Statistical Modeling And Analysis Of Interference In Wireless Networks

Recent wireless communication research faces the challenge of meeting a predicted 1000x increase in demand for wireless Internet data over the next decade. Among the key reasons for such explosive increase in demand include the evolution of Internet as a provider of high-definition video entertainment and two-way video communication, accessed via mobile wireless devices. One way to meet some of this demand is by using multiple antennae at the transmitter and receiver in a wireless device. For example, a system with 4 transmit and 4 receive antennae can provide up to a 4x increase in data throughput. Another key aspect of the overall solution would require sharing radio frequency spectral resources among users, causing severe amounts of interference to wireless systems. Consequently, wireless receivers with multiple antennae would be deployed in network environments that are rife with interference primarily due to wireless resource sharing among users. Other significant sources of interference include computational platform subsystems, signal leakage, and external electronics. Interference causes severe degradation in communication performance of wireless receivers. Having accurate statistical models of interference is a key requirement to designing, and analyzing the communication performance of, multi-antenna wireless receivers in the presence of interference. Prior work on statistical modeling of interference in multi-antenna receivers utilizes either the Gaussian distribution, or non-Gaussian distributions exhibiting either statistical independence or spherical isotropy. This dissertation proposes a framework, based on underlying statistical-physical mechanism of interference generation and propagation, for modeling multi-antenna interference in various network topologies. This framework can model interference which is spherically isotropic, or statistically independent, or somewhere on a continuum between these two extremes. The dissertation then utilizes the derived statistical models to analyze communication performance of multi-antenna receivers in interference-limited wireless networks. Accurate communication performance analysis can highlight the tradeoffs between communication performance and computational complexity of various multi-antenna receiver designs. Finally, using interference statistics, this dissertation proposes receiver algorithms that best mitigate the impact of interference on communication performance. The proposed algorithms include multi-antenna combining strategies, as well as, antenna selection algorithms for cooperative communications.

Cognitive radio is a novel approach for better utilization of the scarce, already packed but highly underutilized radio spectrum. To this end, environment-aware unlicensed secondary wireless devices are envisioned to share the spectrum with the primary licensed network, provided that their operation does not impose unmanageable interference on the primary nodes. To achieve this coexistence goal, interference modeling is of great significance. Interference, in general, has a stochastic nature not only due to randomness in the propagation channel, but also due to the random geographic dispersion of nodes. A statistical representation for interference, in which the power levels of the secondary nodes influence the parameters of the model, is, thus, of considerable interest in analysis and design of cognitive wireless network. Stochastic geometry and spatial point processes are used for modeling the coexisting primary and secondary networks. In particular, we model these networks using spatial bivariate Poisson processes. We obtain statistical properties of the distances in these processes and use them for modeling the interference from secondary network on the primary nodes. We first consider an approximate Gaussian model for interference assuming that Central Limit Theorem (C.L.T) can be applied. We, then, show that a more accurate model for interference is the sum of a Normal and a Log-normal random variables. The power levels of secondary nodes can be adjusted to obtain desirable values for the parameters in both of these models. Having this characterization of interference, we propose power control strategies for the secondary network which assure the satisfaction of interference constraint at the primary nodes. We show that these strategies are very easy to implement with little coordination requirement. Nodes either need to know where they are located in the sequence of nodes ordered according to their Euclidean distance to a primary node or need no location information, based on which strategy is being used. Given that secondary nodes have imposed power control strategies to coexist with the primary nodes, we find the lower bound of achievable throughput for the secondary nodes. We use the statistical properties of distances between secondary nodes and find an upper bound for the interference of secondary network on an arbitrary secondary node and thereby a lower bound for its throughput. We show that the approach is applicable to finding the throughput in a general power-constrained random network.

This authoritative resource offers a comprehensive overview of heterogeneous wireless networks, small cells, and device-to-device (D2D) communications. The book provides insight into network modeling and performance analysis of heterogeneous wireless networks. Interference management framework and design issues are covered as well as details about resource mobility, channel models, and typical and statistical interference modeling. This resource explains leveraging resource heterogeneity in interference mitigation and presents the challenges and feasible solutions for concurrent transmission. Moreover, complete coverage of interference alignment in MIMO heterogeneous networks for both downlink and uplink is presented. This book provides performance results for an ideal partially connected interference network as well as a practical heterogeneous network. Readers find practical guidance for LTE and LTE-Advanced as well as 5G in this resource. New techniques and designs for heterogeneous wireless networks are included.

