5,957 research outputs found
On the Catalyzing Effect of Randomness on the Per-Flow Throughput in Wireless Networks
This paper investigates the throughput capacity of a flow crossing a
multi-hop wireless network, whose geometry is characterized by general
randomness laws including Uniform, Poisson, Heavy-Tailed distributions for both
the nodes' densities and the number of hops. The key contribution is to
demonstrate \textit{how} the \textit{per-flow throughput} depends on the
distribution of 1) the number of nodes inside hops' interference sets, 2)
the number of hops , and 3) the degree of spatial correlations. The
randomness in both 's and is advantageous, i.e., it can yield larger
scalings (as large as ) than in non-random settings. An interesting
consequence is that the per-flow capacity can exhibit the opposite behavior to
the network capacity, which was shown to suffer from a logarithmic decrease in
the presence of randomness. In turn, spatial correlations along the end-to-end
path are detrimental by a logarithmic term
Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space
In asymptotic regimes, both in time and space (network size), the derivation
of network capacity results is grossly simplified by brushing aside queueing
behavior in non-Jackson networks. This simplifying double-limit model, however,
lends itself to conservative numerical results in finite regimes. To properly
account for queueing behavior beyond a simple calculus based on average rates,
we advocate a system theoretic methodology for the capacity problem in finite
time and space regimes. This methodology also accounts for spatial correlations
arising in networks with CSMA/CA scheduling and it delivers rigorous
closed-form capacity results in terms of probability distributions. Unlike
numerous existing asymptotic results, subject to anecdotal practical concerns,
our transient one can be used in practical settings: for example, to compute
the time scales at which multi-hop routing is more advantageous than single-hop
routing
Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification?
This paper investigates two strategies to reduce the communication delay in
future wireless networks: traffic dispersion and network densification. A
hybrid scheme that combines these two strategies is also considered. The
probabilistic delay and effective capacity are used to evaluate performance.
For probabilistic delay, the violation probability of delay, i.e., the
probability that the delay exceeds a given tolerance level, is characterized in
terms of upper bounds, which are derived by applying stochastic network
calculus theory. In addition, to characterize the maximum affordable arrival
traffic for mmWave systems, the effective capacity, i.e., the service
capability with a given quality-of-service (QoS) requirement, is studied. The
derived bounds on the probabilistic delay and effective capacity are validated
through simulations. These numerical results show that, for a given average
system gain, traffic dispersion, network densification, and the hybrid scheme
exhibit different potentials to reduce the end-to-end communication delay. For
instance, traffic dispersion outperforms network densification, given high
average system gain and arrival rate, while it could be the worst option,
otherwise. Furthermore, it is revealed that, increasing the number of
independent paths and/or relay density is always beneficial, while the
performance gain is related to the arrival rate and average system gain,
jointly. Therefore, a proper transmission scheme should be selected to optimize
the delay performance, according to the given conditions on arrival traffic and
system service capability
An End-to-End Stochastic Network Calculus with Effective Bandwidth and Effective Capacity
Network calculus is an elegant theory which uses envelopes to determine the
worst-case performance bounds in a network. Statistical network calculus is the
probabilistic version of network calculus, which strives to retain the
simplicity of envelope approach from network calculus and use the arguments of
statistical multiplexing to determine probabilistic performance bounds in a
network. The tightness of the determined probabilistic bounds depends on the
efficiency of modelling stochastic properties of the arrival traffic and the
service available to the traffic at a network node. The notion of effective
bandwidth from large deviations theory is a well known statistical descriptor
of arrival traffic. Similarly, the notion of effective capacity summarizes the
time varying resource availability to the arrival traffic at a network node.
The main contribution of this paper is to establish an end-to-end stochastic
network calculus with the notions of effective bandwidth and effective capacity
which provides efficient end-to-end delay and backlog bounds that grows
linearly in the number of nodes () traversed by the arrival traffic, under
the assumption of independence.Comment: 17 page
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