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Zhao Dong

Publications and source records attributed to Zhao Dong.

58 records · Page 4Linked to original sources
arXiv2012

An Extended Network Coding Opportunity Discovery Scheme in Wireless Networks

Network coding is known as a promising approach to improve wireless network performance. How to discover the coding opportunity in relay nodes is really important for it. There are more coding chances, there are more times it can improve network throughput by network coding operation. In this paper, an extended network coding opportunity discovery scheme (ExCODE) is proposed, which is realized by appending the current node ID and all its 1-hop neighbors' IDs to the packet. ExCODE enables the next hop relay node to know which nodes else have already overheard the packet, so it can discover the potential coding opportunities as much as possible. ExCODE expands the region of discovering coding chance to n-hops, and have more opportunities to execute network coding operation in each relay node. At last, we implement ExCODE over the AODV protocol, and efficiency of the proposed mechanism is demonstrated with NS2 simulations, compared to the existing coding opportunity discovery scheme.

cs.NI↗
arXiv2009

Phase Transition on The Degree Sequence of a Mixed Random Graph Process

This paper focuses on the problem of the degree sequence for a mixed random graph process which continuously combines the {\it classical} model and the BA model. Note that the number of step added edges for the mixed model is random and non-uniformly bounded. By developing a comparing argument, phase transition on the degree distributions of the mixed model is revealed: while the {\it pure} classical model possesses a {\it exponential} degree sequence, the {\it pure} BA model and the mixed model possess {\it power law} degree sequences. As an application of the methodology, phase transition on the degree sequence of {\it another} mixed model with {\it hard copying} is also studied, especially, in the power law region, the inverse power can take any value greater than 1.

math.PR↗
arXiv2008

On the Degree Sequence and its Critical Phenomenon of an Evolving Random Graph Process

In this paper we focus on the problem of the degree sequence for the following random graph process. At any time-step $t$, one of the following three substeps is executed: with probability $α_1$, a new vertex $x_t$ and $m$ edges incident with $x_t$ are added; or, with probability $α-α_1$, $m$ edges are added; or finally, with probability $1-\a$, $m$ random edges are deleted. Note that in any case edges are added in the manner of preferential attachment. we prove that there exists a critical point $α_c$ satisfying: 1) if $α_1<α_c$, then the model has power law degree sequence; 2) if $α_1>α_c$, then the model has exponential degree sequence; and 3) if $α_1=α_c$, then the model has a degree sequence lying between the above two cases.

math.PR↗