104 research outputs found
Simple Approximations of the SIR Meta Distribution in General Cellular Networks
Compared to the standard success (coverage) probability, the meta
distribution of the signal-to-interference ratio (SIR) provides much more
fine-grained information about the network performance. We consider general
heterogeneous cellular networks (HCNs) with base station tiers modeled by
arbitrary stationary and ergodic non-Poisson point processes. The exact
analysis of non-Poisson network models is notoriously difficult, even in terms
of the standard success probability, let alone the meta distribution. Hence we
propose a simple approach to approximate the SIR meta distribution for
non-Poisson networks based on the ASAPPP ("approximate SIR analysis based on
the Poisson point process") method. We prove that the asymptotic horizontal gap
between its standard success probability and that for the Poisson point
process exactly characterizes the gap between the th moment of the
conditional success probability, as the SIR threshold goes to . The gap
allows two simple approximations of the meta distribution for general
HCNs: 1) the per-tier approximation by applying the shift to each tier
and 2) the effective gain approximation by directly shifting the meta
distribution for the homogeneous independent Poisson network. Given the
generality of the model considered and the fine-grained nature of the meta
distribution, these approximations work surprisingly well.Comment: This paper has been accepted in the IEEE Transactions on
Communications. 14 pages, 13 figure
Performance analysis of user-centric SBS deployment with load balancing in heterogeneous cellular networks : A Thomas cluster process approach
Author's accepted manuscript.Available from: 24/01/2022.acceptedVersio
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.
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. 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).
-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 (mm-wave) 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. 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 effect of mobility on the foreseen densification gain with and without C-plane/U-plane splitting
Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation
Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources.
This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated
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