60,936 research outputs found
A Piecewise Deterministic Markov Toy Model for Traffic/Maintenance and Associated Hamilton-Jacobi Integrodifferential Systems on Networks
We study optimal control problems in infinite horizon when the dynamics
belong to a specific class of piecewise deterministic Markov processes
constrained to star-shaped networks (inspired by traffic models). We adapt the
results in [H. M. Soner. Optimal control with state-space constraint. II. SIAM
J. Control Optim., 24(6):1110.1122, 1986] to prove the regularity of the value
function and the dynamic programming principle. Extending the networks and
Krylov's ''shaking the coefficients'' method, we prove that the value function
can be seen as the solution to a linearized optimization problem set on a
convenient set of probability measures. The approach relies entirely on
viscosity arguments. As a by-product, the dual formulation guarantees that the
value function is the pointwise supremum over regular subsolutions of the
associated Hamilton-Jacobi integrodifferential system. This ensures that the
value function satisfies Perron's preconization for the (unique) candidate to
viscosity solution. Finally, we prove that the same kind of linearization can
be obtained by combining linearization for classical (unconstrained) problems
and cost penalization. The latter method works for very general near-viable
systems (possibly without further controllability) and discontinuous costs.Comment: accepted to Applied Mathematics and Optimization (01/10/2015
Informatics Research Institute (IRIS) September 2008 newsletter
2007-8 was a very busy year for IRIS. It was a bumper year for visiting Profs with Prof Michael Myers visiting from New Zealand, Prof Brian Fitzgerald visiting from University of Limerick, Ireland, Prof. Uzay Kaymak visiting from Erasmus University Netherlands and Prof Steve
Sawyer visiting from Pennsylvania State University, USA. Their visits enriched our doctoral school, seminar programme workshops and our research. We were very lucky to have such a distinguished line up of visiting professors and we offer them hearty thanks and hope to keep
ongoing research links with them
Throughput Maximization for UAV-Aided Backscatter Communication Networks
This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks
Evolution towards Smart Optical Networking: Where Artificial Intelligence (AI) meets the World of Photonics
Smart optical networks are the next evolution of programmable networking and
programmable automation of optical networks, with human-in-the-loop network
control and management. The paper discusses this evolution and the role of
Artificial Intelligence (AI)
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