Steganography (PCS). The bit-reversal
permutation matrix used in polar coding is also employed inpractice to uniformly scatter the changes over the whole image.
Keywords—Embedding impact, matrix embedding, polarcode, steganography, wet paper codes.

I. INTRODUCTIONT

HE steganography is an information hiding technique thatenables to conceal a message in a cover medium x in sucha way that it existence is kept secret [1]. The cover medium
can be digital media such as an image (used in this paper), asound or a video. The steganalysis aims to detect the existenceof secret message. The sender, known as steganographer [2],should embed her covert communication or payload in acover medium in such a way that only the receiver is aware of
the existence of secret communication. The receiver can
extract the messages without being aware of the senderchoices. Steganography have both good and evil uses.
The embedding must be done by making the cover mediumchanges less noticeable as possible. In spatial domain, the bits———————————————————————
Manuscript received December 26, 2015. This work is supported by the
Laboratory of Medical Imagery and Bioinformatics in Polytechnic Institute,
Cheikh Anta Diop University, Senegal, and a follow-up of the invited journal
to the accepted out-standing conference paper of the 17th International
Conference on Advanced Communication Technology (ICACT2015).
Birahime Diouf is with the Department of Computer Science,
Polytechnic Institute, Cheikh Anta Diop University, Dakar, Senegal(Corresponding author, phone: +221 77 220 43 50; fax: +221 8253724;
email: dioufbira11@yahoo.fr).
Idy Diop is with the Department of Computer Science, Polytechnic
Institute, Cheikh Anta Diop University, Dakar, Senegal (idydiop@yahoo.fr).
Sidi Mohamed Farssi is with the Department of Computer Science,
Polytechnic Institute, Cheikh Anta Diop University, Dakar, Senegal(farsism@yahoo.com).

of secret message can be inserted at the LSBs (Least
Significant Bits) of the cover image pixels. To improve this socalled LSB technique, several propositions exist. The mostevident is (a): to make less changes as possible and (b): so thatthey were less detectable. To answer the first problem ofminimizing the number of changes (a), Crandall introduced
and conceptually described a steganographic technique [3]. A
connection between codes and the problem of minimizing thenumber of changed pixels (the constant profile) is establishedby Bierbrauer [4].The first implementation of this technique
was created with the F5 algorithm of Westfeld [5] in which
the Hamming codes were used. Afterwards, several schemes
have implemented this technique in steganography using
Golay [6], BCH (Bose-Chaudhuri- Hocquenghem) [7], [8],LDGMs (Low Density Generator Matrices) [9] incombination with the ZZW (Zhang-Zhang-Wang)
construction [10], STC (Syndrome Trellis Codes) [11], [12]and LDPC (Low Density Parity Check) [13] codes. The
second problem (b) can be solved using wet paper codes [14].
After having introduced polar codes in steganography PCS(Polar Coding Steganography) [15], we propose in this papera new practical method for minimizing embedding impact
with a reduced algorithmic time complexity. It exploits therelation between syndrome decimal value and embedding
changes position. The originality of this work lies in defining
an algorithm which gives stego medium in a single step
compared to PCS [15] and without using lookup tables [16].
Moreover, this new methodology is applied on images inspatial domain. The images are beforehand randomly
permuted to scatter the changes over isolated pixels.
This paper is organized as follows. Section II gives a brief
review of basic concepts in steganography and polar coding.
In Section III, we present PCS scheme. New steganographic
scheme is studied in Section IV. Section V provides a timecomplexity comparison between PCS scheme [15] and the
proposed new algorithm [17]. This section shows also thepractical results of the permutation of images. Section VI
concludes the paper.
II. STEGANOGRAPHY AND POLAR CODES BASIC CONCEPTS
We will denote by x = ( x1 , …, xn ) ∈ X = {0,1}n the LSBsof the cover 8-bits grayscale image and xi its ith LSB in in
spatialdomain.
Thesecretmessagem = ( m1 ,…, mm ) ∈ M = {0,1}m is embedded by slightly
modifying the cover image. This create the LSB-stego-image

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016928

y = ( y1 , …, yn ) ∈ Y = I1 ×…× I n , where I i ⊂ I such thatEmb(x, m) = arg min D (x,y)

xi ⊂ I i . We will use the binary LSB replacement method

Ext (y) = yH = m{y∈ C (m)}where I i = xi , xi (cardinality | I i | = 2 , for all i ), where xi
is xi after flipping its value. We denote by e the embedding
change vector ( y = x + e ) and H ∈ {0,1}m ×nis a parity checkmatrix of the code.
A. Steganography and Basic Concepts
Matrix embedding is introduced in steganography by
Crandall [3] to minimize the number of embedding changes.
It is based on syndrome decoding of error correcting codes.
Subsequently, several codes are used to implement thistechnique in steganography.
1) Distortion function:
The embedding impact produced by cancelling the secretmessage m in the cover vector x will be measured using a
distortion function D. In this paper, we limit ourselves to an
additive distortion [12]nD (x, y) = ∑ ρi (x, yi ) ,

