A Model Based Poisson Point Process for Downlink Cellular Networks Using Joint Scheduling
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This paper proposes a model based on a random cellular network to analyse performance of Joint Scheduling in which a typical user measures signal-to-interference-plus-noise ratio (SINR) on different resource blocks from K nearest BSs in order to find out the BS with the highest SINR to establish communication. The paper derives the general form of average coverage probability of a typical user in the case of \(K>2\) and its close-form expression in the case of \(K=2\). The analytical results which are verified by Monte Carlo simulation indicates that (1) using the Joint Scheduling can improve the user’s performance up to \(34.88 \%\) in the case of the path loss exponent \(\alpha = 3\); (2) the effect of the density of BSs on the user association probability is infinitesimal.
KeywordsPoisson point process Joint scheduling Coverage probability
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