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In this paper, we investigate the aggregate throughput and proportional fairness of two independent secondary user (SU) groups in a cognitive radio (CR) network. Using a continuous time Markov chain, the expression for the aggregate throughput as a function of traffic intensity, forced termination and blocking probabilities is derived. Furthermore, throughput loss of the individual SU groups is analyzed...
For original paper see X. Zhu et. al., IEEE Commun. Lett., vol.11, pp.304-6, (2007).This paper discusses a number of errors in the Markov model and analysis of the blocking and forced termination probabilities in a cognitive radio network, given in [4]. The correct expressions of forced termination and blocking probabilities are provided. Under the same conditions as in [4], the probabilities and...
The goal of this communication is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional maximum likelihood (ML) methods in which classification is carried out based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, our proposed method utilizes information from all classes...
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