(2)

i =1

where ρi(x, yi) is the cost of replacing the cover pixel xi withstego pixel yi. Note that ρi may arbitrarily depend on the entire
cover image x. The independency of the value of ρi(x, yi) tochanges made at other pixels implies that the embedding
changes do not interact. In others words, the change of a pixel
has no effect on the other pixels. In the case of binary
embedding operation, the distortion can be written as [11]nD (x, y) = ∑ ρi x i - yi

(3)

i =1

2) Minimizing embedding impact:
In this paper we use the PLS (Payload-Limited Sender) thatthat is to embed a fixed average payload while minimizing the
average distortion opposed to DLS (Distortion-Limited
Sender) that maximize the average payload while introducing
a fixed average distortion [12]. The PLS is more commonlyused in steganography when compared to the DLS. For a PLS,
the sender tries to embed her secret message m so that the total
distortion D is minimized; that make the resulting
stego-system less detectable (more secure). This problem has
been approached using variants of syndrome coding [5]–[13].
The sender and the receiver, respectively, implement theembedding and extraction functions Emb : X × M → Y and
Ext : Y → M satisfying

Ext ( Emb ( x, m ) ) = m ∀ x ∈ X , ∀m ∈ M ,(4)

The embedding is seen as being universal because thedistortion function D is unknown at the receiver. For abinary linear code C of length n and dimension n − m

(5)

T

where

{

}

C (m) = z ∈ {0,1} | zH T = m
n

is

the

coset

corresponding to syndrome m and all operations are inbinary arithmetic. The extraction function is equivalent to

Ext (y) = yH T = m ↔ Ext (e) = eH T = m - xH T ,(6)Then, in constant profile, searching the stego vector yamounts to search the change vector e of minimal weight inthe coset C (m - xH T ) . Syndrome coding is capacity achieving
for the PLS problem if random linear codes are used.
Unfortunately, random linear codes are not practical due tothe exponential complexity of the optimal binary coset
quantizer (4), which is the most challenging part of the
problem.
3) Wet Paper Codes:
The wet paper channel is based on pixels selection
technique which consists in choosing pixels whose change isless perceptible by human visual system and having lessstatistical effects on the cover image. In the case where all
pixels are assigned ρi = 1 (the so called constant profile),
minimizing the distortion D is reduced to minimize thenumber of embedding changes. However, in practice somepixels of the cover image can be more sensitive to change thanothers. The first called wet pixels (with ρi = ∞); we must force
the embedding algorithm to keep such pixels unchanged. The
second called dry pixels (with ρi = 1) can be changed. In thiscase we say that we have a wet paper channel [15]. The
syndrome coding is also applied to this type of channel using
wet paper codes [11], [12] and [15]. The polar code design for
wet paper channel with a given relative wetness
τ = {i : ρi = ∞} / n , withe |X| denotes the cardinality of the set
X, is described in Section IV-B.
B. Polar Codes
Based on a new paradigm of coding, polar codes aredefined as the first codes that achieve the channel capacity,
limit established by Shannon. A polar code of length n = 2p
and dimension k will be denoted by PC(n,k). W is a B-DMC(Binary-input Discrete Memoryless Channel). The symmetriccapacity [18] of W is denoted by I(W) and the reliability
parameter is Z(W). Let A and its Ac respectively denote
information and frozen bits sets. The construction of polar
codes is based on channel polarization. It consists insynthesizing of n independent copies of a given B-DMC W tocreate n others channels {Wn(i ) :1 ≤ i ≤ n} . It is made up two
steps: channel combining and channel splitting [18] with wesummarize as follows:
combining

splitting(W, W,..., W) → Wn 
→ {Wn(i ) }i =1,…, n.

(7)

The channel combining combines n copies of a given
B-DMC W in a vector channel Wn. It is done recursively by
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016929

combining two copies of Wn/2. During channel splitting we
subdivide Wn into n channels Wn( i ) , 1 ≤ i ≤ n. Channel
polarization can be seen as a recursive channel transformation
process which can be represented as follows [15], [17]:( W , W ) 
we construct → ( W
(i )
n

(i)
n

(2 i −1)2n, W2(2n i ) ) .

(8)
Fig. 2. Equivalent of polar coding scheme.

The polar coding is done using the following relationships:
The most reliable Wn( i ) are used to carry the information

x1n = u1n G nG
G n = Bn G ⊗2 p = Bn  n / 2
G n / 20 ,
G n / 2 

(9)bits and the least reliable ones contain the frozen bits
Z (Wn( i ) ) ≤ Z (Wn( j ) ) , for any i ∈ A and j ∈ A c
Polar codes are several applications in information theory
and have been recently introduced in steganography [15].

with Bn is a bit-reversal permutation matrix, Gn is a
generator matrix, u1i = (u1 ,K , ui ) , with 1 ≤ i ≤ n and G 1 = [1]III. FIRST PCS (POLAR CODING STEGANOGRAPHY) METHOD

and G 2 = 11 01  . The Kronecker product between matrix 
A = [A ij ] , 1 ≤ i ≤ n and 1 ≤ j ≤ m and B = [Bij ] , 1 ≤ i ≤ q andcode PC(n,k).

