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What is Long Time Opportunity (LTO)

Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management
A long idle period of primary user when it doesn’t use any spectrum.
Published in Chapter:
Nonparametric Bayesian Prediction of Primary Users' Air Traffics in Cognitive Radio Networks
Ju Bin Song (Kyung Hee University, South Korea) and Zhu Han (University of Houston, USA)
DOI: 10.4018/978-1-4666-6571-2.ch029
Abstract
In cognitive radio networks a secondary user needs to estimate the primary users' air traffic patterns so as to optimize its transmission strategy. In this chapter, the authors describe a nonparametric Bayesian method for identifying traffic applications, since the traffic applications have their own distinctive air traffic patterns. In the proposed algorithm, the collapsed Gibbs sampler is applied to cluster the air traffic applications using the infinite Gaussian mixture model over the feature space of the packet length, the packet inter-arrival time, and the variance of packet lengths. The authors analyze the effectiveness of their proposed technique by extensive simulation using the measured data obtained from the WiMax networks.
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