Abstract
The employment of stochastic geometry for the analysis and design of ultra dense networks (UDNs) has provided significant insights into network densification. In addition to the characterization of the network performance and behavior, these tools can also be exploited toward solving complex optimization problems that could maximize the capacity benefits arising in UDNs. However, this is preconditioned on the existence of tractable closed form expressions for the considered figures of merit. In this course, the present paper introduces an accurate approximation for the moment generating function (MGF) of the aggregate other-cell interference created by base stations whose positions follow a Poisson point process of given spatial density. Given the pivotal role of the MGF of the aggregate interference in stochastic geometry and the tractability of the derived MGF, the latter can be employed to substantially simplify ensuing stochastic geometry analyses. Subsequently, the present paper employs the introduced MGF to provide closed form expressions for the downlink ergodic capacity for the interference limited case, and validates the accuracy of these expressions by the use of extensive Monte Carlo simulations. The derived expressions depend on the density of users and base stations, setting out a densification road map for network operators and designers of significant value.
Highlights
The advent of multimedia interactive services and the surge in the number of interconnected devices has imposed the investigation of new approaches able to enhance wireless capacity in 5G networks
Following a similar approach as before, we derive closed form expressions for the DL ergodic capacity which depend on the density of user equipment (UE) λUE and the density of Base station (BS) λ
6 Conclusions The present paper has demonstrated how stochastic geometry tools can be exploited to derive not just exact but cumbersome expressions, and simple, albeit extremely accurate closed form expressions that allow for the investigation of complex optimization problems
Summary
The advent of multimedia interactive services and the surge in the number of interconnected devices has imposed the investigation of new approaches able to enhance wireless capacity in 5G networks. The reason for that is that the majority of the stochastic geometry approaches in the literature, including the theoretical analyses presented above, involve intractable integrations Even though such integrals can be computed numerically, allowing the analysis of the network behavior, they cannot be employed for the investigation of complex optimization problems. In these cases, it is imperative that the considered objective functions, which evaluate the system performance, involve tractable closed form expressions. It is imperative that the considered objective functions, which evaluate the system performance, involve tractable closed form expressions In this course, it is essential to exploit the available stochastic geometry tools to develop tractable and accurate approximations in addition to the available exact but cumbersome expressions.
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