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Of late, wireless type technologies stride rampantly pervading every aspect in business as well as societal circles, constantly spiraling the requirement of wider bandwidth accessibility in overwhelming loading in traffic. The predominant strategy for dealing with this co-existence of various wireless type technologies in the space of RF is to allot spectrum for access in a static way. Since the spectrum is as of now set aside, the latest wireless technologies, experience difficulty in working in the bands which are not licensed. They encounter interfering in other services. Such circumstance is commonly named as the scarcity of spectrum, alluding to the inaccessibility of any valuable bands of the spectrum which may get allocated. In any case, investigations of the spectrum scarcity issue by different administrative bodies far and wide, including the Federal Communication Commission (FCC) demonstrate it as the artifact of the spectrum management policy. Furthermore, such findings demonstrate the underutilization of the spectrum being allocated. Truth be told, as indicated by the FCC, the materialistic and geographical varieties while using spectrum assigned extend between 15% up to 85%. In this architecture, licensed users are commonly referred to as Primary Users (PU) and the opportunistic users are normally labeled as Secondary Users (SU). The FCC orders that the licensed spectrum can be accessed by SUs only if it is not in use by PUs. Fundamental to meeting this control is the ability of the SUs in perceiving the part of the spectrum that is idle.
Wasim, Shanidul, Debarati, Srimanta (2015), proposed a method to analyze the spectrum handoff by developing a probability and time relationship between duration of the call by user and ‘spectrum holes of residual type’s time which are defined by ‘random kind of process put forth by Poisson’. They used different distribution models for the analysis. But they never considered the selection of an optimal channel for the handoff process to happen. So, the authors proposed a method for optimal selection. Krishan, Arun and Rajeev (2017) selected an optimal channel using MADM methods like SAW (Alireza &Mohammad, 2014), TOPSIS (Zadeh, Mohaghar & Bazargani, 2013), GRA (Juchi, 2008) and Cost function (Ahmed, Nidal & Hossam, 2006; Kumar, Prakash & Tripathi, 2017). They simply considered the available channels and implemented their process. But the authors used a Markov model to know their current states like if the channels are busy or idle. After implementing the Markov model the authors used the MADM technique to select the best channel out of the idle channels which are known from the Markov model results. Till now analysis process is done only w.r.t stationary user. So, the authors tried to work w.r.t Non-stationary user. When the authors searched the papers for the previous work done in the same area, the authors found the paper (Achille & Alessandra, 2005) in which probability and time relationship between user’s call duration and residual time of spectrum holes are developed in the home cell and the neighboring cell. Achille & Alessandra (2005) discuss the spectrum handoff probability in the exponential model with departure rate (β). So, the authors tried to analyze the Spectrum handoff process under various distribution models such as Lognormal and Erlang-3. The contribution in this paper includes the use of Markov model in identifying the channel. To identify the best channel the following methods are used: SAW, TOPSIS and GRA. In addition, TOPSIS/GRA (HYBRID) method is utilized. Also, the following distribution models are used: Exponential type, Erlang-M and Lognormal for the handoff process.