Next generation network, Monte Carlo, famtocell In a wireless communication environment characterized by dynamic channels, high influence of interference, bandwidth shortage and strong demand for quality of service (QoS) support, the challenge for achieving maximal spectral efficiency and high data rate is unprecedented. The data rate achievable now with most practical systems is in the range of tens of megabits per second. As discussed by Shannon, the channel capacity limit is not dependent on the transmitter/ receiver technologies but on the properties of the communication channel. The SIR in the presence of multipath fading, shadowing and path loss is a very important parameter for studying the capacity of a wireless system. This thesis presents a statistical analysis of the SIR for next generation wireless networks. First a new analytical formula for the PDF of the signal to interference ratio for a single interferer is presented. Monte Carlo simulation is used to validate the analytical formula. However in a realistic environment there are often more than one active interferers in a given cell. Two distinct approaches are used to study the effect of multiple interferers on the statistics of the SIR. The sum of random vector approach is used to find the PDF of the SIR for the multiple interference scenario. Laguerre polynomial approximations are used to simplify the problem of finding the PDF of the SIR. A new algorithm is proposed to find the optimum free parameter for the Laguerre approximation. However even with the optimum free parameter, the computational burden required to approximate the PDF of the SIR for multiple interferers with a Laguerre polynomial was impractical. Therefore an alternative characteristics function approach was proposed in this thesis. In this approach the path loss, shadowing and multipath fading are assumed to be dependent random variables. The statistical analysis of the SIR is used to study the TDD inter-cell interference in a self-organizing user deployed femtocell systems.

With 26 entirely new and 5 extensively revised chapters out of the total of 39, the Mobile Communications Handbook, Third Edition presents an in-depth and up-to-date overview of the full range of wireless and mobile technologies that we rely on every day. This includes, but is not limited to, everything from digital cellular mobile radio and evolving personal communication systems to wireless data and wireless networks Illustrating the extraordinary evolution of wireless communications and networks in the last 15 years, this book is divided into five sections: Basic Principles provides the essential underpinnings for the wide-ranging mobile communication technologies currently in use throughout the world. Wireless Standards contains technical details of the standards we use every day, as well as insights into their development. Source Compression and Quality Assessment covers the compression techniques used to represent voice and video for transmission over mobile communications systems as well as how the delivered voice and video quality are assessed. Wireless Networks examines the wide range of current and developing wireless networks and wireless methodologies. Emerging Applications explores newly developed areas of vehicular communications and 60 GHz wireless communications. Written by experts from industry and academia, this book provides a succinct overview of each topic, quickly bringing the reader up to date, but with sufficient detail and references to enable deeper investigations. Providing much more than a "just the facts" presentation, contributors use their experience in the field to provide insights into how each topic has emerged and to point toward forthcoming developments in mobile communications.