1 ≤ j ≤ r is defined by A11B L A1n B A⊗B =  M
O
M 


L
A
B
A
mn B 
 m1

Denote by SPC(n,m=n-k) the steganography based on polar
A. Construction of Polar Codes in Steganography
The construction of polar codes for the purposes ofsteganography can be summed up in three steps [15] as shownin Fig. 3.

(10)which is a mq×nr matrix. The Kronecker power is defined byA ⊗ p = A ⊗ A ⊗ ( p-1) , for all p ≥ 1, with A ⊗ 0 = [1] .

Fig. 3. Construction of polar codes for steganographic purposes.

For polar code PC(8,4), we have:1 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0
11 10 10 00 00 00 00 00
11 00 10 00 10 00 00 00
1 1 1 1 0 0 0 0⊗p
G 2 = 1 0 0 0 1 0 0 0 and G n = 11 10 10 00 10 00 10 00
1 1 0 0 1 1 0 0
1 1 0 0 1 1 0 0
11 01 11 10 11 00 11 01
11 11 11 11 10 00 10 10

We calculate the reliability parameters as follows [15]:Z ( Wn( j ) ) = 2Z ( Wn((/ 2j +1)/ 2) ) − Z ( Wn((/ 2j +1) / 2) ) if j is even2

(11)

The information word u1n is transformed in a code word
x1n . Each bit xi of x1n borrows a copy of W and the gives thebit yi of the received word y1n as shown in Fig. 1.

Z ( Wn( j ) ) = Z ( Wn( /j2/ 2) )2

, (12)

if j is odd

The initial value is calculated withZ ( W1(1) ) = Z ( W ) = 2 W ( 0|0 ) W ( 0|1) = 2 pe (1 − pe ) , (13)where pe is the error probability of the channel W ,pe = W(0 |1) = W (1| 0 ) and 1 − pe = W(0 | 0) = W(1|1) .

Fig. 1. Polar coding scheme.

In polar coding if u1n has a uniform distribution then Wn( i )is the channel really seen by ui (Fig. 2).

To obtain A and A c we select channels with theparameters of the lowest reliabilities for data bits. The indicesof these channels form the information bits A . Its cardinality
is equal to the dimension k of the considered polar code. Then − k other channels carry redundancy bits. Their indicesconstitute A c .
To determinate a parity check matrix of a polar code, we
use the lemma given by Goela et. al. [19, Lemma 1] whichstates that if the frozen bits are equal to 0 then the transpose of
the parity check matrix H of the polar code is given by thecolumns of the generator matrix Gn whose indices are in Ac.
As examples, we use a polar code PC(4,1) for thesteganography SPC(4,3) and PC(8,4) for SPC(8,4).

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

For PC(4,1), A = {4} , A c = {1, 2, 3} and a parity checkmatrix is:

argminIf

1 1 1 1
H = 0 0 1 1
0 1 0 1

and its transpose HT = wePC(8,4)

use

then

0
0
1
1

0
1
0
1





(14)

A = {4, 6, 7, 8},

A c = {1, 2, 3, 5} and from (11) we have :1 0 0 0
11 00 01 10
1 1 1 1 1 1 1 1
0 0 0 0 1 1 1 1and HT = 11 01 10 10
H= 
0 0 1 1 0 0 1 1
1 1 0 1
0 1 0 1 0 1 0 1
11 11 11 01



(15)

B. First Step
By making the most of the particular form of H and its
transpose HT, we can transform the equations of the relationyHT = m in a system allowing calculating the coefficients ofthe stego vector y (see [15]):

yi = yi +1 H (Ti +1), j + L + yn H nT j + m j ; j = n − k down to 1 (16)with i the position of first 1 on column j of HT. For each j, we
calculate the corresponding yi. The vector y must be
initialized to the cover vector x before the calculations. With(16), we obtain a stego vector yp verifying yHT = m but it is
not the closest to the cover vector x.
C. Optimization of the First Solution
The objective of this step is to find the stego vector yclosest to x by using the polar code PC(n,k). Let ep be theembedding change vector corresponding to the stego vector yp
found with the first step. The distortion (3) can be written:
n

(17)

i =1

where |xi − yi| = ei and ρi = 1 for constant profile and ρi = {1,∞}for wet paper channels. The insertion and extraction functions
become:Emb(x, m) = arg min D (e)e ∈ C (s)Ext (y) = yH = m ⇔ eH T = s = m - xH T

e ∈ {0,1}n binary vector
 TTeH = m − xH = s
Tep initial solution ⇔ e p H = s(19)with f the objective function and ρ = {ρi}1≤i≤n the change cost
vector. This is a problem of linear programming written instandard form. It can be solved using the methods simplex orinterior points [15].
IV. NEW POLAR CODING STEGANOGRAPHIC ALGORITHM

The steganographic scheme is made up two steps.

