Modeling and analyzing the evolution of cellular networks using stochastic geometry
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The increasing complexity of cellular network due to its continuous evolution has made the conventional system level simulations time consuming and cost prohibitive. By modeling base station (BS) and user locations as spatial point processes, stochastic geometry has recently been recognized as a tractable and efficient analytical tool to quantify key performance metrics. The goal of this dissertation is to leverage stochastic geometry to develop an accurate spatial point process model for the conventional homogeneous macro cellular network, and to address the design and analysis challenges for the emerging cellular networks that will explore new spectrum for cellular communications. First, this dissertation proposes to use the repulsive determinantal point processes (DPPs) as an accurate model for macro BS locations in a cellular network. Based on three unique computational properties of the DPPs, the exact expressions of several fundamental performance metrics for cellular networks with DPP configured BSs are analytically derived and numerically evaluated. Using hypothesis testing for various performance metrics of interest, the DPPs are validated to be more accurate than the Poisson point process (PPP) or the deterministic grid model. Then the focus of this dissertation shifts to emerging networks that exploit new spectrum for cellular communications. One promising option is to allow the centrally scheduled cellular system to also access the unlicensed spectrum, wherein a carrier sensing multiple access with collision avoidance (CSMA/CA) protocol is usually used, as in Wi-Fi. A stochastic geometry-based analytical framework is developed to characterize the performance metrics for neighboring Wi-Fi and cellular networks under various coexistence mechanisms. In order to guarantee fair coexistence with Wi-Fi, it is shown that the cellular network needs to adopt either a discontinuous transmission pattern or its own CSMA/CA like mechanisms. Next, this dissertation considers cellular networks operating in the millimeter wave (mmWave) band, where directional beamforming is required to establish viable connections. Therefore, a major design challenge is to learn the necessary beamforming directions through the procedures that establish the initial connection between the mobile user and the network. These procedures are referred to as initial access, wherein cell search on the downlink and random access on the uplink are the two major steps. Stochastic geometry is again utilized to develop a unified analytical framework for three directional initial access protocols under a high mobility scenario where users and random blockers are moving with high speed. The expected delay for a user to succeed in initial access, and the average user-perceived downlink throughput that accounts for the initial access overhead, are derived for all three protocols. In particular, the protocol that has low beam-sweeping overhead during cell search is found to achieve a good trade-off between the initial access delay and user-perceived throughput performance. Finally, in contrast to the high mobility scenario for initial access, the directional cell search delay in a slow mobile network is analyzed. Specifically, the BS and user locations are fixed for long period of time, and therefore a strong temporal correlation for SINR is experienced. A closed-form expression for the expected cell search delay is derived, indicating that the expected cell search delay is infinite for noise-limited networks (e.g., mmWave) whenever the non-line-of-sight path loss exponent is larger than 2. By contrast, the expected cell search delay for interference-limited networks is proved to be infinite when the number of beams to search at the BS is smaller than a certain threshold, and finite otherwise.