This book consists of the identification, characterization, and modeling of electromagnetic interferences in substations for the deployment of wireless sensor networks. The authors present in chapter 3 the measurement setup to record sequences of impulsive noise samples in the ISM band of interest. The setup can measure substation impulsive noise, in wide band, with enough samples per time window and enough precision to allow a statistical study of the noise. During the measurement campaign, the authors recorded around 120 noise sequences in different substations and for four ranges of equipment voltage, which are 25 kV, 230 kV, 315 kV and 735 kV. A characterization process is proposed, by which physical characteristics of partial discharge can be measured in terms of first- and second-order statistics. From the measurement campaign, the authors infer the characteristics of substation impulsive noise as a function of the substation equipment voltage, and can provide representative parameters for the four voltage ranges and for several existing impulsive noise models. The authors investigate in chapters 4 and 5 the modeling of electromagnetic interferences caused by partial discharge sources. First, the authors propose a complete and coherent approach model that links physical characteristics of high-voltage installations to the induced radio-interference spectra of partial discharge sources. The goodness-of-fit of the proposed physical model has been measured based on some interesting statistical metrics. This allows one to assess the effectiveness of the authors' approach in terms of first- and second-order statistics. Chapter 6 proposes a model based on statistical approach. Indeed, substation impulsive noise is composed of correlated impulses, which would require models with memory in order to replicate a similar correlation. Among different models, we have configured a Partitioned Markov Chain (PMC) with 19 states (one state for the background noise and 18 states for the impulse); this Markov-Gaussian model is able to generate impulsive noise with correlated impulse samples. The correlation is observable on the impulse duration and the power spectrum of the impulses. Our PMC model provides characteristics that are more similar to the characteristics of substation impulsive noise in comparison with other models, in terms of time and frequency response, as well as Probability Density Functions (PDF). Although PMC represents reliably substation impulsive noise, the model remains complex in terms of parameter estimation due to a large number of Markov states, which can be an obstacle for future wireless system design. In order to simplify the model, the authors decrease the number of states to 7 by assigning one state to the background noise and 6 states to the impulse and we call this model PMC-6. PMC-6 can generate realistic impulses and can be easily implemented in a receiver in order to mitigate substation impulsive noise. Representative parameters are provided in order to replicate substation impulsive noise for different voltage ranges (25-735 kV). Chapter 7, a generalized radio-noise model for substations is proposed, in which there are many discharges sources that are randomly distributed over space and time according to the Poisson field of interferers approach. This allows for the identification of some interesting statistical properties of moments, cumulants and probability distributions. These can, in turn, be utilized in signal processing algorithms for rapid partial discharge's identification, localization, and impulsive noise mitigation techniques in wireless communications in substations. The primary audience for this book is the electrical and power engineering industry, electricity providers and companies who are interested in substation automation systems using wireless communication technologies for smart grid applications. Researchers, engineers and students studying and working in wireless communication will also want to buy this book as a reference.

This book offers a comprehensive overview of fading and shadowing in wireless channels. A number of statistical models including simple, hybrid, compound and cascaded ones are presented along with a detailed discussion of diversity techniques employed to mitigate the effects of fading and shadowing. The effects of co-channel interference before and after the implementation of diversity are also analyzed. To facilitate easy understanding of the models and the analysis, the background on probability and random variables is presented with relevant derivations of densities of the sums, products, ratios as well as order statistics of random variables. The book also provides material on digital modems of interest in wireless systems. The updated edition expands the background materials on probability by offering sections on Laplace and Mellin transforms, parameter estimation, statistical testing and receiver operating characteristics. Newer models for fading, shadowing and shadowed fading are included along with the analysis of diversity combining algorithms. In addition, this edition contains a new chapter on Cognitive Radio. Based on the response from readers of the First Edition, detailed Matlab scripts used in the preparation of this edition are provided. Wherever necessary, Maple scripts used are also provided.

Since interference is the main performance-limiting factor in most wireless networks, it is crucial to characterize the interference statistics. The main two determinants of the interference are the network geometry (spatial distribution of concurrently transmitting nodes) and the path loss law (signal attenuation with distance). For certain classes of node distributions, most notably Poisson point processes, and attenuation laws, closed-form results are available, for both the interference itself as well as the signal-to-interference ratios, which determine the network performance. This monograph presents an overview of these results and gives an introduction to the analytical techniques used in their derivation. The node distribution models range from lattices to homogeneous and clustered Poisson models to general motion-invariant ones. The analysis of the more general models requires the use of Palm theory, in particular conditional probability generating functionals, which are briefly introduced in the appendix.

This reference text introduces advanced topics in the field of reliability engineering, introduces statistical modeling techniques, and probabilistic methods for diverse applications. It comprehensively covers important topics including consecutive-type reliability systems, coherent structures, multi-scale statistical modeling, the performance of reliability structures, big data analytics, prognostics, and health management. It covers real-life applications including optimization of telecommunication networks, complex infrared detecting systems, oil pipeline systems, and vacuum systems in accelerators or spacecraft relay stations. The text will serve as an ideal reference book for graduate students and academic researchers in the fields of industrial engineering, manufacturing science, mathematics, and statistics.