D (e) = ∑ ρi ei

f (e) = D(e) = < ρ, e > = ρT e
s.t

e
1
1
1
1

930

(18)

T

Considering the problem in the following three points [15]:• we have a first solution ep ® initial solution,• we have to minimize the distortion D(e)®
minimization problem,
TT• verifying eH = m - xH = s ® constraints,
we have a minimization problem with equalities constraints
and initial solution ep. The problem can be formalized asfollows:

In [16], we have proposed two approaches for complexity
reducing. The first use lookup tables and the second exploits
the form of the syndrome but its definition is based on lookup
tables. Additionally, only the second approach was
implemented in practice. In this section we propose a newversion of the second approach which does not need lookup
tables, which is implemented in practice and compared to
PCS. We will consider constant profile case and wet paper
codes.
A. New PCS for Constant Profile
Consider the polar code PC(4,1) for the steganography
SPC(4,3). According to (14), the columns H.j, 1 ≤ j ≤ 4, of theparity check matrix H satisfy the following equations:

H .1 + H .2 = H .3 + H .4 = (0 01)T
H .1 + H .3 = H .2 + H .4 = (010)T
H .1 + H .4 = H .2 + H .3 = (011)T(20)

When using polar code PC(8,4) for steganography
SPC(8,4), the inequalities verified by the columns H.j, 1 ≤ j ≤ 8of the parity check matrix H (15) are:H .1 + H .2 = H .3 + H .4 = H .5 + H .6 = H .7 + H .8 = (0 0 01)T
H .1 + H .3 = H .2 + H .4 = H .5 + H .7 = H .6 + H .8 = (0 010)T
H .1 + H .4 = H .2 + H .3 = H .5 + H .8 = H .6 + H .7 = (0 011) T
H .1 + H .5 = H .2 + H .6 = H .3 + H .7 = H .4 + H .8 = (010 0)T (21)
H .1 + H .6 = H .2 + H .5 = H .3 + H .8 = H .4 + H .7 = (0101) T
H .1 + H .7 = H .2 + H .8 = H .3 + H .5 = H .4 + H .6 = (0110) T
H .1 + H .8 = H .2 + H .7 = H .3 + H .6 = H .4 + H .5 = (0111)TFirst calculate the syndrome s = m - xHT. If it is equal to:• Synd. 1: zero vector then the embedding change
vector e is also equal to zero vector;• Synd. 2: one column of H let be H.j (s = H.j), then it has
as first element 1 and the embedding change vector e has onlyone 1 at position j. The columns H.j (j = 1:n) of H represent thebinary values of the numbers between n and 2n-1 (see, forexample, (20) and (21)). Thus, on column j, we have thebinary representation of n+j-1. The decimal value dec(H.j) = n+ j - 1. Hence, j = dec(s) – n + 1;
• Synd. 3: sum of two columns of H (H.1 and H.j) then it
has a 0 as first coefficient, see (20) and (21). The decimalvalue varies between 1 and n-1. The embedding change vector
has two 1; the first at the first position and the second at
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016

position j. The decimal value of the sum of the two columns
H.1 and H.j is equal to ((n) + (n+j-1)) mod 2n = j-1. Thendec(H.1+H.j) = j - 1. Hence, j = dec(s) + 1.
These three cases are also valid for the equivalent1 systems.
According to these observations, there is a relationshipbetween the decimal value of syndrome s and the position of
the 1 of the embedding change vector e. A necessary
condition is 2n-k=2∙n=2p+1. Then n-k=p+1. Hence
k=n-1-log2n=2p-1-p. The validity of these observations
concerns the values:p ∈ {2,3, 4,5,6,7} = Pn ∈ {4,8,…,128} = Nk ∈ {1, 4,…,120} = K(22)with n = 2p, k = 2p – 1 – p and p ∈ P .
For an arbitrary polar code PC(n,k) [18], the length n is a
power of 2 and the dimension k is a positive integer in {1, 2,…, n-1}. For a polar coding steganographic scheme, theoptimality condition [15] is m = n – k > p = log2 n. The
parameters of our polar code in the proposed approach satisfy
this optimality condition because we have n – k = p + 1 > p.
Consider a given PC(N=2P,K) for steganography
SPC(N,N-K). If NÏN (i.e. PÎ{8,9,¼} = ℕ\(P∪{0,1}))) orKÏK, we can always come down to a validity case. For NÎN,if KÎK then we apply directly the steganographic methodwith SPC(N,N-K) else we normalize K. For NÏN, wenormalize N and then K.
• Normalization of N: subdivide N in several integers n
so that nÎN. Since N and n are both power of 2 (N = 2P and
n=2p), with N > n, then N is divisible by any nÎN. The ratio
N 2P== 2P− p = 2a is a power of 2. Thus, we obtain 2an 2p
segments of size nÎN each.
•

