scholarly journals Spectrum handoff scheme with multiple attributes decision making for optimal network selection in cognitive radio networks

2017 ◽  
Vol 3 (3) ◽  
pp. 164-175 ◽  
Author(s):  
Krishan Kumar ◽  
Arun Prakash ◽  
Rajeev Tripathi
Keyword(s):  
Decision Making ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Network Selection ◽  
Spectrum Handoff ◽  
Optimal Network ◽  
Multiple Attributes ◽  
Multiple Attributes Decision Making

scholarly journals A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Krishan Kumar ◽  
Arun Prakash ◽  
Rajeev Tripathi
Keyword(s):  
Cognitive Radio ◽  
Vehicular Networks ◽  
Mobile Network ◽  
Network Selection ◽  
Spectrum Handoff ◽  
Radio Access ◽  
Optimal Network ◽  
Multiple Attributes ◽  
Grey Relational ◽  
Multiple Attributes Decision Making

When a mobile network changes its point of attachments in Cognitive Radio (CR) vehicular networks, the Mobile Router (MR) requires spectrum handoff. Network Mobility (NEMO) in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM) methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

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