529 research outputs found
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
Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems
In this paper, we consider a queue-aware distributive resource control
algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay
buffering is an effective way to reduce the intrinsic half-duplex penalty in
cooperative systems. The complex interactions of the queues at the source node
and the relays are modeled as an average-cost infinite horizon Markov Decision
Process (MDP). The traditional approach solving this MDP problem involves
centralized control with huge complexity. To obtain a distributive and low
complexity solution, we introduce a linear structure which approximates the
value function of the associated Bellman equation by the sum of per-node value
functions. We derive a distributive two-stage two-winner auction-based control
policy which is a function of the local CSI and local QSI only. Furthermore, to
estimate the best fit approximation parameter, we propose a distributive online
stochastic learning algorithm using stochastic approximation theory. Finally,
we establish technical conditions for almost-sure convergence and show that
under heavy traffic, the proposed low complexity distributive control is global
optimal.Comment: 30 pages, 7 figure
Analyzing Linear Communication Networks using the Ribosome Flow Model
The Ribosome Flow Model (RFM) describes the unidirectional movement of
interacting particles along a one-dimensional chain of sites. As a site becomes
fuller, the effective entry rate into this site decreases. The RFM has been
used to model and analyze mRNA translation, a biological process in which
ribosomes (the particles) move along the mRNA molecule (the chain), and decode
the genetic information into proteins.
Here we propose the RFM as an analytical framework for modeling and analyzing
linear communication networks. In this context, the moving particles are
data-packets, the chain of sites is a one dimensional set of ordered buffers,
and the decreasing entry rate to a fuller buffer represents a kind of
decentralized backpressure flow control. For an RFM with homogeneous link
capacities, we provide closed-form expressions for important network metrics
including the throughput and end-to-end delay. We use these results to analyze
the hop length and the transmission probability (in a contention access mode)
that minimize the end-to-end delay in a multihop linear network, and provide
closed-form expressions for the optimal parameter values
Performance Modelling and Optimisation of Multi-hop Networks
A major challenge in the design of large-scale networks is to predict and optimise the
total time and energy consumption required to deliver a packet from a source node to a
destination node. Examples of such complex networks include wireless ad hoc and sensor
networks which need to deal with the effects of node mobility, routing inaccuracies, higher
packet loss rates, limited or time-varying effective bandwidth, energy constraints, and the
computational limitations of the nodes. They also include more reliable communication
environments, such as wired networks, that are susceptible to random failures, security
threats and malicious behaviours which compromise their quality of service (QoS) guarantees.
In such networks, packets traverse a number of hops that cannot be determined
in advance and encounter non-homogeneous network conditions that have been largely
ignored in the literature. This thesis examines analytical properties of packet travel in
large networks and investigates the implications of some packet coding techniques on both
QoS and resource utilisation.
Specifically, we use a mixed jump and diffusion model to represent packet traversal
through large networks. The model accounts for network non-homogeneity regarding
routing and the loss rate that a packet experiences as it passes successive segments of a
source to destination route. A mixed analytical-numerical method is developed to compute
the average packet travel time and the energy it consumes. The model is able to capture
the effects of increased loss rate in areas remote from the source and destination, variable
rate of advancement towards destination over the route, as well as of defending against
malicious packets within a certain distance from the destination. We then consider sending
multiple coded packets that follow independent paths to the destination node so as to
mitigate the effects of losses and routing inaccuracies. We study a homogeneous medium
and obtain the time-dependent properties of the packet’s travel process, allowing us to
compare the merits and limitations of coding, both in terms of delivery times and energy
efficiency. Finally, we propose models that can assist in the analysis and optimisation
of the performance of inter-flow network coding (NC). We analyse two queueing models
for a router that carries out NC, in addition to its standard packet routing function. The
approach is extended to the study of multiple hops, which leads to an optimisation problem
that characterises the optimal time that packets should be held back in a router, waiting
for coding opportunities to arise, so that the total packet end-to-end delay is minimised
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