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In this paper, we study the throughput and delay in wireless cognitive social networks. Specifically, we consider a common scenario for cognitive radio networks (CRNs) where the primary and secondary networks operate at the same time and space and share the spectrum. On this basis, we integrate a social relationship into the CRN where each source node selects its destination upon a rank-based model, which captures the social characteristic well. By applying a cellular time-division multiple-access scheduling scheme, we first characterize the distinct traffic pattern caused by the social relationships between nodes. Then, we derive the achievable throughput and delay for both primary and secondary networks under the new network setting. In addition, we also study the cognitive social networks with infrastructure where $l=o$ $(n)$ base stations are regularly deployed within the primary network. Given a probabilistic routing strategy, throughput of the proposed network is recalculated. Particularly, due to the social relationships between nodes, we reveal that a larger $l$ is required if we expect a significant capacity gain within the primary network compared with previous works. [ABSTRACT FROM PUBLISHER]

The medium access probability (MAP) of a random access protocol can severely impact network throughput especially for delay-sensitive applications, since it determines whether a node should transmit packets in a given slot or not. This paper focuses on network throughput maximization through optimizing the MAPs of all users under delay quality-of-service constraints in wireless interference-limited networks. Specifically, first, the total delay for transmitting one packet for a user is analyzed and derived based on an M/G/1 model. Then, the stochastic property of the aggregated interference is analyzed and its distribution is modeled as a log-normal distribution. Based on the delay and interference models, an optimization problem is formulated to derive the optimal MAPs so that the network throughput is maximized under the delay constraints. Two network traffic scenarios, homogeneous and heterogeneous user traffic, are discussed, respectively. For the case of homogeneous traffic, a closed-form expression of the optimal MAP is derived; for the case of heterogeneous traffic, a global $\epsilon $ -optimal algorithm based on the branch-and-bound framework and convex relaxation technology is proposed with relatively low complexity. Simulations results show that the proposed algorithms can achieve superior network throughput performance over existing schemes. [ABSTRACT FROM PUBLISHER]

The widespread of smart phones has brought new social-aware features to wireless networks. Users prefer to forward traffic to their social contacts in social-aware networks, which is highly different from the traditional uniform traffic pattern. To this end, we analyze a social-aware wireless network, which is modeled by the social contacts between nodes, and explore its impact on network throughput capacity. We present that it can refine social contacts, thus improving the network capacity. Moreover, directional antennas are applied to further enhance the capacity performance, which can reduce the interference brought by other simultaneous communications. We derive the throughput capacity for social-aware networks with single-beam and multi-beam directional antenna, respectively. For single-beam directional antenna, we prove that when wireless networks are dominated by traffics of short-distance social contact, the throughput capacity can be promoted from order of (\Theta (1/\theta ^2n\,\text log\,n)^1/2) to \Theta (1/\theta ^2 \text log\,n) . In addition, when the beamwidth \theta is at order of \Omega ({1}/{({\log {n}})^{1/2}}) , the throughput capacity is a constant. Thus, the wireless network is scalable. For multi-beam antennas, we prove that when sidelobe gain G_{s} is at order of o(Gm) , where Gm is main lobe gain, compared with the single-beam case, the network capacity can achieve the order of \Theta (1/\theta ^2\text log\,n) . [ABSTRACT FROM AUTHOR]