Stochastic Geometry Analysis of LTE-A Cellular Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Geometry Analysis of LTE-A Cellular Networks write by Peng Guan. This book was released on 2015. Stochastic Geometry Analysis of LTE-A Cellular Networks available in PDF, EPUB and Kindle. The main focus of this thesis is on performance analysis and system optimization of Long Term Evolution - Advanced (LTE-A) cellular networks by using stochastic geometry. Mathematical analysis of cellular networks is a long-lasting difficult problem. Modeling the network elements as points in a Poisson Point Process (PPP) has been proven to be a tractable yet accurate approach to the performance analysis in cellular networks, by leveraging the powerful mathematical tools such as stochastic geometry. In particular, relying on the PPP-based abstraction model, this thesis develops the mathematical frameworks to the computations of important performance measures such as error probability, coverage probability and average rate in several application scenarios in both uplink and downlink of LTE-A cellular networks, for example, multi-antenna transmissions, heterogeneous deployments, uplink power control schemes, etc. The mathematical frameworks developed in this thesis are general enough and the accuracy has been validated against extensive Monte Carlo simulations. Insights on performance trends and system optimization can be done by directly evaluating the formulas to avoid the time-consuming numerical simulations.
Stochastic Geometry Analysis of Cellular Networks
Stochastic Geometry Analysis of Cellular Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Geometry Analysis of Cellular Networks write by Bartłomiej Błaszczyszyn. This book was released on 2018-04-19. Stochastic Geometry Analysis of Cellular Networks available in PDF, EPUB and Kindle. Achieve faster and more efficient network design and optimization with this comprehensive guide. Some of the most prominent researchers in the field explain the very latest analytic techniques and results from stochastic geometry for modelling the signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks. This book will help readers to understand the effects of combining different system deployment parameters on key performance indicators such as coverage and capacity, enabling the efficient allocation of simulation resources. In addition to covering results for network models based on the Poisson point process, this book presents recent results for when non-Poisson base station configurations appear Poisson, due to random propagation effects such as fading and shadowing, as well as non-Poisson models for base station configurations, with a focus on determinantal point processes and tractable approximation methods. Theoretical results are illustrated with practical Long-Term Evolution (LTE) applications and compared with real-world deployment results.
Stochastic Geometry Analysis of Cellular Networks
Stochastic Geometry Analysis of Cellular Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Geometry Analysis of Cellular Networks write by Bartłomiej Błaszczyszyn. This book was released on 2018-04-19. Stochastic Geometry Analysis of Cellular Networks available in PDF, EPUB and Kindle. Achieve faster and more efficient network design and optimization with this comprehensive guide. Some of the most prominent researchers in the field explain the very latest analytic techniques and results from stochastic geometry for modelling the signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks. This book will help readers to understand the effects of combining different system deployment parameters on key performance indicators such as coverage and capacity, enabling the efficient allocation of simulation resources. In addition to covering results for network models based on the Poisson point process, this book presents recent results for when non-Poisson base station configurations appear Poisson, due to random propagation effects such as fading and shadowing, as well as non-Poisson models for base station configurations, with a focus on determinantal point processes and tractable approximation methods. Theoretical results are illustrated with practical Long-Term Evolution (LTE) applications and compared with real-world deployment results.
Stochastic Geometry and Wireless Networks
Stochastic Geometry and Wireless Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Geometry and Wireless Networks write by François Baccelli. This book was released on 2009. Stochastic Geometry and Wireless Networks available in PDF, EPUB and Kindle. This volume bears on wireless network modeling and performance analysis. The aim is to show how stochastic geometry can be used in a more or less systematic way to analyze the phenomena that arise in this context. It first focuses on medium access control mechanisms used in ad hoc networks and in cellular networks. It then discusses the use of stochastic geometry for the quantitative analysis of routing algorithms in mobile ad hoc networks. The appendix also contains a concise summary of wireless communication principles and of the network architectures considered in the two volumes.
Stochastic Geometry Analysis of Multiple Access, Mobility, and Learning in Cellular Networks
Stochastic Geometry Analysis of Multiple Access, Mobility, and Learning in Cellular Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Geometry Analysis of Multiple Access, Mobility, and Learning in Cellular Networks write by Mohammad Salehi. This book was released on 2021. Stochastic Geometry Analysis of Multiple Access, Mobility, and Learning in Cellular Networks available in PDF, EPUB and Kindle. Use cases of future wireless networks (e.g. fifth-generation [5G] networks and beyond [B5G]) will have service-quality requirements including higher data rates than today's networks for enhanced mobile broadband (eMBB), minimal latency and high network availability for ultra-reliability low-latency connection (URLLC), and massive access support for machine-type communications (mMTC). Also, 5G and B5G are expected to support communications for highly mobile scenarios with applications in new vertical sectors such as unmanned aerial vehicle (UAV) and autonomous car. Therefore, 5G and B5G cellular systems require a set of new technology enablers and solutions. In this thesis, we address some of the challenges of future wireless networks. In particular, we develop novel analytical models as well as methods, which will enable us to obtain insights into the performance of large-scale cellular networks and optimize network parameters. Non-orthogonal multiple access (NOMA) is a promising multiple access technique that enables massive connectivity and reduces the delay. We develop an analytical framework to derive the distribution of transmission success probabilities, meta distribution, for uplink and downlink NOMA. We also investigate the accuracy of distance-based ranking, instead of instantaneous signal power-based ranking, in the successive interference cancellation (SIC) at the NOMA receiver. Sojourn time, the time duration that a mobile user stays within a cell, is a mobility-aware parameter that can significantly impact the performance of mobile users and it can also be exploited to improve resource allocation and mobility management methods in the network. We derive the distribution and mean of the sojourn time in multi-tier cellular networks. Future wireless networks will exploit data-driven machine learning techniques for improving network management as well as service provisioning. Due to privacy and communication issues, learning at a centralized location (for example, at a base station) by collecting data from the mobile devices may not be always feasible. Federated learning is a machine learning setting where the centralized location trains a learning model using remote devices. Federated learning algorithms cannot be employed in real-world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. We propose a federated learning algorithm that is suitable for wireless networks.
