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.

This book bridges the gap between theory and applications that currently exist in undergraduate engineering probability textbooks. It offers examples and exercises using data (sets) in addition to traditional analytical and conceptual ones. Conceptual topics such as one and two random variables, transformations, etc. are presented with a focus on applications. Data analytics related portions of the book offer detailed coverage of receiver operating characteristics curves, parametric and nonparametric hypothesis testing, bootstrapping, performance analysis of machine vision and clinical diagnostic systems, and so on. With Excel spreadsheets of data provided, the book offers a balanced mix of traditional topics and data analytics expanding the scope, diversity, and applications of engineering probability. This makes the contents of the book relevant to current and future applications students are likely to encounter in their endeavors after completion of their studies. A full suite of classroom material is included. A solutions manual is available for instructors. Bridges the gap between conceptual topics and data analytics through appropriate examples and exercises; Features 100's of exercises comprising of traditional analytical ones and others based on data sets relevant to machine vision, machine learning and medical diagnostics; Intersperses analytical approaches with computational ones, providing two-level verifications of a majority of examples and exercises.

The book takes a problem solving approach in presenting the topic of differential equations. It provides a complete narrative of differential equations showing the theoretical aspects of the problem (the how's and why's), various steps in arriving at solutions, multiple ways of obtaining solutions and comparison of solutions. A large number of comprehensive examples are provided to show depth and breadth and these are presented in a manner very similar to the instructor's class room work. The examples contain solutions from Laplace transform based approaches alongside the solutions based on eigenvalues and eigenvectors and characteristic equations. The verification of the results in examples is additionally provided using Runge-Kutta offering a holistic means to interpret and understand the solutions. Wherever necessary, phase plots are provided to support the analytical results. All the examples are worked out using MATLAB (R) taking advantage of the Symbolic Toolbox and LaTex for displaying equations. With the subject matter being presented through these descriptive examples, students will find it easy to grasp the concepts. A large number of exercises have been provided in each chapter to allow instructors and students to explore various aspects of differential equations.

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.

The book takes a problem solving approach in presenting the topic of differential equations. It provides a complete narrative of differential equations showing the theoretical aspects of the problem (the how's and why's), various steps in arriving at solutions, multiple ways of obtaining solutions and comparison of solutions. A large number of comprehensive examples are provided to show depth and breadth and these are presented in a manner very similar to the instructor's class room work. The examples contain solutions from Laplace transform based approaches alongside the solutions based on eigenvalues and eigenvectors and characteristic equations. The verification of the results in examples is additionally provided using Runge-Kutta offering a holistic means to interpret and understand the solutions. Wherever necessary, phase plots are provided to support the analytical results. All the examples are worked out using MATLAB (R) taking advantage of the Symbolic Toolbox and LaTex for displaying equations. With the subject matter being presented through these descriptive examples, students will find it easy to grasp the concepts. A large number of exercises have been provided in each chapter to allow instructors and students to explore various aspects of differential equations.

This book bridges the gap between theory and applications that currently exist in undergraduate engineering probability textbooks. It offers examples and exercises using data (sets) in addition to traditional analytical and conceptual ones. Conceptual topics such as one and two random variables, transformations, etc. are presented with a focus on applications. Data analytics related portions of the book offer detailed coverage of receiver operating characteristics curves, parametric and nonparametric hypothesis testing, bootstrapping, performance analysis of machine vision and clinical diagnostic systems, and so on. With Excel spreadsheets of data provided, the book offers a balanced mix of traditional topics and data analytics expanding the scope, diversity, and applications of engineering probability. This makes the contents of the book relevant to current and future applications students are likely to encounter in their endeavors after completion of their studies. A full suite of classroom material is included. A solutions manual is available for instructors. Bridges the gap between conceptual topics and data analytics through appropriate examples and exercises; Features 100's of exercises comprising of traditional analytical ones and others based on data sets relevant to machine vision, machine learning and medical diagnostics; Intersperses analytical approaches with computational ones, providing two-level verifications of a majority of examples and exercises.

The study of signal transmission and deterioration in signal characteristics as the signal propagates through wireless channels is of great significance. The book presents a comprehensive view of channel degradation arising from fading and shadowing. Various statistical models including simple, hybrid, compound, complex and cascaded ones are presented with detailed derivations along with measures to quantify the deterioration such as the amount of fading, error rates and outage probabilities. The models range from the Rayleigh and Rician through Suzuki, generalized K, cascaded and alpha-mu and similar ones. This is followed by the analysis of mitigation of fading and shadowing through diversity (simple, hybrid, micro- and macro- level) and combining algorithms. The density and distribution functions, error rates and outages are derived and results analyzed to quantify the improvements. The effects of co-channel interference before and after the implementation of diversity are also analyzed. To facilitate easy understanding of the models and analysis, the background information in terms of probability and random variables is presented with relevant derivations of densities of linear and nonlinear transformation of random variables, the sums, products, ratios as well as order statistics of random variables of all types. The book also provides material on digital modems of interest in wireless systems. Thus, the book with 1100+ equations and 350+ Matlab generated figures and tables is an ideal source for students, educators, researchers and professionals in wireless communications allowing access to information currently unavailable.