In this paper, sensitivity of the first three standardized moments of both unencumbered call interruption time and cell dwell time on teletraffic performance metrics of wireless cellular networks is investigated. Mathematical analysis for obtaining system-level performance metrics is developed considering that cell dwell time and unencumbered call interruption time are phase-type distributed random variables. Numerical results allow us to quantify the extent by which system performance depends on first three standardized moments of both cell dwell time and unencumbered interruption time.
In this paper, a teletraffic analysis method based on channel holding time statistics for new and handoff calls for system level performance evaluation of mobile cellular networks with link unreliability is developed. Firstly, mathematical expressions for the probability distribution function of channel holding time for new and handoff calls are derived considering different phase-type distributions for both cell dwell time and unencumbered call interruption time due to link unreliability. Then, moments of channel holding time for new and handoff calls are derived using its moment generating function. After that, distribution of channel holding time is approximated by different phase-type distribution functions using the moment matching method. Finally, a novel teletraffic analysis formulation based on this channel holding time approximation is developed. This teletraffic model allows us to reduce computational complexity compared to the exact teletraffic model, which is based on cell dwell time and unencumbered call interruption time statistics. Important system-level performance metrics are derived using this model. Our numerical results are in good agreement with both simulation results and analytical results obtained using the exact teletraffic model.
Cell dwell time (DT) and unencumbered interruption time (IT) are fundamental time interval variables in the teletraffic analysis for the performance evaluation of mobile cellular networks. Although a diverse set of general distributions has been proposed to model these time interval variables, the effect of their moments higher than the expected value on system performance has not been reported in the literature. In this paper, sensitivity of teletraffic performance metrics of mobile cellular networks to the first three standardized moments of both DT and IT is investigated in a comprehensive manner. Mathematical analysis is developed considering that both DT and IT are phase-type distributed random variables. This work includes substantial numerical results for quantifying the dependence of system level performance metrics to the values of the first three standardized moments of both DT and IT. For instance, for a high mobility scenario where DT is modeled by a hyper-Erlang distribution, we found that call forced termination probability decreases around 60% as the coefficient of variation (CoV) and skewness of DT simultaneously change from 1 to 20 and from 60 to 2, respectively. Also, numerical results confirm that as link unreliability increases the forced termination probability increases while both new call blocking and handoff failure probabilities decrease. Numerical results also indicate that for low values of skewness, performance metrics are highly sensitive to changes in the CoV of either the IT or DT. In general, it is observed that system performance is more sensitive to the statistics of the IT than to those of the DT. Such understanding of teletraffic engineering issues is vital for planning, designing, dimensioning, and optimizing mobile cellular networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.