PROJECT TITLE :

Accurate and Efficient Algorithms for Cognitive Radio Modeling Applications Under the i.n.i.d. Paradigm

ABSTRACT:

Given that the statistical analysis of the frequency band of interest are out there, it has been shown that adaptive looking for white areas could improve by seventypercent compared with random searching. In this paper, we tend to characterize and provide a statistical estimation of the amount of channels offered for opportunistic usage by secondary users (SUs) in a very spectrum band. But, following the independent but not identically distributed (i.n.i.d.) paradigm, predicting the exact distribution of the count of available channels isn't solely computationally intense due to the number of mixtures however infeasible in observe, further, even for frequency bands with a moderate variety of channels. Existing analysis has resorted to approximations that lacked accuracy and efficiency. To resolve this problem, we have a tendency to propose 3 novel strategies primarily based on convolution, recursive, and hybrid convolution–recursive strategies that can efficiently compute the precise distribution in frequency bands with a large range of channels. We assess their potency by analyzing every algorithm's time complexity and running time and then any comparing their performance against existing models within the literature. Moreover, knowing the provision of the channel's immediate neighbors can permit economical power management and prioritize channel allocation to SUs. Therefore, we tend to categorize offered channels into three different varieties based mostly on the occupancy of its two adjacent channels and then model their availability. Additionally, from a network performance analysis perspective, predicting the count of obtainable channels should be evaluated against the probability of detecting these channels at intervals the same i.n.i.d. framework. Respectively, we tend to propose a novel approach to calculate the probability of detecting multiple channels simultaneously. Finally, we tend to validate the effectiveness of the proposed models using several real-time measurements and more present two associated applications whe- e one options novel a pair of-D (time and frequency) availability prediction.


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