An efficient spectrum handoff decision making scheme for cognitive radio networks. (2019)

• Record Type: Journal Article
• Authors: Preetha, K.S.; Kalaivani, S.
• Is Part Of: International journal of internet technology and secured transactions. Volume 9:Number 1/2(2019)
• Page Start: 68
• Page End: 95
• Publication Date: 2019
• Subjects: spectrum handoff -- multiple attribute decision making -- MADM -- artificial neural networks -- ANN -- probabilistic Markov model; Internet -- Security measures -- Periodicals; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1748-569X

Abstract

There has been a gigantic spike in the usage and development of wireless devices since the time wireless technology has come into existence. This has contributed to a very serious problem of spectrum unavailability or spectrum scarcity. The solution to this problem comes in the form of cognitive radio networks where the secondary user which is also known as the unlicensed users makes use of the spectrum in an opportunistic manner. The secondary user uses the spectrum in a manner such that the primary or the licensed user does not face interference above a threshold level of tolerance. Whenever a primary user comes back to reclaim its licensed channel, the secondary user using it needs to perform a spectrum handoff to another channel which is free of primary user. Our primary focus is on performing spectrum handoff decision making. The SU selects staying or changing policy based on the average extended delivery time. The spectrum handoff decision making is performed, i.e., the optimal channel for spectrum handoff is decided only if changing policy is adopted which is based on multiple attribute decision making (MADM). This spectrum handoff decision making scheme is later extended using artificial neural networks (ANN) and probabilistic Markov model.