10,776 research outputs found
Network Code Design for Orthogonal Two-hop Network with Broadcasting Relay: A Joint Source-Channel-Network Coding Approach
This paper addresses network code design for robust transmission of sources
over an orthogonal two-hop wireless network with a broadcasting relay. The
network consists of multiple sources and destinations in which each
destination, benefiting the relay signal, intends to decode a subset of the
sources. Two special instances of this network are orthogonal broadcast relay
channel and the orthogonal multiple access relay channel. The focus is on
complexity constrained scenarios, e.g., for wireless sensor networks, where
channel coding is practically imperfect. Taking a source-channel and network
coding approach, we design the network code (mapping) at the relay such that
the average reconstruction distortion at the destinations is minimized. To this
end, by decomposing the distortion into its components, an efficient design
algorithm is proposed. The resulting network code is nonlinear and
substantially outperforms the best performing linear network code. A motivating
formulation of a family of structured nonlinear network codes is also
presented. Numerical results and comparison with linear network coding at the
relay and the corresponding distortion-power bound demonstrate the
effectiveness of the proposed schemes and a promising research direction.Comment: 27 pages, 9 figures, Submited to IEEE Transaction on Communicatio
Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks
A model, called the linear transform network (LTN), is proposed to analyze
the compression and estimation of correlated signals transmitted over directed
acyclic graphs (DAGs). An LTN is a DAG network with multiple source and
receiver nodes. Source nodes transmit subspace projections of random correlated
signals by applying reduced-dimension linear transforms. The subspace
projections are linearly processed by multiple relays and routed to intended
receivers. Each receiver applies a linear estimator to approximate a subset of
the sources with minimum mean squared error (MSE) distortion. The model is
extended to include noisy networks with power constraints on transmitters. A
key task is to compute all local compression matrices and linear estimators in
the network to minimize end-to-end distortion. The non-convex problem is solved
iteratively within an optimization framework using constrained quadratic
programs (QPs). The proposed algorithm recovers as special cases the regular
and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the
distortion region of multi-source, multi-receiver networks are given for linear
coding based on convex relaxations. Cut-set lower bounds are also given for any
coding strategy based on information theory. The distortion region and
compression-estimation tradeoffs are illustrated for different communication
demands (e.g. multiple unicast), and graph structures.Comment: 33 pages, 7 figures, To appear in IEEE Transactions on Signal
Processin
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path
choice behavior of network users. This problem has been extensively studied in
transportation science, where it is known as the route choice problem. In this
literature, individuals' choice of paths are typically predicted using discrete
choice models. This article is a tutorial on a specific category of discrete
choice models called recursive, and it makes three main contributions: First,
for the purpose of assisting future research on route choice, we provide a
comprehensive background on the problem, linking it to different fields
including inverse optimization and inverse reinforcement learning. Second, we
formally introduce the problem and the recursive modeling idea along with an
overview of existing models, their properties and applications. Third, we
extensively analyze illustrative examples from different angles so that a
novice reader can gain intuition on the problem and the advantages provided by
recursive models in comparison to path-based ones
Optimal experiment design in a filtering context with application to sampled network data
We examine the problem of optimal design in the context of filtering multiple
random walks. Specifically, we define the steady state E-optimal design
criterion and show that the underlying optimization problem leads to a second
order cone program. The developed methodology is applied to tracking network
flow volumes using sampled data, where the design variable corresponds to
controlling the sampling rate. The optimal design is numerically compared to a
myopic and a naive strategy. Finally, we relate our work to the general problem
of steady state optimal design for state space models.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS283 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Using Channel Output Feedback to Increase Throughput in Hybrid-ARQ
Hybrid-ARQ protocols have become common in many packet transmission systems
due to their incorporation in various standards. Hybrid-ARQ combines the normal
automatic repeat request (ARQ) method with error correction codes to increase
reliability and throughput. In this paper, we look at improving upon this
performance using feedback information from the receiver, in particular, using
a powerful forward error correction (FEC) code in conjunction with a proposed
linear feedback code for the Rayleigh block fading channels. The new hybrid-ARQ
scheme is initially developed for full received packet feedback in a
point-to-point link. It is then extended to various different multiple-antenna
scenarios (MISO/MIMO) with varying amounts of packet feedback information.
Simulations illustrate gains in throughput.Comment: 30 page
Estimating Dynamic Traffic Matrices by using Viable Routing Changes
Abstract: In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbon
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