Content uploaded by Jayakumar Loganathan
Author content
All content in this area was uploaded by Jayakumar Loganathan on Jan 27, 2020
Content may be subject to copyright.
DOI: 10.4018/IJEIS.2020010106
Volume 16 • Issue 1 • January-March 2020
Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
108
Jayakumar Loganathan, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India
S. Janakiraman, Pondicherry University, Pondicherry, India
Ankur Dumka, Graphic Era Deemed to be University, Chennai, India
In the future, the wireless network environment may suffer due to the unavailability of new spectrum
bands. Cognitive radio research considers the current spectrum underutilization and provides a better
model for the next-generation wireless environment. Since the implementation of the cognitive radio
and its policies is a bigger challenge under static spectrum allocation, i.e., current wireless networks
policy, many issues are in front of us to accomplish a better cognitive radio wireless environment.
One of the major challenges is a secure transmission and efficient free channel selection. In this
research, the authors considered an efficient free-channel selection scheme as objective and derived
an integrated approach for free-channel selection with techniques, Dynamic weighted-VIKOR.
Cooperative CRN, Multi-Criteria Decision Making, Spectrum Decision, VIKOR
Cognitive radio (CR) is a form of wireless communication which consists of a transceiver that is
capable of detecting which communication channels are in use and which are not in use and move
into unoccupied channels while avoiding occupied ones. Cognitive radio is the most hopeful way for
increasing the efficiency of the spectrum by providing opportunistic access to frequency bands to a
group of unlicensed users. Increasing demand for internet usage and wireless application, the demand
for radio spectrum will be very high in the coming years. Internet traffic has estimated as 100 GB for
a day in the year of 1992, the same amount of traffic created per hour in 1997. In 2002, per second
network traffic has calculated as 100GB and in 2014, the same traffic achieved in 400th of a second.
From this study, if the usage of internet forecast for coming years, then 20-25% of the increase will
be achieved every year. On the other hand, utilization of spectrum in some of the licensed bands are
considerably very less, nearly 30% of the allocated spectrum only utilized on those bands, which
is contradictory with the increasing demand of spectrum for various applications (Akyildiz et al.,
2006). So, balancing the future requirement of spectrum for wireless applications, effectively licensed
spectrum should be utilized for various applications. The cognitive radio network is one of the feasible
Volume 16 • Issue 1 • January-March 2020
109
solutions for handling this exponential growth of network traffic in a future radio communication
system by exploring underutilized spectrum opportunities (Kolodzy, 2002; Yucek & Arslan, 2009).
A number of researches had been conducted in various levels of CRN so far such as spectrum
sensing, spectrum decision, network management, resource management, routing, etc., Spectrum
sensing models propose a technique to find opportunities by using Listen Before Talk (LBT) method.
Numerous sensing model has already been found, the energy detector is one which considered by
most of them. Spectrum decision used to decide better channel with fewer chances of interference for
transmission, here also various decision-making schemes from various areas such as game theory,
Markovian, statistical prediction models are been considered (Kumar et al., 2017; Zhu et al., 2014;
Taherpour et al., 2017; Ali & Homouda, 2017).
In this paper, we propose a novel spectrum decision making the process for cooperative CRN by
using Multiple Criteria Decision making (MCDM) scheme. So far MCDM techniques are successfully
used in operation management, decision problems and resource management. Each technique has its
own merit and challenges with it. SAW (Kaliszewski & Podkopaev, 2016), TOPSIS (Kumar et al.,
2017; Jayakumar et al., 2016; Tian et al., 2010), VIKOR (Liao t al., 2015; Wei & Lin, 2008; Gul et
al., 2016), ELECTRE (Yu et al., 2018), PROMETHE (Shaher et al., 2017) are some familiar classical
MCDM models. MOORA, ARAS and SWARA are a recent and simple model for making a decision
from multiple numbers of alternatives. This paper makes the following list of contribution towards
the channel decision model for cooperative CRN:
1. Finalizing the network model for implementing spectrum decision model for cooperative CRN;
2. Proposed spectrum decision-making model using an enhanced version of MCDM;
3. Validated the proposed model with other classical MCDM techniques.
Among many key tasks of CRN, spectrum decision is the most important task which directly related to
the overall performance of CRN (see Figure 1). After completion of spectrum sensing, list of available
Figure 1. Spectrum sensing and decision model for CRN
22 more pages are available in the full version of this
document, which may be purchased using the "Add to Cart"
button on the product's webpage:
www.igi-global.com/article/optimal-spectrum-hole-detection-
scheme-for-cooperative-crn-using-dynamic-weighted-
vikor/243706?camid=4v1
This title is available in InfoSci-Journals, InfoSci-Journal
Disciplines Business, Administration, and Management.
Recommend this product to your librarian:
www.igi-global.com/e-resources/library-
recommendation/?id=2
Related Content
RFID Implementation in Australian Hospitals: Implications for ICT Sector and
Health Informatics
Chandana Unnithan (2014). International Journal of Enterprise Information Systems
(pp. 40-61).
www.igi-global.com/article/rfid-implementation-in-australian-
hospitals/112077?camid=4v1a
The Probabilistic Profitable Tour Problem
Mengying Zhang, John Wang and Hongwei Liu (2017). International Journal of
Enterprise Information Systems (pp. 51-64).
www.igi-global.com/article/the-probabilistic-profitable-tour-
problem/185548?camid=4v1a
Understanding Information Technology Implementation Failure: An
Interpretive Case Study of Information Technology Adoption in a Loosely
Coupled Organization
Marie-Claude Boudreau and Jonny Holmström (2011). Enterprise Information
Systems: Concepts, Methodologies, Tools and Applications (pp. 1496-1512).
www.igi-global.com/chapter/understanding-information-technology-
implementation-failure/48626?camid=4v1a
Business Process Management as a Critical Success Factor in EIS
Implementation
Vladimír Modrák (2010). Enterprise Information Systems for Business Integration in
SMEs: Technological, Organizational, and Social Dimensions (pp. 24-36).
www.igi-global.com/chapter/business-process-management-critical-
success/38191?camid=4v1a
