The characterization of the latency is essential for operation of 5G and beyond 5G cellular networks. This paper develops a spatiotemporal model to characterize latency in the downlink of large-scale cellular networks with random scheduling. In particular, the framework integrates stochastic geometry and queueing theory, to capture the interwoven interactions between the microscopic behavior of each wireless link and the macroscopic mutual interference between all links in the network. The developed framework enables a traffic-aware characterization of the transmission success probability and of the latency across the network.

Network secrecy is vital for a variety of wireless applications and can be accomplished by exploiting network interference. Recently, interference engineering strategies (IESs) have been developed to harness network interference, depending on the wireless environment (node distribution, transmission policy, and channel conditions). Typically, the node spatial distribution has been modeled according to a homogeneous Poisson point process for mathematical tractability. However, such a model can be inadequate for inhomogeneous (e.g., sensor and vehicular) networks. This paper develops a framework for the design and analysis of inhomogeneous wireless networks with intrinsic secrecy. Based on the characterization of the network interference and received signal-to-interference ratio for different receiver selection strategies. Local and global secrecy metrics are introduced for characterizing the level of intrinsic secrecy in inhomogeneous wireless networks from a link and a network perspective. The benefits of IESs are quantified by simulations in various scenarios, thus corroborating the analysis. Results show that IESs can elevate the network secrecy significantly.

The massive wireless networks (MWNs) enable surging applications for the Internet of Things and cyber physical systems. In these applications, nodes typically exhibit stringent power constraints, limited computing capabilities, and sporadic traffic patterns. This paper develops a spatiotemporal model to characterize and design uncoordinated multiple access (UMA) strategies for MWNs. By combining stochastic geometry and queueing theory, the paper quantifies the scalability of UMA via the maximum spatiotemporal traffic density that can be accommodated in the network, while satisfying the target operational constraints (e.g., stability) for a given percentile of the nodes. The developed framework is then used to design UMA strategies that stabilize the node data buffers and achieve desirable latency, buffer size, and data rate.

The impressive growth of secrecy-sensitive wireless applications calls for methods to complement traditional cryptography. In particular, physical-layer security is attractive to enhance communication confidentiality by exploiting the characteristics of the wireless environment. Recent works on information-theoretic wireless network secrecy have shown how to exploit interference to enhance the level of information confidentiality for node spatial distribution according to a homogeneous Poisson point process. We propose a framework for design and analysis of inhomogeneous Poisson networks with maximum secrecy rate. We determine the effects of the inhomogeneous node spatial distribution on the secrecy metrics, which provide guidelines for network design.