Normalization of K: we aim to bring K back to an
integer kÎK. But, since we are interested in the size m = n-kof the message for steganography rather than k, then we willsubdivide N-K in n-k = p+1 parts such as n = 2pÎN and k =(n-1-log2 n)ÎK. Since we know n, we can determine k. N-K is
not always divisible by n-k. Let N-K = (n-k)⋅q+r, with 0 ≤ r <n-k. If r = 0 then we subdivide N-K in q segments of size n-k.
Otherwise (i.e. 0< r < n-k), we have q segments of size n-k andanother one of size r. In this case, we complete this segmentwith (n-k)-r bits 0 to have a size equal to n-k.
The embedding is done by pair of a cover medium segment
and a message segment. The number of cover segments must
be equal to or greater than the number of message segments.
The following algorithm (Algorithm 1) calculates a coset
leader for a given syndrome s.

931

Algorithm 1 Calculation of a syndrome coset leader.
Inputs: cover vector x, message m and parity check matrix H.
Outputs: syndrome coset leader e.
1: Initialization:
2: p ← an element of P; n=2p; k ← n–1–p;
3: e ← (0, …, 0); y ← x;
4: Calculation:
5: If xHT ≠ m then6:s ← m – xHT;
7:
calculate decimal value of binary syndrome vector
8:(dec← decimalConversion (s))
9:
if 1st coefficient of syndrome s is equal to 1 then10:affect the (dec+1-n)-th coefficient of e to 1;
else
11:
12:
affect 1st and (dec+1)-th coefficients of e to 1;
13:
end (if)
14: End (If)
The function decimalConversion(s) converts a binary
vector s into its decimal value.
For a given parameter p not in validity domain, we canalways come down to valid parameter by subdividing it to oneof the valid parameters in P. Furthermore, we can choose oneof the valid parameters pÎP and subdivide the cover medium
size N to nÎN and the secret message size to n-k with kÎK.
This implies that we can choose the parameter p, which
minimizes well the embedding impact.
B. Wet Paper Polar Codes
In this section, we explain how polar codes can be used forthe wet paper channel. Give first two theorems that makeapplicable polar codes for wet paper.
Theorem 1 (Rank of the parity check matrix): The rank ofa parity check matrix H of a polar code of block length n anddimension k isrank(H) = n − k(23)Proof: The generator matrix of the polar code Gn isinvertible [18] i.e. the columns of Gn are linearly
independents (none of the columns is linear combination of
the others). This is equivalent to rank(Gn) = n. The matrix HT
is obtained by pruning the k columns of Gn whose indices arein the information set A [19]. Then Gn is the matrix HT at
which we add k others columns which none is linear
combination of the others columns of HT. Thus rank(HT) + k= rank(Gn) = n. Since rank(HT) = rank(H). Then rank(H) + k =n. Finally rank(H) = n – k.
Consider still the set of wet elements J. The maximum
number of positions that we can lock for the wet paper
steganography is n – rank(H) = k.
Theorem 2 (Maximum number of locked elements): Let
SPC(n,m=n-k) denote the polar coding steganography suchthat nÎN and kÎK. The maximum number ℓmax for which weare always able to lock any combination of ℓmax positions is

1

Equivalent denotes the system obtained for another polar coding
steganographic parameter n different to 8.

l max =

n
−1
2

(24)

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

Proof: Consider, for example, the lock of n/2 last positionsof the cover vector. This amounts to prune the n/2 lastcolumns of the parity check matrix H for the matrix productyHT = m. In this case, the second row of the matrix H has all
its elements equal to 0 and may then, be written as linear
combination of the others (see for example (14) and (15)).
This means that we can’t always lock n/2 positions or more.
The maximal number of positions that we can always lock, for
any combination, is then less than n/2. It is between 1 andn/2-1. Let ℓ be the number of locked positions, then 1 ≤ ℓ ≤n/2-1. In our steganographic problem, we must lock a numberof positions such that yHT = m has, at least, one solution.
Then, the system must have a number of unknowns more thanor equal to the number of equations. The number of unknowns
after locking is equal to n-ℓ and the number of equations is n-k= 1+log2(n) = 1+p. Thus, we must have n-ℓ ≥ n-k then ℓ ≤ k.
The value of ℓ have two constrains (ℓ ≤ n/2-1 and ℓ ≤ k). Soℓmax = min{k, n/2-1} = min{n-1-log2(n), n/2-1}. Prove thatℓmax = n/2-1. This amounts to demonstrate that n/2-1 ≤n-1-log2(n). That is equivalent to prove that their difference d= n-1-log2(n)-n/2+1 = n/2-log2(n) = 2p-1-p is positive or null.
Consider the following function f : N \{0,1} → Z such thatf(p)= 2p-1-p. This function is continuous and differentiable. Itsderivative is f’(p)=(p-1)∙2p-2-1 ≥ 0, for p ≥ 2. This mean that f
is monotonic and f (2) = 0. Then f(p) ≥ 0 for any p ≥ 2 ⇒ d ≥0. Consequently n-1-log2(n) ≥ n/2-1. Finally, we have ℓmax =n/2-1.
On the one hand, we can lock k positions but not any ones.
On the other hand, any combination of n/2-1 positions can bechosen for the locking. Then, to ensure to always succeed thelocking, we will not exceed ℓmax.
Let J be the set of wet elements, then:• If syndrome s is in Synd. 1 then we do nothing because
e is also a zero vector;• If s is in Synd. 2 then consider, by quadruplets, thedecimal values of the n syndromes whose embedding change
vectors have a single 1. These syndromes correspond to the ncolumns of H and constitute the half of all possible 2n-k = 2n.
We have n/4 quadruplets and each consists of four
consecutive syndromes:
Qi = {n + 4i − 4; n + 4i − 3; n + 4i − 2; n + 4i − 1} , i = 1,K, n / 4 (25)

