Modeling and analysis of wireless networks with correlation and motion

Date

2019-06-13

Authors

Choi, Chang-sik

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Abstract

The use of stochastic geometry allows the analysis of the typical performance of a wireless network. Specifically, under a stationary framework, the network performance at a typical receiver represents the network performance spatially-averaged over all receivers. This approach has been applied to the Poisson point processes whose points are independently located in space. The Poisson point process expresses a total independence type randomness in network architectures. Its tractability leads to its wide use in modeling various wireless networks, e.g., cellular networks, ad hoc networks, and vehicular networks.

However, a network analysis using the Poisson point process might be inaccurate when the network components are geometrically correlated or in motion, as in heterogeneous cellular networks, or vehicular networks. For instance, macro base stations are deployed far from each other. Vehicles are located on roads, i.e., lines, and they move on the lines. As a result, the analysis of these networks can be improved by new spatial models that capture these spatial and dynamic features.

In my first contribution, I derive the signal-to-interference ratio (SIR) coverage probability of a typical user in heterogeneous cellular networks where base stations are modeled by the sum of a Poisson point process and a stationary square grid. In my second contribution, I develop a stationary framework based on the sum of a Cox point process and a Poisson point process to model random cellular networks with linear base stations and linear users on straight lines. I derive the SIR coverage probability of the typical user and characterize its association. In the third contribution, I investigate the statistical properties of the Cox point process, exploring the nearest distance distribution and the convergence of the Cox-Voronoi cell. In the above three contributions, I analyze the performance of wireless networks by focusing on their correlated structures, extracting results which cannot be obtained from models based only on Poisson point processes.

In my fourth contribution, I propose a new technology for harvesting Internet-of-Things (IoT) data based on mesh relaying with vehicles as sinks. I derive the network capacity and compare it to the traditional approach, which is based on static base stations. In the fifth contribution, I derive the SIR distribution of direct communication from roadside devices to vehicles. By characterizing the evolution of the network snapshots, I derive the behavior of vehicles' service coverage area and the network latency. In my sixth contribution, I propose a data harvesting technology for the ground-based data devices, based on the use of unmanned aerial vehicles (UAVs). I derive the total data transmitted from a typical device by characterizing the evolution of network geometry with respect to time. These last three contributions are built on a combination of network snapshot analysis and network evolution analysis.

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