43,760 research outputs found
Joint Coding and Scheduling Optimization in Wireless Systems with Varying Delay Sensitivities
Throughput and per-packet delay can present strong trade-offs that are
important in the cases of delay sensitive applications.We investigate such
trade-offs using a random linear network coding scheme for one or more
receivers in single hop wireless packet erasure broadcast channels. We capture
the delay sensitivities across different types of network applications using a
class of delay metrics based on the norms of packet arrival times. With these
delay metrics, we establish a unified framework to characterize the rate and
delay requirements of applications and optimize system parameters. In the
single receiver case, we demonstrate the trade-off between average packet
delay, which we view as the inverse of throughput, and maximum ordered
inter-arrival delay for various system parameters. For a single broadcast
channel with multiple receivers having different delay constraints and feedback
delays, we jointly optimize the coding parameters and time-division scheduling
parameters at the transmitters. We formulate the optimization problem as a
Generalized Geometric Program (GGP). This approach allows the transmitters to
adjust adaptively the coding and scheduling parameters for efficient allocation
of network resources under varying delay constraints. In the case where the
receivers are served by multiple non-interfering wireless broadcast channels,
the same optimization problem is formulated as a Signomial Program, which is
NP-hard in general. We provide approximation methods using successive
formulation of geometric programs and show the convergence of approximations.Comment: 9 pages, 10 figure
Nonlinear Integer Programming
Research efforts of the past fifty years have led to a development of linear
integer programming as a mature discipline of mathematical optimization. Such a
level of maturity has not been reached when one considers nonlinear systems
subject to integrality requirements for the variables. This chapter is
dedicated to this topic.
The primary goal is a study of a simple version of general nonlinear integer
problems, where all constraints are still linear. Our focus is on the
computational complexity of the problem, which varies significantly with the
type of nonlinear objective function in combination with the underlying
combinatorial structure. Numerous boundary cases of complexity emerge, which
sometimes surprisingly lead even to polynomial time algorithms.
We also cover recent successful approaches for more general classes of
problems. Though no positive theoretical efficiency results are available, nor
are they likely to ever be available, these seem to be the currently most
successful and interesting approaches for solving practical problems.
It is our belief that the study of algorithms motivated by theoretical
considerations and those motivated by our desire to solve practical instances
should and do inform one another. So it is with this viewpoint that we present
the subject, and it is in this direction that we hope to spark further
research.Comment: 57 pages. To appear in: M. J\"unger, T. Liebling, D. Naddef, G.
Nemhauser, W. Pulleyblank, G. Reinelt, G. Rinaldi, and L. Wolsey (eds.), 50
Years of Integer Programming 1958--2008: The Early Years and State-of-the-Art
Surveys, Springer-Verlag, 2009, ISBN 354068274
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