Elements of information theory
Elements of information theory
Randomized algorithms
Graph Theory With Applications
Graph Theory With Applications
Network Coding for Efficient Wireless Unicast
IZS '06 Proceedings of the 2006 International Zurich Seminar on Communications
The capacity of wireless networks
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A correction to the proof of a lemma in "The capacity of wireless networks"
IEEE Transactions on Information Theory
On the multicast throughput capacity of network coding in wireless ad-hoc networks
Proceedings of the 2nd ACM international workshop on Foundations of wireless ad hoc and sensor networking and computing
Bounds on the throughput gain of network coding in unicast and multicast wireless networks
IEEE Journal on Selected Areas in Communications - Special issue on network coding for wireless communication networks
On the improvement of scaling laws for large-scale MANETs with network coding
IEEE Journal on Selected Areas in Communications - Special issue on network coding for wireless communication networks
Multicast throughput order of network coding in wireless ad-hoc networks
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Network coding does not change the multicast throughput order of wireless ad hoc networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Multicast capacity-delay tradeoff with network coding in MANETs
WASA'11 Proceedings of the 6th international conference on Wireless algorithms, systems, and applications
Optimal precoding for bi-directional MIMO transmission with network coding
WASA'11 Proceedings of the 6th international conference on Wireless algorithms, systems, and applications
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Gupta and Kumar established that the per node throughput of ad hoc networks with multipair unicast traffic scales as λ(n) = Θ(1/√nlogn), thus indicating that network performance does not scale well with an increasing number of nodes. However, the model of Gupta and Kumar did not allow for the possibility of network coding and broadcasting, and recent work has suggested that such techniques have the potential to greatly improve network throughput. Here, for multiple unicast flows in a random topology under the protocol communication model of Gupta and Kumar [1], we show that for arbitrary network coding and broadcasting in a two-dimensional random topology that the throughput scales as λ(n) = Θ(1/nr(n)), where n is the total number of nodes and r(n) is the transmission radius. When r(n) is set to ensure connectivity, λ(n) = Θ(1/√nlogn), which is of the same order as the lower bound for the throughput without network coding and broadcasting; in other words, network coding and broadcasting at most provides a constant factor improvement in the throughput. This result is also extended to one-dimensional and three-dimensional random deployment topologies, where it is shown that λ(n) = Θ(1/n) or the one-dimensional topology and λ(n) = Θ(1/3√nlog2n) for three-dimensional networks.