Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Subject: search.filters.subject.automatic differentiation

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    Performance Metric Sensitivity Computation for Optimization and Trade-off Analysis in Wireless Networks
    (2008-02-26) Baras, John S.; Tabatabaee, Vahid; Papageorgiou, George; Rentz, Nicolas
    We develop and evaluate a new method for estimating and optimizing various performance metrics for multi-hop wireless networks, including MANETs. We introduce an approximate (throughput) loss model that couples the physical, MAC and routing layers effects. The model provides quantitative statistical relations between the loss parameters that are used to characterize multiuser interference and physical path conditions on the one hand and the traffic rates between origin-destination pairs on the other. The model takes into account effects of the hidden nodes, scheduling algorithms, IEEE 802.11 MAC and PHY layer transmission failures and finite packet transmission retries at the MAC layer in arbitrary network topologies where multiple paths share nodes. We apply Automatic Differentiation (AD) to these implicit performance models, and develop a methodology for sensitivity analysis, parameter optimization and trade-off analysis for key wireless protocols. Finally, we provide simulation experiments to evaluate the effectiveness and performance estimation accuracy of the proposed models and methodologies.
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    Automatic Differentiation for Iterative Process and Its Applications in Network Performance Analysis
    (1997) Liu, Mingyan D.; Baras, John S.; ISR; CSHCN
    In this paper we focus on the application of automatic differentiation (AD)technique on iterative processes. We review some of the results on theconvergence of general iterative processes and the convergence of thederivative code of such iterative processes. We are especially interestedin a class of fixed point iteration problems and we extended some of thoseresults to discuss this class of problems. Finally we apply an AD packageADIC to a network performance evaluation problem for numerical experimentsto get sensitivities of network blocking probabilities w.r.t. networkoffered traffic load.