343,693 research outputs found
Topological reversibility and causality in feed-forward networks
Systems whose organization displays causal asymmetry constraints, from
evolutionary trees to river basins or transport networks, can be often
described in terms of directed paths (causal flows) on a discrete state space.
Such a set of paths defines a feed-forward, acyclic network. A key problem
associated with these systems involves characterizing their intrinsic degree of
path reversibility: given an end node in the graph, what is the uncertainty of
recovering the process backwards until the origin? Here we propose a novel
concept, \textit{topological reversibility}, which rigorously weigths such
uncertainty in path dependency quantified as the minimum amount of information
required to successfully revert a causal path. Within the proposed framework we
also analytically characterize limit cases for both topologically reversible
and maximally entropic structures. The relevance of these measures within the
context of evolutionary dynamics is highlighted.Comment: 9 pages, 3 figure
Distributed Robust Set-Invariance for Interconnected Linear Systems
We introduce a class of distributed control policies for networks of
discrete-time linear systems with polytopic additive disturbances. The
objective is to restrict the network-level state and controls to user-specified
polyhedral sets for all times. This problem arises in many safety-critical
applications. We consider two problems. First, given a communication graph
characterizing the structure of the information flow in the network, we find
the optimal distributed control policy by solving a single linear program.
Second, we find the sparsest communication graph required for the existence of
a distributed invariance-inducing control policy. Illustrative examples,
including one on platooning, are presented.Comment: 8 Pages. Submitted to American Control Conference (ACC), 201
Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail
While the notion of age of information (AoI) has recently emerged as an
important concept for analyzing ultra-reliable low-latency communications
(URLLC), the majority of the existing works have focused on the average AoI
measure. However, an average AoI based design falls short in properly
characterizing the performance of URLLC systems as it cannot account for
extreme events that occur with very low probabilities. In contrast, in this
paper, the main objective is to go beyond the traditional notion of average AoI
by characterizing and optimizing a URLLC system while capturing the AoI tail
distribution. In particular, the problem of vehicles' power minimization while
ensuring stringent latency and reliability constraints in terms of
probabilistic AoI is studied. To this end, a novel and efficient mapping
between both AoI and queue length distributions is proposed. Subsequently,
extreme value theory (EVT) and Lyapunov optimization techniques are adopted to
formulate and solve the problem. Simulation results shows a nearly two-fold
improvement in terms of shortening the tail of the AoI distribution compared to
a baseline whose design is based on the maximum queue length among vehicles,
when the number of vehicular user equipment (VUE) pairs is 80. The results also
show that this performance gain increases significantly as the number of VUE
pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure
Rate-Distortion Function of the Stochastic Block Model
The stochastic block model (SBM) is extensively used to model networks in
which users belong to certain communities. In recent years, the study of
information-theoretic compression of such networks has gained attention, with
works primarily focusing on lossless compression. In this work, we address the
lossy compression of SBM graphs by characterizing the rate-distortion function
under a Hamming distortion constraint. Specifically, we derive the conditional
rate-distortion function of the SBM with community membership as side
information. We approach this problem as the classical Wyner-Ziv lossy problem
by minimising mutual information of the graph and its reconstruction
conditioned on community labels. Lastly, we also derive the rate-distortion
function of the Erd\H{o}s-R\'enyi (ER) random graph model.Comment: 9 pages, 1 figure, Accepted for presentation at Asilomar Conference
on Signals, Systems, and Computers, 202
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