17,235 research outputs found
Near Optimal Broadcast with Network Coding in Large Sensor Networks
We study efficient broadcasting for wireless sensor networks, with network
coding. We address this issue for homogeneous sensor networks in the plane. Our
results are based on a simple principle (IREN/IRON), which sets the same rate
on most of the nodes (wireless links) of the network. With this rate selection,
we give a value of the maximum achievable broadcast rate of the source: our
central result is a proof of the value of the min-cut for such networks, viewed
as hypergraphs. Our metric for efficiency is the number of transmissions
necessary to transmit one packet from the source to every destination: we show
that IREN/IRON achieves near optimality for large networks; that is,
asymptotically, nearly every transmission brings new information from the
source to the receiver. As a consequence, network coding asymptotically
outperforms any scheme that does not use network coding.Comment: Dans First International Workshop on Information Theory for Sensor
Netwoks (WITS 2007) (2007
Heuristics for Network Coding in Wireless Networks
Multicast is a central challenge for emerging multi-hop wireless
architectures such as wireless mesh networks, because of its substantial cost
in terms of bandwidth. In this report, we study one specific case of multicast:
broadcasting, sending data from one source to all nodes, in a multi-hop
wireless network. The broadcast we focus on is based on network coding, a
promising avenue for reducing cost; previous work of ours showed that the
performance of network coding with simple heuristics is asymptotically optimal:
each transmission is beneficial to nearly every receiver. This is for
homogenous and large networks of the plan. But for small, sparse or for
inhomogeneous networks, some additional heuristics are required. This report
proposes such additional new heuristics (for selecting rates) for broadcasting
with network coding. Our heuristics are intended to use only simple local
topology information. We detail the logic of the heuristics, and with
experimental results, we illustrate the behavior of the heuristics, and
demonstrate their excellent performance
Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs
We study a general framework for broadcast gossip algorithms which use
companion variables to solve the average consensus problem. Each node maintains
an initial state and a companion variable. Iterative updates are performed
asynchronously whereby one random node broadcasts its current state and
companion variable and all other nodes receiving the broadcast update their
state and companion variable. We provide conditions under which this scheme is
guaranteed to converge to a consensus solution, where all nodes have the same
limiting values, on any strongly connected directed graph. Under stronger
conditions, which are reasonable when the underlying communication graph is
undirected, we guarantee that the consensus value is equal to the average, both
in expectation and in the mean-squared sense. Our analysis uses tools from
non-negative matrix theory and perturbation theory. The perturbation results
rely on a parameter being sufficiently small. We characterize the allowable
upper bound as well as the optimal setting for the perturbation parameter as a
function of the network topology, and this allows us to characterize the
worst-case rate of convergence. Simulations illustrate that, in comparison to
existing broadcast gossip algorithms, the approaches proposed in this paper
have the advantage that they simultaneously can be guaranteed to converge to
the average consensus and they converge in a small number of broadcasts.Comment: 30 pages, submitte
Disjoint LDPC Coding for Gaussian Broadcast Channels
Low-density parity-check (LDPC) codes have been used for communication over a
two-user Gaussian broadcast channel. It has been shown in the literature that
the optimal decoding of such system requires joint decoding of both user
messages at each user. Also, a joint code design procedure should be performed.
We propose a method which uses a novel labeling strategy and is based on the
idea behind the bit-interleaved coded modulation. This method does not require
joint decoding and/or joint code optimization. Thus, it reduces the overall
complexity of near-capacity coding in broadcast channels. For different rate
pairs on the boundary of the capacity region, pairs of LDPC codes are designed
to demonstrate the success of this technique.Comment: 5 pages, 1 figure, 3 tables, To appear in Proc. IEEE International
Symposium on Information Theory (ISIT 2009), Seoul, Korea, June-July 200
The Capacity of Smartphone Peer-To-Peer Networks
We study three capacity problems in the mobile telephone model, a network abstraction that models the peer-to-peer communication capabilities implemented in most commodity smartphone operating systems. The capacity of a network expresses how much sustained throughput can be maintained for a set of communication demands, and is therefore a fundamental bound on the usefulness of a network. Because of this importance, wireless network capacity has been active area of research for the last two decades.
The three capacity problems that we study differ in the structure of the communication demands. The first problem is pairwise capacity, where the demands are (source, destination) pairs. Pairwise capacity is one of the most classical definitions, as it was analyzed in the seminal paper of Gupta and Kumar on wireless network capacity. The second problem we study is broadcast capacity, in which a single source must deliver packets to all other nodes in the network. Finally, we turn our attention to all-to-all capacity, in which all nodes must deliver packets to all other nodes. In all three of these problems we characterize the optimal achievable throughput for any given network, and design algorithms which asymptotically match this performance. We also study these problems in networks generated randomly by a process introduced by Gupta and Kumar, and fully characterize their achievable throughput.
Interestingly, the techniques that we develop for all-to-all capacity also allow us to design a one-shot gossip algorithm that runs within a polylogarithmic factor of optimal in every graph. This largely resolves an open question from previous work on the one-shot gossip problem in this model
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