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ABSTRACT: Network coding is an effective way to achieve the maximum flow of multicast networks. In this letter, we focus on the statistical properties of the maximum flow or the capacity of network coding for ad-hoc networks based on random graph models. Theoretical analysis shows that the maximum flow can be modelled as extreme order statistics of Gaussian distribution for both wired and wireless ad-hoc networks as the node number is relatively large under a certain condition. We also investigate the effects of the nodes' covering capabilities on the capacity of network coding.
IEEE Transactions on Wireless Communications 01/2008; · 2.59 Impact Factor
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IEEE Transactions on Wireless Communications. 01/2007; 6:4193-4198.
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ABSTRACT: Network coding is an essential way to achieve the maximum flow of multicast networks. Recent results have shown that random graph theory has a heuristic role in guiding the construction of network coding. In this paper, we study the distribution of maximum flow in different random graph models. We show that in a n nodes network with sampling probability p the mean of any s-t maximum flow is about (n-1)p-√((n-1)pq/π). In a n nodes random geometric graph, a model for wireless network, we can get similar results as long as the parameter has corresponding value to the one in random graph model. We also show that the changing of connectivity radius would affect the maximum flow significantly.
Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2005. MAPE 2005. IEEE International Symposium on; 09/2005
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http://sunzi.lib.hku.hk/hkuto/record/B44205041.
