173,926 research outputs found
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
Elements of Cellular Blind Interference Alignment --- Aligned Frequency Reuse, Wireless Index Coding and Interference Diversity
We explore degrees of freedom (DoF) characterizations of partially connected
wireless networks, especially cellular networks, with no channel state
information at the transmitters. Specifically, we introduce three fundamental
elements --- aligned frequency reuse, wireless index coding and interference
diversity --- through a series of examples, focusing first on infinite regular
arrays, then on finite clusters with arbitrary connectivity and message sets,
and finally on heterogeneous settings with asymmetric multiple antenna
configurations. Aligned frequency reuse refers to the optimality of orthogonal
resource allocations in many cases, but according to unconventional reuse
patterns that are guided by interference alignment principles. Wireless index
coding highlights both the intimate connection between the index coding problem
and cellular blind interference alignment, as well as the added complexity
inherent to wireless settings. Interference diversity refers to the observation
that in a wireless network each receiver experiences a different set of
interferers, and depending on the actions of its own set of interferers, the
interference-free signal space at each receiver fluctuates differently from
other receivers, creating opportunities for robust applications of blind
interference alignment principles
Parameterizable Byzantine Broadcast in Loosely Connected Networks
We consider the problem of reliably broadcasting information in a multihop
asynchronous network, despite the presence of Byzantine failures: some nodes
are malicious and behave arbitrarly. We focus on non-cryptographic solutions.
Most existing approaches give conditions for perfect reliable broadcast (all
correct nodes deliver the good information), but require a highly connected
network. A probabilistic approach was recently proposed for loosely connected
networks: the Byzantine failures are randomly distributed, and the correct
nodes deliver the good information with high probability. A first solution
require the nodes to initially know their position on the network, which may be
difficult or impossible in self-organizing or dynamic networks. A second
solution relaxed this hypothesis but has much weaker Byzantine tolerance
guarantees. In this paper, we propose a parameterizable broadcast protocol that
does not require nodes to have any knowledge about the network. We give a
deterministic technique to compute a set of nodes that always deliver authentic
information, for a given set of Byzantine failures. Then, we use this technique
to experimentally evaluate our protocol, and show that it significantely
outperforms previous solutions with the same hypotheses. Important disclaimer:
these results have NOT yet been published in an international conference or
journal. This is just a technical report presenting intermediary and incomplete
results. A generalized version of these results may be under submission
Optimal Decentralized State-Feedback Control with Sparsity and Delays
This work presents the solution to a class of decentralized linear quadratic
state-feedback control problems, in which the plant and controller must satisfy
the same combination of delay and sparsity constraints. Using a novel
decomposition of the noise history, the control problem is split into
independent subproblems that are solved using dynamic programming. The approach
presented herein both unifies and generalizes many existing results
Quantum Control Landscapes
Numerous lines of experimental, numerical and analytical evidence indicate
that it is surprisingly easy to locate optimal controls steering quantum
dynamical systems to desired objectives. This has enabled the control of
complex quantum systems despite the expense of solving the Schrodinger equation
in simulations and the complicating effects of environmental decoherence in the
laboratory. Recent work indicates that this simplicity originates in universal
properties of the solution sets to quantum control problems that are
fundamentally different from their classical counterparts. Here, we review
studies that aim to systematically characterize these properties, enabling the
classification of quantum control mechanisms and the design of globally
efficient quantum control algorithms.Comment: 45 pages, 15 figures; International Reviews in Physical Chemistry,
Vol. 26, Iss. 4, pp. 671-735 (2007
Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies
We study the problem of multi-robot target assignment to minimize the total
distance traveled by the robots until they all reach an equal number of static
targets. In the first half of the paper, we present a necessary and sufficient
condition under which true distance optimality can be achieved for robots with
limited communication and target-sensing ranges. Moreover, we provide an
explicit, non-asymptotic formula for computing the number of robots needed to
achieve distance optimality in terms of the robots' communication and
target-sensing ranges with arbitrary guaranteed probabilities. The same bounds
are also shown to be asymptotically tight.
In the second half of the paper, we present suboptimal strategies for use
when the number of robots cannot be chosen freely. Assuming first that all
targets are known to all robots, we employ a hierarchical communication model
in which robots communicate only with other robots in the same partitioned
region. This hierarchical communication model leads to constant approximations
of true distance-optimal solutions under mild assumptions. We then revisit the
limited communication and sensing models. By combining simple rendezvous-based
strategies with a hierarchical communication model, we obtain decentralized
hierarchical strategies that achieve constant approximation ratios with respect
to true distance optimality. Results of simulation show that the approximation
ratio is as low as 1.4
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