24,387 research outputs found
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression
Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a
system's dynamics that is built from data. We seek to reduce the order of this
model by identifying a reduced set of modes that best fit the output. We adopt
a model selection algorithm from statistics and machine learning known as Least
Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS
to select DMD modes. We refer to the resulting algorithm as Least Angle
Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting
Dynamic Mode Decomposition (DMDSP), a popular mode-selection algorithm, serves
as a benchmark for comparison. Numerical results from a Poiseuille flow test
problem show that LARS4DMD yields reduced-order models that have comparable
performance to DMDSP. LARS4DMD has the added benefit that the regularization
weighting parameter required for DMDSP is not needed.Comment: 14 pages, 2 Figures, Submitted to AIAA Aviation Conference 201
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