Optimal channel probing in communication systems: The two-channel case

We consider a multi-channel communication system in which a transmitter has access to two channels, but does not know the state of either channel. We model the channel state using an ON/OFF Markovian model, and allow the transmitter to probe one of the channels at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must transmit over the probed channel, it has been shown that a myopic policy that probes the channel most likely to be ON is optimal. In this work, we allow the transmitter to select a channel over which to transmit that is not necessarily the one it probed. We show that in the case where the two channels are i.i.d, all probing policies yield equal reward. We extend this problem to dynamically choose when to probe based on the results of previous probes, and characterize the optimal policy, as well as provide a LP in terms of state action frequencies to find the optimal policy.National Science Foundation (U.S.) (Grant CNS-0915988)National Science Foundation (U.S.) (Grant CNS-1217048)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238

A robust optimization approach to network design

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 91-93).This thesis addresses the problem of logical topology design for optical backbone networks subject to traffic following a Gaussian distribution. The network design problem is broken into three tasks: traffic routing, capacity allocation, and link placement. The routing and capacity allocation problems are formulated as a convex mathematical program. To extend this formulation to discrete optimization problems, such as the link placement sub-problem, it is reformulated as a mixed integer linear program (MILP) by extending tools from robust optimization to Gaussian variables. Bounds are presented to relate capacity allocation to the probability of traffic overflow on a link. Lastly, the link placement subproblem is formulated as an MILP and network topologies for deterministic traffic are compared with those for stochastic traffic. Additionally, this thesis presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over-provisioning. We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing amongst backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for large-scale networks, and we present simulation results to verify our analysis on optimal backup networks.by Matthew R. Johnston.S.M

Channel probing in communication systems: Myopic policies are not always optimal

We consider a multi-channel communication system in which a transmitter has access to a large number of channels, but does not know the state of these channels. We model channel state using an ON/OFF Markovian model, and allow the transmitter to probe one of the channels at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must send over the probed channel, it has been shown that a myopic policy that probes the channel most likely to be ON is optimal. In this work, we allow the transmitter to select a channel over which to transmit that is not necessarily the one it probed. We show that the myopic policy is not optimal, and propose a simple alternative probing policy, which achieves a higher per-slot expected throughput. Finally, we consider the case where there is a fixed cost associated with probing and derive optimal probing intervals.National Science Foundation (U.S.) (Grant CNS1217048)National Science Foundation (U.S.) (CNS-0915988)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238

Channel Probing in Opportunistic Communication Systems

We consider a multi-channel communication system in which a transmitter has access to M channels, but does not know the state of any of the channels. We model the channel state using an ON/OFF Markov process, and allow the transmitter to probe a single channel at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must transmit over the probed channel, it has been shown that a myopic policy probing the channel most likely to be ON is optimal. In this paper, we allow the transmitter to select a channel over which to transmit that is potentially different from the probed channel. For a system of two channels, we show that the choice of which channel to probe does not affect the throughput. For a system with many channels, we show that a probing policy that probes the channel that is the second-most likely to be ON results in higher throughput. We extend the channel probing problem to dynamically choose when to probe based on probing history, and characterize the optimal probing policy for various scenarios

Opportunistic scheduling with limited channel state information: A rate distortion approach

We consider an opportunistic communication system in which a transmitter selects one of multiple channels over which to schedule a transmission, based on partial knowledge of the network state. We characterize a fundamental limit on the rate that channel state information must be conveyed to the transmitter in order to meet a constraint on expected throughput. This problem is modeled as a causal rate distortion optimization of a Markov source. We introduce a novel distortion metric capturing the impact of imperfect channel state information on throughput. We compute a closed-form expression for the causal information rate distortion function for the case of two channels, as well as an algorithmic upper bound on the causal rate distortion function. Finally, we characterize the gap between the causal information rate distortion and the causal entropic rate-distortion functions.National Science Foundation (U.S.) (Grant CNS-0915988)National Science Foundation (U.S.) (Grant CNS-1217048)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238)United States. Office of Naval Research (Grant N00014-12-1-0064)National Science Foundation (U.S.). Center for Science of Information (Grant CCF-09-39370

A robust optimization approach to backup network design with random failures

This paper presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over-provisioning. We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing amongst backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for largescale networks, and present simulation results that verify our analysis and approach.National Science Foundation (U.S.) (grant CNS-0626781)National Science Foundation (U.S.) (grant CNS-0830961)United States. Defense Threat Reduction Agency (grant HDTRA1-07-1-0004)United States. Defense Threat Reduction Agency (grant HDTRA-09-1-005

In vitro bacterial vaginosis biofilm community manipulation using endolysin therapy

Bacterial vaginosis (BV) affects approximately 26% of women of childbearing age globally, presenting with 3–5 times increased risk of miscarriage and two-fold risk of pre-term birth. Antibiotics (metronidazole and clindamycin) are typically employed to treat BV; however the success rate is low due to the formation of recalcitrant polymicrobial biofilms. As a novel therapeutic, promising results have been obtained in vitro using Gardnerella endolysins, although to date their efficacy has only been demonstrated against simple biofilm models. In this study, a four-species biofilm was developed consisting of Gardnerella vaginalis, Fannyhessea vaginae, Prevotella bivia and Mobiluncus curtisii. Biofilms were grown in NYC III broth and treated using antibiotics and an anti-Gardnerella endolysin (CCB7.1) for 24 h. Biofilm composition, viability and structure were assessed using colony counts, live/dead qPCR and scanning electron microscopy. All species colonised biofilms to varying degrees, with G. vaginalis being the most abundant. Biofilm composition remained largely unchanged when challenged with escalated concentrations of conventional antibiotics. A Gardnerella-targeted endolysin candidate (CCB7.1) showed efficacy against several Gardnerella species planktonically, and significantly reduced viable G. vaginalis within polymicrobial biofilms at 1 to 4X pMIC (p < 0.05 vs. vehicle control). Collectively, this study highlights the resilience of biofilm-embedded pathogens against the currently used antibiotics and provides a polymicrobial model that allows for more effective pre-clinical screening of BV therapies. The Gardnerella-specific endolysin CCB7.1 demonstrated significant activity against G. vaginalis within polymicrobial biofilms, altering the overall community dynamic and composition

Copper active sites in biology

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