Searching to know the quadruplet Qi of a given syndrome s,
we calculate its index i by:

i = (dec(s) − n + 1) /4932

vectors, ei∙HT = si, for i=1, 2, 3. In each quadruplet, a
syndrome is equal to the sum of the three other; therefore s =s1 + s2 + s3. Let e4 = e1 + e2 + e3, then e4∙HT = e1∙HT + e2∙HT +e3∙HT = s1 + s2 + s3 = s. Thus e4 is in the coset of s.
Consequently, to lock the position j, we choose as embedding
change vector e4. Each of the vectors e1, e2, e3 has only one 1
respectively at different positions j, h, and t. Then e4 has three
1 at positions h, l and t. If at least one of these three positions
is in J, then we search another embedding change vector with
1 at positions not in J. To do so, we choose a pair in the triplet
containing one or two elements belonging to J. The chosen
pair, let be (h, l), is then replaced by another pair (f, g) which
is not included in J with the equality of an equivalent systemof (21) (see Algorithm 2). The new embedding change vector
will have 1 at positions f, g and t which and 0 at replaced
positions h and l.
• If s is in Synd. 3 (i.e. e has two 1 at positions 1 and jwhich at least one is in J), then we search with (21) or anequivalent, the pair (h, l) not included in J using Algorithm 2.
The embedding change vector will have then two 1 atpositions h and l.
For position replacement, the algorithm is as follows:
Algorithm 2 Replacement of a pair by another not in J.
Inputs: pair to replace (i, j) the set of wet element J.
Outputs: the new pair obtained (l, t).
1: While (not found and not end of J)
2:

Search a first position l1 (from 1 to n) not in J

3:
Search a second position t1 (t1> l1) not in J
5: If ((l1, t1) verifies with (i, j) one of the equalities of (21))
6:l ← l1; t ← t1; return (l, t);
7: Else If (not end of J)
8:
Go to line 3.
9: End (If)
10: if (no good pair (l1, t1) is found)11:
Go to line 2.
12: end (if)13:
Repeat until having a good pair (l1, t1)
14:l ← l1; t ← t1; return (l, t);
14: End (While)with bin(a) is the binary value of the number a.
The new proposed scheme can be summarized as follows:(26)

where   denote the ceil operator and dec(s) is the decimalvalue of the syndrome s.
Proof: According to (25), dec(s) varies between n+4i-4 andn+4i-1 for quadruplet Qi. Therefore (dec(s)+n+1)/4 isincluded between i-3/4 and i. Thus, when applying the roundto the upper bound then (dec(s)+n+1)/4 is between i-3/4=i
and i. This gives (26).
Let s, s1, s2 and s3 be the syndromes forming a quadruplet Q
and e, e1, e2, e3 their corresponding embedding change

Fig. 4. New polar coding steganographic scheme.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016933

The upper part of the scheme deal with the constant profilecase with the three possible cases. In the case of wet paper
channel we continuous with the lower side by replacing the
wet elements indices. After replacement, the new positions
are set to 1 and the old reset to 0 in the embedding change
vector e. After calculating e, we can obtain the stego vector by
y = x + e.
V. EXPERIMENTAL RESULTS
We have represented in Fig. 5 the embedding efficiency
e=m/D(x,y) of the proposed method in wet paper channel
according to relative wetness t = |{i : ρi = ∞}|/n. We havelooked n/2-1 elements and then t = (n/2-1)/n.
For relative wetness t varying between 0.25 and 0.5, theembedding efficiency increase from 2.4 to 5.9. The increasing
is faster than the relative wetness is great. This proves the
goodness of the embedding efficiency.

Fig. 6. The execution time of the two schemes for constant profile.

