Design and Performance Analysis of Next Generation Heterogeneous Cellular Networks for the Internet of Things

The Internet of Things (IoT) is a system of inter-connected computing devices, objects and mechanical and digital machines, and the communications between these devices/objects and other Internet-enabled systems. Scalable, reliable, and energy-efficient IoT connectivity will bring huge benefits to the society, especially in transportation, connected self-driving vehicles, healthcare, education, smart cities, and smart industries. Multi-tier heterogeneous cellular networks have been a key technology in the development and implementation of the IoT. In this dissertation, we study heterogeneous cellular networks that are composed of two tiers: Tier 1 consists of macro base stations (MBSs) with higher transmission power and lower deployment density. Tier 2 consists of small base stations (SBSs) with lower transmission power and higher density deployment. SBSs are also termed aggregators in the context of several IoT applications that involve heavy data traffic in the uplink direction from devices to the core network. The objective of this dissertation is to model and analyze the performance of large-scale heterogeneous two-tier IoT cellular networks, and offer design insights to maximize their performance. Using stochastic geometry, we develop realistic yet tractable models to study the performance of such networks. In particular, we propose solutions to the following research problems: • We propose a novel analytical model to estimate the mean uplink device data rate utility function under both spectrum allocation schemes, full spectrum reuse (FSR) and orthogonal spectrum partition (OSP), for uplink two-hop IoT networks. The model takes into account the aggregator spatial density, aggregator association bias and spectrum partition ratio across the MBS tier and the aggregator tier, and device and aggregator power control fractionals (PCFs). We develop constraint gradient ascent optimization algorithms to obtain the optimal aggregator association bias (for the FSR scheme) and the optimal joint spectrum partition ratio and optimal aggregator association bias (for the OSP scheme). II • We study the performance of two-tier IoT cellular networks in which one tier operates in the traditional sub-6GHz spectrum and the other, in the millimeter wave (mmwave) spectrum. In particular, we characterize the meta distributions of the downlink signal-to-interference ratio (sub-6GHz spectrum), the signal-to-noise ratio (mm-wave spectrum) and the data rate of a typical device in such a hybrid spectrum network. We characterize the conditional success probability (CSP) and its b moment for (1) a typical device when it associates with a sub-6GHz MBS (direct transmission), (2) a typical device when it associates with a mm-wave SBS (access transmission), and (3) the tagged SBS when it associates with a sub-6GHz MBS (backhaul transmission). Finally, we characterize the meta distributions of the SIR/SNR and data rate of a typical device by substituting the cumulative moment of the CSP of a user device into the Gil-Pelaez inversion theorem. • We propose to split the control plane (C-plane) and user plane (U-plane) as a potential solution to harvest densification gain in heterogeneous two-tier networks while minimizing the handover rate and network control overhead. We develop a tractable mobility-aware model for a two-tier downlink cellular network with high density small cells and a C-plane/U-plane split architecture. The developed model is then used to quantify the effect of mobility on the foreseen densification gain with and without C-plane/U-plane splitting.