Performance Analysis Of Large-scale Cognitive Social Networks

In the past decade, the asymptotic performance of wireless networks has been the important hot research topic within the network community. As the number of nodes in large-scale wireless networks tends to infinity, researchers find the performance analysis complex and difficult. As a result, lots of works focus on the homogeneous network, where the node distribution is homogeneous and the source node randomly chooses the destination. However, the real network serves mainly the independent and unique people, which may generate the social characteristics within the network. For making the theoretical analysis of wireless networks much more applicable to the real network settings, we must deeply understand and investigate the wireless networks based on the social behavior of nodes.This paper explores the asymptotic performance of large-scale cognitive social networks. First, we introduce some common models which are designed for the asymptotic analysis of wireless networks and the definition of asymptotic capacity and delay within random networks. Then, we summarize and classify the works and results which were already known. After that, we analyze the source and destination(S-D) distribution based on the social behavior of nodes and the resulting traffic locality phenomenon. Based on the S-D distribution, we investigate the capacity and delay of general cognitive networks; the cooperative transmission scheme and the impact of node mobility on the performance of cognitive social networks in detail.We distinguish our work from most previous literatures on cognitive networks. In this paper, we incorporate the social behavior of nodes, which contain the human behavior of communicating, into the network model. We utilize the mathematical instruments of random process to depict the S-D distribution, which reflects the non-randomness of the way source chooses the destination.We are the first introducing the social S-D distribution into cognitive networks, and theoretically analyze the impact of social S-D distribution on the performance of general cognitive networks. Results show that the social S-D distribution will not destroy the operating independency of either the primary or secondary network.For general cognitive networks, we propose the cooperative transmission scheme and investigate how the social S-D distribution and cooperative transmission scheme simultaneously influence the network performance. Results indicate that the combination of them can significantly improve the performance of primary users, while not degrading the performance of secondary users. In addition, the social S-D distribution could also reduce the network operation cost.Based on the cooperative transmission scheme, we further incorporate the node mobility into secondary networks, studying the impact of node mobility on the performance of the whole network. Results prove that although the node mobility of secondary users can not further enhance the capacity of primary users, the transmission delay within primary networks can be largely reduced.In all, this paper solves several key problems of cognitive social networks. It is important for understanding and designing the large-scale cognitive social network from the theoretical perspective.