Handbook on Array Processing and Sensor Networks provides readers with a collection of tutorial articles contributed by world-renowned experts on recent advancements and the state of the art in array processing and sensor networks.
Focusing on fundamental principles as well as applications, the handbook provides exhaustive coverage of: wavelets; spatial spectrum estimation; MIMO radio propagation; robustness issues in sensor array processing; wireless communications and sensing in multi-path environments using multi-antenna transceivers; implicit training and array processing for digital communications systems; unitary design of radar waveform diversity sets; acoustic array processing for speech enhancement; acoustic beamforming for hearing aid applications; undetermined blind source separation using acoustic arrays; array processing in astronomy; digital 3D/4D ultrasound imaging technology; self-localization of sensor networks; multi-target tracking and classification in collaborative sensor networks via sequential Monte Carlo; energy-efficient decentralized estimation; sensor data fusion with application to multi-target tracking; distributed algorithms in sensor networks; cooperative communications; distributed source coding; network coding for sensor networks; information-theoretic studies of wireless networks; distributed adaptive learning mechanisms; routing for statistical inference in sensor networks; spectrum estimation in cognitive radios; nonparametric techniques for pedestrian tracking in wireless local area networks; signal processing and networking via the theory of global games; biochemical transport modeling, estimation, and detection in realistic environments; and security and privacy for sensor networks.
Handbook on Array Processing and Sensor Networks is the first book of its kind and will appeal to researchers, professors, and graduate students in array processing, sensor networks, advanced signal processing, and networking.
The emerging wireless technology Wideband Multiple-Input, Multiple-Output (MIMO) holds the promise of greater bandwidth efficiency and wireless link reliability. This technology is just now being implemented into hardware and working its way into wireless standards such as the ubiquitous 802.11g, as well as third- and fourth-generation cellular standards.
Multiple-Input Multiple-Output Channel Models uniquely brings together the theoretical and practical aspects of MIMO communications, revealing how these systems use their multipath diversity to increase channel capacity. It gives the reader a clear understanding of the underlying propagation mechanisms in the wideband MIMO channel, which is fundamental to the development of communication algorithms, signaling strategies, and transceiver design for MIMO systems.
MIMO channel models are important tools in understanding the potential gains of a MIMO system. This book discusses two types of wideband MIMO models in detail: correlative channel models—specifically the Kronecker, Weichselberger, and structured models—and cluster models, including Saleh-Valenzuela, European Cooperation in the field of Scientific and Technical Research (COST) 273, and Random Cluster models. From simple to complex, the reader will understand the models' mechanisms and the reasons behind the parameters. Next, channel sounding is explained in detail, presenting the theory behind a few channel sounding techniques used to sound narrowband and wideband channels. The technique of digital matched filtering is then examined and, using real-life data, is shown to provide very accurate estimates of channel gains. The book concludes with a performance analysis of the structured and Kronecker models.
Multiple-Input Multiple-Output Channel Models is the first book to apply tensor calculus to the problem of wideband MIMO channel modeling. Each chapter features a list of important references, including core literary references, Matlab implementations of key models, and the location of databases that can be used to help in the development of new models or communication algorithms. Engineers who are working in the development of telecommunications systems will find this resource invaluable, as will researchers and students at the graduate or post-graduate level.
The literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem.
The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as:
Shows TLS problems and gives a sketch of their history and applications
Presents MCA EXIN and compares it with the other existing approaches
Introduces the TLS EXIN neuron and the SCG and BFGS acceleration techniques and compares them with TLS GAO
Outlines the GeTLS EXIN theory for generalizing and unifying the regression problems
Establishes the GeMCA theory, starting with the identification of GeTLS EXIN as a generalization eigenvalue problem
In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing real-time problems and show accurate solutions. Neural-Based Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers.
The human brain, as exemplified by cognitive radar, cognitive radio, and cognitive computing, inspires the field of Cognitive Dynamic Systems. In particular, cognitive radio is growing at an exponential rate. Fundamentals of Cognitive Radio details different aspects of the human brain and provides examples of how it can be mimicked by cognitive dynamic systems. The text offers a communication-theoretic background, including information on resource allocation in wireless networks and the concept of robustness.
The authors provide a thorough mathematical background with data on game theory, variational inequalities, and projected dynamic systems. They then delve more deeply into resource allocation in cognitive radio networks. The text investigates the dynamics of cognitive radio networks from the perspectives of information theory, optimization, and control theory. It also provides a vision for the new world of wireless communications by integration of cellular and cognitive radio networks. This groundbreaking book:Shows how wireless communication systems increasingly use cognition to enhance their networks Explores how cognitive radio networks can be viewed as spectrum supply chain networks Derives analytic models for two complementary regimes for spectrum sharing (open-access and market-driven) to study both equilibrium and disequilibrium behaviors of networks Studies cognitive heterogeneous networks with emphasis on economic provisioning for resource sharing Introduces a framework that addresses the issue of spectrum sharing across licensed and unlicensed bands aimed for Pareto optimality
Written for students of cognition, communication engineers, telecommunications professionals, and others, Fundamentals of Cognitive Radio offers a new generation of ideas and provides a fresh way of thinking about cognitive techniques in order to improve radio networks.