28,846 research outputs found
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
Optimal Nested Test Plan for Combinatorial Quantitative Group Testing
We consider the quantitative group testing problem where the objective is to
identify defective items in a given population based on results of tests
performed on subsets of the population. Under the quantitative group testing
model, the result of each test reveals the number of defective items in the
tested group. The minimum number of tests achievable by nested test plans was
established by Aigner and Schughart in 1985 within a minimax framework. The
optimal nested test plan offering this performance, however, was not obtained.
In this work, we establish the optimal nested test plan in closed form. This
optimal nested test plan is also order optimal among all test plans as the
population size approaches infinity. Using heavy-hitter detection as a case
study, we show via simulation examples orders of magnitude improvement of the
group testing approach over two prevailing sampling-based approaches in
detection accuracy and counter consumption. Other applications include anomaly
detection and wideband spectrum sensing in cognitive radio systems
Distinct counting with a self-learning bitmap
Counting the number of distinct elements (cardinality) in a dataset is a
fundamental problem in database management. In recent years, due to many of its
modern applications, there has been significant interest to address the
distinct counting problem in a data stream setting, where each incoming data
can be seen only once and cannot be stored for long periods of time. Many
probabilistic approaches based on either sampling or sketching have been
proposed in the computer science literature, that only require limited
computing and memory resources. However, the performances of these methods are
not scale-invariant, in the sense that their relative root mean square
estimation errors (RRMSE) depend on the unknown cardinalities. This is not
desirable in many applications where cardinalities can be very dynamic or
inhomogeneous and many cardinalities need to be estimated. In this paper, we
develop a novel approach, called self-learning bitmap (S-bitmap) that is
scale-invariant for cardinalities in a specified range. S-bitmap uses a binary
vector whose entries are updated from 0 to 1 by an adaptive sampling process
for inferring the unknown cardinality, where the sampling rates are reduced
sequentially as more and more entries change from 0 to 1. We prove rigorously
that the S-bitmap estimate is not only unbiased but scale-invariant. We
demonstrate that to achieve a small RRMSE value of or less, our
approach requires significantly less memory and consumes similar or less
operations than state-of-the-art methods for many common practice cardinality
scales. Both simulation and experimental studies are reported.Comment: Journal of the American Statistical Association (accepted
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