570 research outputs found

    Dynamic bandwidth allocation using infinitesimal perturbation analysis

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    Advances in network management and switching technologies make dynamic bandwidth allocation of logical networks built on top of a physical network possible. Previous proposed dynamic bandwidth allocation algorithms are based on simplified network model. The analytical model is valid only under restrictive assumptions. Infinitesimal Perturbation Analysis, a technique which estimates the gradients of the functions in discrete event dynamic systems by passively observing the system, is used to estimate delay sensitivities under general traffic patterns. A new dynamic bandwidth allocation algorithm using on-line sensitivity estimation is proposed. Simulation results show that the approach further improves network performance. Implementation of the proposed algorithm in operational networks is also discussed.published_or_final_versio

    Optimal Policies Search for Sensor Management : Application to the AESA Radar

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    This report introduces a new approach to solve sensor management problems. Classically sensor management problems are formalized as Partially-Observed Markov Decision Process (POMPD). Our original approach consists in deriving the optimal parameterized policy based on stochastic gradient estimation. Two differents techniques nammed Infinitesimal Approximation (IPA) and Likelihood Ratio (LR) can be used to adress such a problem. This report discusses how these methods can be used for gradient estimation in the context of sensor management . The effectiveness of this general framework is illustrated by the managing of an Active Electronically Scanned Array Radar (AESA Radar)

    Dynamic automatic differentiation of GPU broadcast kernels

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    We show how forward-mode automatic differentiation (AD) can be employed within larger reverse-mode computations to dynamically differentiate broadcast operations in a GPU-friendly manner. Our technique fully exploits the broadcast Jacobian's inherent sparsity structure, and unlike a pure reverse-mode approach, this "mixed-mode" approach does not require a backwards pass over the broadcasted operation's subgraph, obviating the need for several reverse-mode-specific programmability restrictions on user-authored broadcast operations. Most notably, this approach allows broadcast fusion in primal code despite the presence of data-dependent control flow. We discuss an experiment in which a Julia implementation of our technique outperformed pure reverse-mode TensorFlow and Julia implementations for differentiating through broadcast operations within an HM-LSTM cell update calculation

    Optimal Policies Search for Sensor Management

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    International audienceThis paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as Partially-Observed Markov Decision Processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line (in simulation ) using models of the environement and of the sensor(s). The learned policy can then be used to manage the sensor(s). In order to approximate the gradient in a stochastic context, we introduce a new method to approximate the gradient, based on Infinitesimal Perturbation Approximation (IPA). The effectiveness of this general framework is illustrated by the managing of an Electronically Scanned Array Radar. First simulations results are finally proposed

    Simplicial Homology for Future Cellular Networks

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    Simplicial homology is a tool that provides a mathematical way to compute the connectivity and the coverage of a cellular network without any node location information. In this article, we use simplicial homology in order to not only compute the topology of a cellular network, but also to discover the clusters of nodes still with no location information. We propose three algorithms for the management of future cellular networks. The first one is a frequency auto-planning algorithm for the self-configuration of future cellular networks. It aims at minimizing the number of planned frequencies while maximizing the usage of each one. Then, our energy conservation algorithm falls into the self-optimization feature of future cellular networks. It optimizes the energy consumption of the cellular network during off-peak hours while taking into account both coverage and user traffic. Finally, we present and discuss the performance of a disaster recovery algorithm using determinantal point processes to patch coverage holes

    Optimal Policies Search for Sensor Management

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    International audienceThis paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as Partially-Observed Markov Decision Processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line (in simulation ) using models of the environement and of the sensor(s). The learned policy can then be used to manage the sensor(s). In order to approximate the gradient in a stochastic context, we introduce a new method to approximate the gradient, based on Infinitesimal Perturbation Approximation (IPA). The effectiveness of this general framework is illustrated by the managing of an Electronically Scanned Array Radar. First simulations results are finally proposed

    Reference Chaser Bandwidth Controller for Wireless QoS Mapping under Delay Constraints

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    Telecommunications networks are composed of functional layers acting in cascade. Quality of Service (QoS) derives from the action of each layer that must assure a specific level of quality to the upper layer in terms of performance parameters (e.g., loss, delay, jitter of the packets). Appropriate algorithms are needed to compute the bandwidth necessary so to assure the requested QoS when information is transferred from one layer to the next one below. This paper proposes a scheme that adapts the bandwidth to be allocated to a buffer which conveys heterogeneous traffic (both concerning traffic sources and QoS requirements) in a layer-in-cascade model. The proposal is focused on delay constraints. The proposed algorithm is based only on measures and does not use closed-form expressions, a priori information about traffic statistical properties, and assumptions about buffer dimension. Simulation results show the reliability of the approach in comparison with other techniques at the state of the art, thus corroborating the application of the algorithm for a large set of operative situations, including fading conditions
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