3,362 research outputs found
Stronger wireless signals appear more Poisson
Keeler, Ross and Xia (2016) recently derived approximation and convergence
results, which imply that the point process formed from the signal strengths
received by an observer in a wireless network under a general statistical
propagation model can be modelled by an inhomogeneous Poisson point process on
the positive real line. The basic requirement for the results to apply is that
there must be a large number of transmitters with different locations and
random propagation effects.The aim of this note is to apply some of the main
results of Keeler, Ross and Xia (2016) in a less general but more easily
applicable form to illustrate how the results can be applied in practice. New
results are derived that show that it is the strongest signals, after being
weakened by random propagation effects, that behave like a Poisson process,
which supports recent experimental work.Comment: 7 pages with 1.5 line spacin
Stronger wireless signals appear more Poisson
Keeler, Ross and Xia [1] recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modelled by an inhomogeneous Poisson point process on the positive real line. The basic requirement for the results to apply is that there must be a large number of transmitters with different locations and random propagation effects. The aim of this note is to apply some of the main results of [1] in a less general but more easily applicable form to illustrate how the results can be applied in practice. New results are derived that show that it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work. [1] P. Keeler, N. Ross, and A. Xia:``When do wireless network signals appear Poisson?? '
Stronger wireless signals appear more Poisson
Keeler, Ross and Xia [1] recently derived approximation and convergence
results, which imply that the point process formed from the signal strengths
received by an observer in a wireless network under a general statistical
propagation model can be modelled by an inhomogeneous Poisson point process
on the positive real line. The basic requirement for the results to apply is
that there must be a large number of transmitters with different locations
and random propagation effects. The aim of this note is to apply some of the
main results of [1] in a less general but more easily applicable form to
illustrate how the results can be applied in practice. New results are
derived that show that it is the strongest signals, after being weakened by
random propagation effects, that behave like a Poisson process, which
supports recent experimental work
Performance analysis of wireless LANs: an integrated packet/flow level approach
In this paper we present an integrated packet/flow level modelling approach for analysing flow throughputs and transfer times in IEEE 802.11 WLANs. The packet level model captures the statistical characteristics of the transmission of individual packets at the MAC layer, while the flow level model takes into account the system dynamics due to the initiation and completion of data flow transfers. The latter model is a processor sharing type of queueing model reflecting the IEEE 802.11 MAC design principle of distributing the transmission capacity fairly among the active flows. The resulting integrated packet/flow level model is analytically tractable and yields a simple approximation for the throughput and flow transfer time. Extensive simulations show that the approximation is very accurate for a wide range of parameter settings. In addition, the simulation study confirms the attractive property following from our approximation that the expected flow transfer delay is insensitive to the flow size distribution (apart from its mean)
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
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