31,381 research outputs found
Robust Transport over Networks
We consider transportation over a strongly connected, directed graph.
The scheduling amounts to selecting transition probabilities for a discrete-time Markov evolution which is designed to be consistent with initial and final marginal constraints on mass transport. We address the situation where initially the mass is concentrated on certain nodes and needs to be transported over a certain time period to another set of nodes, possibly disjoint from the first. The evolution is selected to be closest to a {\em prior} measure on paths in the relative entropy sense--such a construction is known as a Schroedinger bridge between the two given marginals. It may be viewed as an atypical stochastic control problem where the control consists in suitably modifying the prior transition mechanism. The prior can be chosen to incorporate constraints and costs for traversing specific edges of the graph, but it can also be selected to allocate equal probability to all paths of equal length connecting any two nodes (i.e., a uniform distribution on paths). This latter choice for prior transitions relies on the so-called Ruelle-Bowen random walker and gives rise to scheduling that tends to utilize all paths as uniformly as the topology allows. Thus, this Ruelle-Bowen law () taken as prior, leads to a transportation plan that tends to lessen congestion and ensures a level of robustness. We also show that the distribution on paths, which attains the maximum entropy rate for the random walker given by the topological entropy, can itself be obtained as the time-homogeneous solution of a maximum entropy problem for measures on paths (also a Schr\"{o}dinger bridge problem, albeit with prior that is not a probability measure). Finally we show that the paradigm of Schr\"odinger bridges as a mechanism for scheduling transport on networks can be adapted to graphs that are not strongly connected, as well as to weighted graphs. In the latter case, our approach may be used to design a transportation plan which effectively compromises between robustness and other criteria such as cost. Indeed, we explicitly provide a robust transportation plan which assigns {\em maximum probability} to {\em minimum cost paths} and therefore compares favorably with Optimal Mass Transportation strategies
Optimization of Free Space Optical Wireless Network for Cellular Backhauling
With densification of nodes in cellular networks, free space optic (FSO)
connections are becoming an appealing low cost and high rate alternative to
copper and fiber as the backhaul solution for wireless communication systems.
To ensure a reliable cellular backhaul, provisions for redundant, disjoint
paths between the nodes must be made in the design phase. This paper aims at
finding a cost-effective solution to upgrade the cellular backhaul with
pre-deployed optical fibers using FSO links and mirror components. Since the
quality of the FSO links depends on several factors, such as transmission
distance, power, and weather conditions, we adopt an elaborate formulation to
calculate link reliability. We present a novel integer linear programming model
to approach optimal FSO backhaul design, guaranteeing -disjoint paths
connecting each node pair. Next, we derive a column generation method to a
path-oriented mathematical formulation. Applying the method in a sequential
manner enables high computational scalability. We use realistic scenarios to
demonstrate our approaches efficiently provide optimal or near-optimal
solutions, and thereby allow for accurately dealing with the trade-off between
cost and reliability
Improved Approximation Algorithms for Computing k Disjoint Paths Subject to Two Constraints
For a given graph with positive integral cost and delay on edges,
distinct vertices and , cost bound and delay bound , the bi-constraint path (BCP) problem is to compute disjoint
-paths subject to and . This problem is known NP-hard, even when
\cite{garey1979computers}. This paper first gives a simple approximation
algorithm with factor-, i.e. the algorithm computes a solution with
delay and cost bounded by and respectively. Later, a novel improved
approximation algorithm with ratio
is developed by constructing
interesting auxiliary graphs and employing the cycle cancellation method. As a
consequence, we can obtain a factor- approximation algorithm by
setting and a factor- algorithm by
setting . Besides, by setting , an
approximation algorithm with ratio , i.e. an algorithm with
only a single factor ratio on cost, can be immediately obtained. To
the best of our knowledge, this is the first non-trivial approximation
algorithm for the BCP problem that strictly obeys the delay constraint.Comment: 12 page
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