132,166 research outputs found
Self-adaptive congestion control for multi-class intermittent connections in a communication network
A Markovian model of the evolution of intermittent connections of various
classes in a communication network is established and investigated. Any
connection evolves in a way which depends only on its class and the state of
the network, in particular as to the route it uses among a subset of the
network nodes. It can be either active (ON) when it is transmitting data along
its route, or idle (OFF). The congestion of a given node is defined as a
functional of the transmission rates of all ON connections going through it,
and causes losses and delays to these connections. In order to control this,
the ON connections self-adaptively vary their transmission rate in TCP-like
fashion. The connections interact through this feedback loop. A Markovian model
is provided by the states (OFF, or ON with some transmission rate) of the
connections. The number of connections in each class being potentially huge, a
mean-field limit result is proved with an appropriate scaling so as to reduce
the dimensionality. In the limit, the evolution of the states of the
connections can be represented by a non-linear system of stochastic
differential equations, of dimension the number of classes. Additionally, it is
shown that the corresponding stationary distribution can be expressed by the
solution of a fixed-point equation of finite dimension
Compute-and-Forward: Harnessing Interference through Structured Codes
Interference is usually viewed as an obstacle to communication in wireless
networks. This paper proposes a new strategy, compute-and-forward, that
exploits interference to obtain significantly higher rates between users in a
network. The key idea is that relays should decode linear functions of
transmitted messages according to their observed channel coefficients rather
than ignoring the interference as noise. After decoding these linear equations,
the relays simply send them towards the destinations, which given enough
equations, can recover their desired messages. The underlying codes are based
on nested lattices whose algebraic structure ensures that integer combinations
of codewords can be decoded reliably. Encoders map messages from a finite field
to a lattice and decoders recover equations of lattice points which are then
mapped back to equations over the finite field. This scheme is applicable even
if the transmitters lack channel state information.Comment: IEEE Trans. Info Theory, to appear. 23 pages, 13 figure
Intrinsically-generated fluctuating activity in excitatory-inhibitory networks
Recurrent networks of non-linear units display a variety of dynamical regimes
depending on the structure of their synaptic connectivity. A particularly
remarkable phenomenon is the appearance of strongly fluctuating, chaotic
activity in networks of deterministic, but randomly connected rate units. How
this type of intrinsi- cally generated fluctuations appears in more realistic
networks of spiking neurons has been a long standing question. To ease the
comparison between rate and spiking networks, recent works investigated the
dynami- cal regimes of randomly-connected rate networks with segregated
excitatory and inhibitory populations, and firing rates constrained to be
positive. These works derived general dynamical mean field (DMF) equations
describing the fluctuating dynamics, but solved these equations only in the
case of purely inhibitory networks. Using a simplified excitatory-inhibitory
architecture in which DMF equations are more easily tractable, here we show
that the presence of excitation qualitatively modifies the fluctuating activity
compared to purely inhibitory networks. In presence of excitation,
intrinsically generated fluctuations induce a strong increase in mean firing
rates, a phenomenon that is much weaker in purely inhibitory networks.
Excitation moreover induces two different fluctuating regimes: for moderate
overall coupling, recurrent inhibition is sufficient to stabilize fluctuations,
for strong coupling, firing rates are stabilized solely by the upper bound
imposed on activity, even if inhibition is stronger than excitation. These
results extend to more general network architectures, and to rate networks
receiving noisy inputs mimicking spiking activity. Finally, we show that
signatures of the second dynamical regime appear in networks of
integrate-and-fire neurons
A finite strain thermo-mechanically coupled material model for semi-crystalline polymers
In this work, a thermo-mechanically coupled constitutive model for semicrystalline polymers is derived in a thermodynamically consistent manner. In general, the macroscopic material behaviour of this class of materials is dictated by the underlying microstructure, i.e. by the distribution and structure of crystalline regimes, which form up after cooling from the amorphous melt. In order to account for the latter, the total degree of crystallinity is incorporated as an internal variable and its evolution is prescribed by means of a non-isothermal crystallisation kinetics model. The numerically eïŹcient and robust framework is characterised based on experimental data for Polyamide 6 and shows a promising potential to predict the hyperelastic, visco-plastic material behaviour at various temperature
A finite strain thermo-mechanically coupled material model for semi-crystalline polymers
In this work, a thermo-mechanically coupled constitutive model for semicrystalline polymers is derived in a thermodynamically consistent manner. In general, the macroscopic material behaviour of this class of materials is dictated by the underlying microstructure, i.e. by the distribution and structure of crystalline regimes, which form up after cooling from the amorphous melt. In order to account for the latter, the total degree of crystallinity is incorporated as an internal variable and its evolution is prescribed by means of a non-isothermal crystallisation kinetics model. The numerically eïŹcient and robust framework is characterised based on experimental data for Polyamide 6 and shows a promising potential to predict the hyperelastic, visco-plastic material behaviour at various temperature
Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective
In this paper we present and analyze a queueing-theoretical model for
autonomous mobility-on-demand (MOD) systems where robotic, self-driving
vehicles transport customers within an urban environment and rebalance
themselves to ensure acceptable quality of service throughout the entire
network. We cast an autonomous MOD system within a closed Jackson network model
with passenger loss. It is shown that an optimal rebalancing algorithm
minimizing the number of (autonomously) rebalancing vehicles and keeping
vehicles availabilities balanced throughout the network can be found by solving
a linear program. The theoretical insights are used to design a robust,
real-time rebalancing algorithm, which is applied to a case study of New York
City. The case study shows that the current taxi demand in Manhattan can be met
with about 8,000 robotic vehicles (roughly 60% of the size of the current taxi
fleet). Finally, we extend our queueing-theoretical setup to include congestion
effects, and we study the impact of autonomously rebalancing vehicles on
overall congestion. Collectively, this paper provides a rigorous approach to
the problem of system-wide coordination of autonomously driving vehicles, and
provides one of the first characterizations of the sustainability benefits of
robotic transportation networks.Comment: 10 pages, To appear at RSS 201
A Statistical Physics Perspective on Web Growth
Approaches from statistical physics are applied to investigate the structure
of network models whose growth rules mimic aspects of the evolution of the
world-wide web. We first determine the degree distribution of a growing network
in which nodes are introduced one at a time and attach to an earlier node of
degree k with rate A_ksim k^gamma. Very different behaviors arise for gamma<1,
gamma=1, and gamma>1. We also analyze the degree distribution of a
heterogeneous network, the joint age-degree distribution, the correlation
between degrees of neighboring nodes, as well as global network properties. An
extension to directed networks is then presented. By tuning model parameters to
reasonable values, we obtain distinct power-law forms for the in-degree and
out-degree distributions with exponents that are in good agreement with current
data for the web. Finally, a general growth process with independent
introduction of nodes and links is investigated. This leads to independently
growing sub-networks that may coalesce with other sub-networks. General results
for both the size distribution of sub-networks and the degree distribution are
obtained.Comment: 20 pages; solicited mini-review to appear in aspecial issue of
Computer Networks and ISDN Systems; submitted to the journal April 1, 200
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