The execution time of the new algorithm is lower than the
PCS scheme [15] in constant profile (Fig. 6) as well as in wet
paper channel (Fig. 7). The difference between the two curves
increases with the size of the cover vector n. This allows us topronounce on complexity reducing. Therefore, the scheme
proposed in this paper allows minimizing the embedding
impact with a reduced time complexity when compared to
first PCS.

Fig. 5. Embedding efficiency for wet paper codes.

We have also given the complexity variation of thesteganographic scheme based on polar codes [15] and thoseof the algorithm proposed in this paper. To compare thecomplexity of algorithm PCS with the new algorithm, we
measure the required time resources amount for solving the
problem of minimizing the embedding impact (here, researchof the embedding change vector). For that, we observe their
execution time on a computer. We perform several tests on
Dual Core CPU running at 3.46 GHz with 2 GB RAM. We
chose a polar code of block length nÎN and dimension kÎK
because our algorithm is applied to these values (see (22)).
For each pair (n,k)Î(N,K), 20 cover vectors and 20 messages
are randomly generated. Then, we calculate the execution
times average (in seconds) of messages embedding in covervectors. This calculation is done for the two algorithms.
The obtained results for constant profile and wet paper
channel cases are respectively represented by Fig. 5 and Fig.
6. Each curve represents specifically the average execution
times of the research algorithm of the embedding change
vector corresponding to the syndrome calculated fromrandomly generated cover vector and message. The execution
time curve of PCS algorithm is blue and the red one represents
the proposed new algorithm.

Fig. 7. The execution time of the two schemes for wet paper channel.

We can test the embedding scheme with cover images
coming from BOSSbase database version 1.01 (Break Our
Stego System) [21] containing 10.000 512×512 8-bit
grayscale images of pgm format coming from rescaled andcropped natural images of various sizes of eight diﬀerent
cameras.
To make the message less detectable, we choose to permute
the pixels of the cover image before embedding. Because theimages have a fixed size of 512×512 pixels and 512 is a powerof 2, we can use the bit-reversal permutation matrix B512,
described in Section II-B, for permutation. This permutation
matrix B512 can be used to permute the rows and the columnsof the cover images before embedding the secret message asshows by Fig. 8. After permutation the obtained image issplitting in 512/n=29-p blocs because the bloc length n=2p ofthe used polar code is also a power of 2. Further, we canpermute the rows and the columns of these n×n pixels bloc
images using bit-reversal permutation matrix Bn. Then, we
repeat the same process as in Fig. 8 with bloc images IiRC andBn.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

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

934

In Fig. 10, white pixels correspond to changes by +1 or −1
and the black ones correspond to pixels that did not change.
For the rows and columns permuted image, the changes are
uniformly distributed over the whole image (right) whencompared to the image without permutation (left) in which thechanges are all at the top of the image. The changes in thestego rows and columns permuted matrix will, of course,
more hard to be detected by an attacker.
Fig. 8. Permutation and splitting images.

Thus the changes will be scattered over isolated pixels of
the image making less detectable the secret message andallowing a more secure insertion. After insertion, it isnecessary to find the original order of pixels of the coverimage. To achieve this, we still use the matrix Bn since it isinvertible and equal to its own inverse. Note that permutation
technique was used in the pass but it depended on a key
derived from a password. The receiver needed the correct
secret key to be able to repeat the permutation which had
linear time complexity O(n) in [5]. Our permutation techniquedepends only on the bit-reversal permutation matrix Bn whichis already used in the construction of the polar code.
In this manner, we have four images choices to embed thesecret message. We can choose the original cover image I, or
the rows permuted image IR, or the columns permuted image
IC, or rows and columns permuted image IRC. This secretchoice can be shared with de receiver and is unknown to allanother person. The image ‘28.pgm’ of BOSSbase is used toillustrate the permutation effects. The original image and thethree permuted images are shows in Fig. 9 (top-left theoriginal image, top-right the rows permuted image,bottom-left the columns permuted image and bottom-right therows and columns image).
As we can see, the black band on the right columns of theoriginal image is also visible on the rows permuted image.
In the same, the white pixels on the top remains on the top
rows of the columns permuted image. Conversely, for therows and columns permuted image the pixels are uniformly
distributed.

Fig.10. Positions of the embedding changes on non-permuted image (left)
and rows and columns permuted image (right) when 0.2 bpp (bit per pixel) isembedded in ‘28.pgm’.

