567 research outputs found
Tail asymptotics of signal-to-interference ratio distribution in spatial cellular network models
We consider a spatial stochastic model of wireless cellular networks, where
the base stations (BSs) are deployed according to a simple and stationary point
process on , . In this model, we investigate tail
asymptotics of the distribution of signal-to-interference ratio (SIR), which is
a key quantity in wireless communications. In the case where the path-loss
function representing signal attenuation is unbounded at the origin, we derive
the exact tail asymptotics of the SIR distribution under an appropriate
sufficient condition. While we show that widely-used models based on a Poisson
point process and on a determinantal point process meet the sufficient
condition, we also give a counterexample violating it. In the case of bounded
path-loss functions, we derive a logarithmically asymptotic upper bound on the
SIR tail distribution for the Poisson-based and -Ginibre-based models.
A logarithmically asymptotic lower bound with the same order as the upper bound
is also obtained for the Poisson-based model.Comment: Dedicated to Tomasz Rolski on the occasion of his 70th birthda
High-SIR Transmission Capacity of Wireless Networks with General Fading and Node Distribution
In many wireless systems, interference is the main performance-limiting
factor, and is primarily dictated by the locations of concurrent transmitters.
In many earlier works, the locations of the transmitters is often modeled as a
Poisson point process for analytical tractability. While analytically
convenient, the PPP only accurately models networks whose nodes are placed
independently and use ALOHA as the channel access protocol, which preserves the
independence. Correlations between transmitter locations in non-Poisson
networks, which model intelligent access protocols, makes the outage analysis
extremely difficult. In this paper, we take an alternative approach and focus
on an asymptotic regime where the density of interferers goes to 0. We
prove for general node distributions and fading statistics that the success
probability \p \sim 1-\gamma \eta^{\kappa} for , and
provide values of and for a number of important special
cases. We show that is lower bounded by 1 and upper bounded by a value
that depends on the path loss exponent and the fading. This new analytical
framework is then used to characterize the transmission capacity of a very
general class of networks, defined as the maximum spatial density of active
links given an outage constraint.Comment: Submitted to IEEE Trans. Info Theory special issu
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