This paper investigates the multicast capacity of static wireless social networks. We adopt the two-layer network model, which includes the social layer and the networking layer. In the social layer, the social group size of each source node is modeled as power-law distribution. Moreover, the rank-based model is utilized to describe the relation between source and destinations in the networking layer. Based on the two-layer network model, the probability density function (PDF) of the destination positions is analyzed and verified by numerical simulation, which is different from the traditional ad hoc networks. According to the PDF, the bound of the network capacity is derived, and we propose a Euclidean minimum-spanning-tree-based transmission scheme, which is proved to achieve the order of capacity bound for most cases. Finally, the capacity of social networks is compared to the traditional multicast ad hoc networks, which indicates that the capacity scaling performs better in social networks than traditional ones. To our best knowledge, this is the first work of analyzing the impact on the capacity of social relation and group size in multicast ad hoc networks for the rank-based model.

Comparing with the unicast technology, multiple flows from the same source in multicast scenario can be aggregated even if their destinations are different. This paper evaluates such distinction by the multicast gain on per-node capacity and delay, which are defined as the per-node capacity and delay ratios between multi-unicast and multicast ( m destinations for each multicast session). Particularly, the restricted mobility model is proposed, which is a representative mobility model characterizing a class of mobility models with different average moving speeds. The theoretical analysis of this model indicates that the mobility significantly decreases the multicast gain on per-node capacity and delay, though the per-node capacity of both unicast and multicast can be enhanced by mobility. This finding suggests that mobility weakens the distinction between multicast and unicast. Finally, a general framework of multicast study is constituted by analyzing the upper-bound ( Θ(m)), the lower-bound ( Θ(1)) and the main determinants of the multicast gain on both per-node capacity and delay regardless of mobility model.

In this paper, we study the throughput and delay scaling laws for cognitive radio network with static primary users and heterogeneous mobile secondary users coexist in the unit planar region for both without base-station case and with base-station case. The primary network consists of $n$ randomly and uniformly distributed static primary users with higher priority to access the spectrum. The secondary network consists of $m=(h+1)n^{1+\epsilon}$ heterogeneous mobile secondary users with $h=O(\log n)$ We use standard asymptotic notations in our paper. Consider two nonnegative function $f(\cdot)$ and $g(\cdot)$ : (1) $f(n)=o(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/\break g(n)= 0$ . (2) $f(n)=O(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n) . (3) $f(n)=\omega(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n)=\infty$ . (4) $f(n)=\Omega(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n) > 0$ . (5) $f(n)=\Theta(g(n))$ means $f(n)=O(g(n))$ and $g(n)=O(f(n))$ . (6) $f(n)=\mathtilde{\Theta}(g(n))$ means that $f(n)=\Theta(g(n))$ when poly-logarithmic factors are ignored.

This paper investigates throughput and delay based on a traffic pattern, called convergecast, where each of the n nodes in the network acts as a destination with k randomly chosen sources corresponding to it. Adopting Multiple-Input-Multiple-Output (MIMO) technology, we devise two many-to-one cooperative schemes under convergecast for both static and mobile ad hoc networks (MANETs), respectively. We call them Convergimo Schemes. In static networks, our Convergimo scheme highly utilizes hierarchical cooperation MIMO transmission. This feature overcomes the bottleneck which hinders convergecast traffic from yielding ideal performance in traditional ad hoc network, by turning the originally interfering signals into interference-resistant ones. It helps to achieve an aggregate throughput up to Ω(n1-ϵ) for any ϵ >;0. In the mobile ad hoc case, our Convergimo scheme characterizes on joint transmission from multiple nodes to multiple receivers. With optimal network division where the number of nodes per cell is constantly bounded, the achievable per-node throughput can reach Θ(1) with the corresponding delay reduced to Θ(k). The gain comes from the strong and intelligent cooperation between nodes in our scheme, along with the maximum number of concurrent active cells and the shortest waiting time before transmission for each node within a cell. This increases the chances for each destination to receive the data it needs with minimum overhead on extra transmission. Moreover, our converge-based analysis well unifies and generalizes previous work since the results derived from convergecast in our schemes can also cover other traffic patterns. Last but not the least, our schemes are of interest not only from a theoretical perspective but also provide useful theoretical guidelines to future design of MIMO schemes in wireless networks.

This paper investigates throughput and delay based on a newly predominant traffic pattern, called converge-cast, where each of the n nodes in the network act as a destination with k randomly chosen sources corresponding to it. Adopting Multiple-Input-Multiple-Output (MIMO) technology, we devise two many-to-one cooperative schemes under converge-cast for both static and mobile ad hoc networks (MANETs), respectively. In a static network, our scheme highly utilizes hierarchical cooperation MIMO transmission. This feature overcomes the bottleneck which hinders converge-cast traffic from yielding ideal performance in traditional ad hoc network, by turning the originally interfering signals into interference-resistant ones. It helps to achieve an aggregate throughput up to Ω(n1-ε) for any ε >; 0. In the mobile ad hoc case, our scheme characterizes on joint transmission from multiple nodes to multiple receivers. With optimal network division where the number of nodes per cell is constant bounded, the achievable per-node throughput can reach Θ(1) with the corresponding delay reduced to Θ(k). The gain comes from the strong and intelligent cooperation between nodes in our scheme, along with the maximum number of concurrent active cells and the shortest waiting time before transmission for each node within a cell. This, to a great extent, increases the chances for each destination to receive the data it needs with minimum overhead on extra transmission. Moreover, our converge-based analysis well unifies and generalizes previous work since the results derived from converge-cast in our schemes can also cover other traffic patterns. Last but not least, our cooperative schemes are of interest not only from a theoretical perspective but also shed light on future design of MIMO schemes in wireless networks.