VI. CONCLUSION
We proposed, in this paper, new practical steganographic
methodology based on polar codes that significantly reducethe complexity of PCS scheme [15] without using lookup
tables [16]. This approach exploits the form of the syndrome
calculated from the cover medium and the secret message, todetermine the embedding change minimizing the distortionfunction. A relationship between the decimal value of the
syndrome and the position of non-zero elements of theembedding change vector is established. This relationship isused to evaluate the changes position on the cover vector. As
PCS, this method allows minimizing the embedding impact
with a reduced time complexity. The algorithm proposed inthis paper provides good performance in terms of embedding
efficiency and has a lower time complexity than PCS for bothconstant profile and wet paper cases as shown by the
execution time comparison curves of the two schemes. We
have also applied the scheme on images in spatial domain. We
have chosen to permute the pixels of images before
embedding the private message. The permutation can be done
only on the rows or only on the columns or both on the rows
and columns of the cover image. This allowed scattering thechanges at isolate pixels of the image and made thestego-system more secure.
As part of our future research, we plan to propose anadaptive steganographic scheme based on polar codes using
adaptive linear programming decoding of polar codes. We
also plan to propose a method of steganalysis.
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Fig. 9. Original image and different permuted images.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 5, September 2016
J. Bierbrauer, “On Crandall’s Problem,” [Online]. Available:
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935

[4]

Birahime Diouf received the Electronic andTelecommunications Engineering Degree from Gaston
Berger University (UGB), Saint-Louis, Senegal in2012, and the M.Sc. in Computer Science, Modelingand Simulation of Complex Systems from Polytechnic
Institute, Cheikh Anta Diop University (UCAD),
Dakar, Senegal in 2013. He is currently working
towards the Ph.D. degree.
He is currently a Researcher at the Department ofComputer Science, Polytechnic Institute, Cheikh Anta Diop University. His
research interests include data hiding, information theory, coding theory,
image processing and signal processing.

Idy DIOP graduated from the University of Dakar.
He received his engineering degree from Electronic
and telecommunication in 2006 to the Gaston Berger
University of Saint-Louis of Senegal, and a Diploma
of master research: Physics for Engineers in Ecole
Supérieure Polytechnique (ESP), Dakar-Senegal(2007). He holds a PhD in Engineering Thesis (2011):
watermarking medical image based on JPEG 2000
ESP; He is co-responsible of several memories of Master in Computer
Science and Telecommunications, author of several publications ininternational journals and several studies reported with publications in theproceedings of International Congresses with peer and scientific committee
member of several international conferences. His research interests include
steganography, steganalysis, compression, watermarking, information andcoding theory and wireless communications.

Sidi Mohammed FARSSI graduated from the
University of Dakar. He received his engineering
degree from Electrical Engineering Design option(EEAI) in 1988 to the ESP, and a Diploma of
Advanced Studies: Physics for Engineers in Paris
XII (1989). He holds a PhD in Engineering Thesis(1993): Biomedical image processing in ESP (Ecole
Supérieure Polytechnique de Dakar- Senegal) andthen PhD State es-sciences (1997): Biomedical Image Processing, Dakar
ESP. He is Director of several doctoral theses in Information Processing,Director of several DEA in Computer Science and Telecommunications,
Director of several memories of Master in Computer Science andTelecommunications, author of several publications in international journals
and several studies reported with publications in the proceedings ofInternational Congresses with peer, expert player in international scientificjournals, scientific committee member of several international conferences,
participating in several workshops and training expertise, expert for theUnited Nations University for Education and Scientific Research, Memberof the reflection of TOKTEN project, Expert of 'World ORT Union, Memberof Networks of Excellence SIMILAR, Member of the Society of African
scientists, Expert for the recognition and equivalence of degrees to African
schools CAM, Expert for the Association of Francophone universities AUF..

Copyright ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 5

6J

1 Ontology Modification Using Ontological-Semantic Rules

902

Anastasia Mochalova*, Victor Zacharov**, Vladimir Mochalov** Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS , Mirnaia str. 7, 684034
Paratunka, Kamchatka region, Russia, **Petersburg State University, Universitetskaya emb. 7-9., 199034 St
Petersburg, Russia

2 A performance analysis of optimized semi-blind channel estimation method in OFDM systems

907

Sangirov Gulomjon*, Fu Yongqing*, Jamshid Sangirov**, Fang Ye* and Ahmad Olmasov****Information and Communication Engineering College, Harbin Engineering University, Harbin, 150001 China**Samsung Electronics, South Korea, ***Samarkand branch of Tashkent University of Information
Technologies, Uzbekistan

3 Terminal-based Energy-Efficient Resource Allocation in OFDMA-Based Wireless Multicast

913

Systems
Jun Liu
Research and Application Innovation Center for Big Data Technology in Railway, Institute of Computing
Technologies, China Academy of Railway Science, Beijing, China

4 Innovation, Convergence and the Disenfranchised: Investigating the Inclusiveness of921

Convergence in Malaysia
Kamarulzaman Ab. Aziz**Faculty of Management, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia

5 Performances of Polar Codes in Steganographic Embedding Impact Minimization

927

Birahime Diouf, Idy Diop, Sidi Mohamed Farssi
Department of Computer Science, Polytechnic Institute (ESP) / Cheikh Anta Diop University (UCAD), Dakar,
SenegalCopyright ⓒ 2016 GiRI (Global IT Research Institute)