We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resource...
We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which degrades performance isolation. The analysis of this tradeoff enables network operators to determine the effect of performance isolation on the operating cost of each NS. To study the tradeoff, we solve an optimization problem where we find the multiplexing policy that requires the least provisioned resources to honor the Service Level Agreements (SLAs) of all NSs. The SLA of each NS $i$ states that its resource demand should be met for $P_{i}^{H}$ fraction of time, and for $P_{i}^{L}\leq P_{i}^{H}$ fraction of time, it should be met regardless of the demands of other NSs. For resource demands that follow ergodic Markov chains, we show that the well-known Max-Weight scheduler is an optimal multiplexing policy. Since the Max-Weight scheduler does not require any knowledge of the statistics of the resource demands, we also propose its use in non-markovian settings. For resource demands obtained in the LTE module of ns-3, we show that the Max-Weight scheduler reduces the provisioned bandwidth by 36.2% when no performance isolation is required. Lastly, for these non-markovian resource demands, the Max-Weight scheduler maintains its optimality since it requires as much provisioned bandwidth as the best non-causal scheduler.
Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention, both from industry and academia, over the past several years. Beam placement is among the major resource allocation problems...
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Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention, both from industry and academia, over the past several years. Beam placement is among the major resource allocation problems in multi-beam NGSO systems. In this paper, we formulate the beam placement problem as a Euclidean disk cover optimization model. We aim at minimizing the number of placed beams while satisfying the total downlink traffic demand of targeted ground terminals without exceeding the capacity of the placed beams. We present a low-complexity deterministic annealing (DA)-based algorithm to solve the NP-hard optimization model for near-optimal solutions. We further propose an extended variant of the previous model to ensure the traffic assigned to the beams is balanced. We verify the effectiveness of our proposed methods by means of numerical experiments and show that our scheme is superior to the state-of-the-art methods in that it covers the ground users by fewer number of beams on average.
In this paper, we develop a distributionally robust model predictive control framework for the control of wind farms with the goal of power tracking and mechanical stress reduction of the individual wind turbines. We ...
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We show that a periodic multi-grated-gate structure can be applied to THz plasmonic FETs (TeraFETs) to improve the THz detection sensitivity. The introduction of spatial nonuniformity by separated gate sections create...
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Shared information is a measure of mutual dependence among m≥2 jointly distributed discrete random variables. A new undirected probabilistic graphical model, a cliqueylon graph, is introduced, with potential applicat...
Shared information is a measure of mutual dependence among m≥2 jointly distributed discrete random variables. A new undirected probabilistic graphical model, a cliqueylon graph, is introduced, with potential applications in leader-follower swarms and neuron clusters with correlations of varying strength. Shared information is characterized explicitly for the cliqueylon, relying on structural properties of an underlying optimization. Implications for the data compression problem of omniscience are highlighted.
Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why an...
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Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.
In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents' nonlinear models can be approximated by integrator systems, by their feed...
In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents' nonlinear models can be approximated by integrator systems, by their feedback-linearized equivalents, or by dynamics composed of deterministic linear and nonlinear terms. The resulting approaches associated with such assumptions, however, are hardly applicable to general nonlinear systems. To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems. The agents also exchange information via a leader-follower communication topology modeled as an undirected and connected graph with a single leader node. By leveraging standard tools from algebraic graph theory and Lyapunov analysis, we first derive a locally asymptotically stabilizing formation tracking law. Next, to demonstrate the effectiveness of our approach, we present results from numerical simulations of an example in robotics. These results - together with a comparison of the formation errors obtained with our approach and those realized via an optimization-based method - further validate our theoretical propositions.
Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy...
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In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what ...
In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what current robots can do. Owing to the difficulty of observing and recovering the trajectories of the agents, we focus on the dynamics of their probability densities, which are governed by partial differential equations (PDEs), namely compressible Euler equations subject to non-local forces. We formulate the inverse problem of learning interactions as a PDE-constrained optimization problem of minimizing the squared Hellinger distance between the histogram of the flock and the distribution associated to our PDEs. The numerical methods used to efficiently solve the PDE-constrained optimization problem are described. Realistic flocking data are simulated using the Boids model of flocking agents, which differs in nature from the reconstruction models used in our PDEs. Our analysis and simulated experiments show that the behavior of cohesive flocks can be recovered accurately with approximate PDE solutions.
In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max−consensus is reached in networks characterized by unreliable communication links (i.e., links suffering fr...
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