Yongxiang Xia

Performance of differential chaos-shift-keying digital communication systems over a multipath fading channel with delay spread

The performance of the noncoherent differential chaos-shift-keying (DCSK) communication system over a multipath fading channel with delay spread is evaluated. Analytical expressions of the bit error rates are derived under the assumption of an independent Rayleigh fading two-ray channel model. Analytical and simulated results are presented and compared. The multipath performance of the DCSK system is also compared with that of the coherent CSK system as well as conventional generic waveform communication schemes.

Threshold for the Outbreak of Cascading Failures in Degree-Degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.

Cascading failures of loads in interconnected networks under intentional attack

Cascading failures of loads in isolated networks under random failures or intentional attacks have been studied in the past decade. The corresponding results for interconnected networks remain missing. In this paper we extend the cascading failure model used in isolated networks to the case of interconnected networks, and study cascades of failures in a data-packet transport scenario. We find that for sparse coupling, enhancing the coupling probability can make interconnected networks more robust against intentional attacks, but keeping increasing the coupling probability has the opposite effect for dense coupling. Additionally, the optimal coupling probability is largely affected by the coupling preference. Finally, assortative coupling is more helpful to resist the cascades compared to disassortative or random coupling. These results can be useful for the design and optimization of interconnected networks such as communication networks, power grids and transportation systems.

Traffic congestion in interconnected complex networks

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

Link Prediction in Complex Networks: A Mutual Information Perspective

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

Robust-yet-fragile nature of interdependent networks.

Interdependent networks have been shown to be extremely vulnerable based on the percolation model. Parshani et al. [Europhys. Lett. 92, 68002 (2010)] further indicated that the more intersimilar networks are, the more robust they are to random failures. When traffic load is considered, how do the coupling patterns impact cascading failures in interdependent networks? This question has been largely unexplored until now. In this paper, we address this question by investigating the robustness of interdependent Erdös-Rényi random graphs and Barabási-Albert scale-free networks under either random failures or intentional attacks. It is found that interdependent Erdös-Rényi random graphs are robust yet fragile under either random failures or intentional attacks. Interdependent Barabási-Albert scale-free networks, however, are only robust yet fragile under random failures but fragile under intentional attacks. We further analyze the interdependent communication network and power grid and achieve similar results. These results advance our understanding of how interdependency shapes network robustness.

Analysis of telephone network traffic based on a complex user network

The traffic in telephone networks is analyzed in this paper. Unlike the classical traffic analysis where call blockings are due to the limited channel capacity, we consider here a more realistic cause for call blockings which is due to the way in which users are networked in a real-life human society. Furthermore, two kinds of user network, namely, the fully connected user network and the scale-free network, are employed to model the way in which telephone users are connected. We show that the blocking probability is generally higher in the case of the scale-free user network, and that the carried traffic intensity is practically limited not only by the network capacity but also by the property of the user network.

Dynamical interplay between epidemics and cascades in complex networks

Epidemics and cascading failure are extensively investigated. Traditionally, they are independently studied, but in practice, there are many cases where these two dynamics interact with each other and neither of their effects can be ignored. For example, consider that a digital virus is spreading in a communication network, which is transferring data in the meantime. We build a model based on the epidemiological SIR model and a local load sharing cascading failure model to study the interplay between these two dynamics. In this model, when the dynamical process stops at equilibrium, the nodes both uninfected and unfailed form several clusters. We consider the relative size of the largest one, i.e. the giant component. A phenomenon is observed in both Erd?s-R?nyi (ER) random networks and Barab?si-Albert (BA) scale-free networks that when the infection probability is over some critical value, a giant component forms only if the tolerance parameter ? is within some interval . In this interval, the size of the remained giant component first increases and then decreases. After analyzing the cause of this phenomenon, we then present in ER random networks a theoretical solution of the key values of and , which are very important when we evaluate the robustness of the network. Finally, our theory is verified by numerical simulations.

Dynamic behavior of the interaction between epidemics and cascades on heterogeneous networks

Epidemic spreading and cascading failure are two important dynamical processes on complex networks. They have been investigated separately for a long time. But in the real world, these two dynamics sometimes may interact with each other. In this paper, we explore a model combined with the SIR epidemic spreading model and a local load sharing cascading failure model. There exists a critical value of the tolerance parameter for which the epidemic with high infection probability can spread out and infect a fraction of the network in this model. When the tolerance parameter is smaller than the critical value, the cascading failure cuts off the abundance of paths and blocks the spreading of the epidemic locally. While the tolerance parameter is larger than the critical value, the epidemic spreads out and infects a fraction of the network. A method for estimating the critical value is proposed. In simulations, we verify the effectiveness of this method in the uncorrelated configuration model (UCM) scale-free networks.

Traffic congestion analysis in complex networks

The problem of traffic congestion in complex networks is studied. Two kinds of complex network structures, namely random graphs and scale-free networks, are considered. In terms of the structure of connection, random graphs are homogeneous networks whereas the scale-free networks are heterogeneous networks. For both types of networks, we introduce an additional scale-free feature in the load generation process such that a small number of nodes are more heavily loaded than others. A traffic model similar to the routing algorithm in computer networks is used in our simulation study. We show how the network structures and parameters influence the traffic congestion status