Abstract
Despite the widespread popularity of stochastic geometry analysis for cellular networks, most analytical results lack the perspective of channel-adaptive user scheduling. This study presents a stochastic geometry analysis of the SINR distribution and scheduling gain of normalized SNR-based scheduling in an uplink Poisson cellular network, in which the per-user truncated fractional transmit power control is performed. Because the effects of multi-user diversity depend on the number of candidate users to be scheduled, which is a random variable in a Poisson cellular network, the number distribution of candidate users is a major factor in analyzing the SINR distribution of user scheduling. However, the maximum transmit power constraint of users complicates the distribution of candidate users. This study provides the number distribution of candidate users in a general form, which is obtained by modeling the area of the existing range of candidate users using a beta distribution. Based on this result, this study successfully obtains the uplink SINR distribution under channel-adaptive user scheduling, including cases in which edge users are both allowed and not allowed to transmit at the maximum transmit power. Numerical evaluations reveal that the scheduling gain varies depending on the SNR and the fraction of edge users.
Highlights
T HE methodology through which the performance analysis of a wireless network is conducted has changed dramatically during the past decade because of the emergence of stochastic geometry [1]–[3]
We provide the pmf of the number of candidate users in each cell in an uplink network, by which we derive the marginalized SINR distribution under uplink user scheduling
The SINR ccdf FSINR(θ) of the normalized signal-to-noise power ratio (SNR)-based scheduling in uplink cellular networks is given by (19), shown at the bottom of this page, which is appropriate if gi(λBS, Rd) 1 (i = 1, 2). 2) General Case: we provide the pmf of n, which is always applicable regardless of λBS and Rd
Summary
T HE methodology through which the performance analysis of a wireless network is conducted has changed dramatically during the past decade because of the emergence of stochastic geometry [1]–[3]. This type of performance analysis is conventionally conducted using Monte Carlo simulations, which are often time-consuming, as when, for example, an appropriate system parameter setting is sought. Andrews et al [5] proposed a basic framework for a downlink cellular analysis and derived the SINR distribution P(SINR > θ) as a simple result, which is in good agreement with Monte Carlos simulations. The framework of downlink analysis has been extended to an uplink case [7], [8], and their extensions have appeared in a wide variety of scenarios [9]–[12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
