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Modeling and Analysis of Stochastic Clustered Networks

Azimi Abarghouyi, Mohammad | 2020

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 53468 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nasiri Kenari, Masoumeh
  7. Abstract:
  8. Wireless communication networks are undergoing a significant transformation from coverage-driven deployment of macrocells to user-centric and cluster-based capacity-driven deployments of low-power base stations (BSs), usually at the locations of high user density. On the other hand, the increasing irregularity and randomness in the locations of nodes has led to an increased interest in the use of random spatial models along with tools from stochastic geometry and point process theory for their accurate modeling and tractable analysis. To this end, this thesis investigates clustered networks for different wireless applications. In this regards, comprehensive and tractable analytic frameworks for stochastic geometry modeling and performance analyses of these networks are proposed and key design rules are provided. First, we develop fundamentals of single-cluster networks where a number of transmitters are confined in a finite geographical region. In this regards, we define finite homogeneous Poisson point processes (FHPPPs) to model the number and locations of the transmitters in the region. Accordingly, we study the coverage probability for a reference receiver for two strategies; closest-selection, where the receiver is served by the closest transmitter among all transmitters, and completely-random-selection, where the serving transmitter is selected randomly with uniform distribution. We also propose tight bounds on the coverage probability for both selection strategies. Then, as a generalization suitable for higher frequencies, we investigate millimeter wave (mmWave) single-cluster networks. Considering the unique features of mmWave communications such as directional transmit and receive beamforming and having different channels for line-of-sight (LOS) and non-line-of-sight (NLOS) links, we employ a selection strategy to serve a reference receiver by the transmitter providing the minimum pathloss among all transmitters. Accordingly, we study the coverage probability and the ergodic rate for the reference receiver. Also, we propose upper and lower bounds on the coverage probability which are tight at small and large beamwidths, respectively. Next, we develop fundamentals of multi-cluster networks consisting of different single-cluster networks. For the transmitters, we use the Matern cluster process (MCP) as a clustered generalization of FHPPP. For the receivers, we consider two types, i) closed-access receivers, which are located around the cluster centers of the transmitters with a symmetric normal distribution and are allowed to be served only by the transmitters of their corresponding clusters according to the closest- or completely-random-selection strategy, and ii) open-access receivers, which can be served by all transmitters according to the closest-selection strategy. We derive exact expressions for the coverage probability in the cases with different transmitter selection strategies and types of receivers. Then, as an application of multi-cluster networks and using cloud architectures, we investigate the modeling, analysis, and optimization of wireless cloud caching networks comprised of multiple-antenna radio units (RUs) inside clouds with coordinated multi-point transmissions and guard zones. We consider MCP to model RUs, and the probabilistic content placement to cache files in RUs. Accordingly, we study the hit probability for a reference user for two strategies; closest selection, where the user is served by the closest RU that has its requested file, and best power selection, where the serving RU having the requested file provides the maximum instantaneous received power at the user. Also, we approximate the hit probabilities for both the closest and the best power selections in such a way that the related objective functions for the content caching design of the network can lead to tractable concave and popularity-aware optimization problems. Solving the optimization problems, we propose algorithms to efficiently find their optimal content placements
  9. Keywords:
  10. Random Networks ; Clusterd Network ; Stochastic Geometry ; Point Process ; Caching ; Millimeter Wave Frequency ; Coverage Probability ; Hit Porbability ; Cloud Networks

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