Optimization of adaptive signal processing algorithms for wireless communications is based on a model of the underlying propagation channel. In practice, this model is never known perfectly. For example, its parameters have to be estimated and are only known with significant errors. In this book, a systematic treatment of this practical design problem is provided.

High-Resolution and Robust Signal Processing describes key methodological and theoretical advances achieved in this domain over the last twenty years, placing emphasis on modern developments and recent research pursuits. Applications-grounded, this sophisticated resource links theoretical background with high-resolution methods used in wireless communications, brain signal analysis, and space-time radar signal processing. Chapter extras include theorem proofs, derivations, and computational shortcuts, as well as open problems, numerical measurement, and performance examples, and simulation results Sixteen illustrious field leaders invest High-Resolution and Robust Signal Processing with: in-depth reviews of parametric high-resolution estimation and detection techniques; robust array processing solutions for adaptive beam forming and high-resolution direction finding; Parafac techniques for high-resolution array processing and specific areas of application; high-resolution nonparametric methods and implementation tactics for spectral analysis; multidimensional high-resolution data models and discussion of R-D unitary ESPRIT with colored noise; multidimensional high-resolution parameter estimation techniques applicable to channel sounding; estimation procedures for high-resolution space-time radar signal processing using 2-D or 1-D/1-D models; and models and methods for EEG/MEG space-time dipole source estimation and sensory array design.

This book presents an alternative and simplified approaches for the robust adaptive detection and beamforming in wireless communications. It adopts several systems models including DS/CDMA, OFDM/MIMO with antenna array, and general antenna arrays beamforming model. It presents and analyzes recently developed detection and beamforming algorithms with an emphasis on robustness. In addition, simplified and efficient robust adaptive detection and beamforming techniques are presented and compared with exiting techniques. Practical examples based on the above systems models are provided to exemplify the developed detectors and beamforming algorithms. Moreover, the developed techniques are implemented using MATLAB—and the relevant MATLAB scripts are provided to help the readers to develop and analyze the presented algorithms. em style="mso-bidi-font-style: normal;"Simplified Robust Adaptive Detection and Beamforming for Wireless Communications starts by introducing readers to adaptive signal processing and robust adaptive detection. It then goes on to cover Wireless Systems Models. The robust adaptive detectors and beamformers are implemented using the well-known algorithms including LMS, RLS, IQRD-RLS, RSD, BSCMA, CG, and SD. The robust detection and beamforming are derived based on the existing detectors/beamformers including MOE, PLIC, LCCMA, LCMV, MVDR, BSCMA, and MBER. The adopted cost functions include MSE, BER, CM, MV, and SINR/SNR.

Wireless Communication Systems: Advanced Techniques for Signal Receptionoffers a unified frameworkfor understanding today's newest techniques for signal processing in communication systems - andusing them to design receivers for emerging wireless systems. Two leading researchers cover a fullrange of physical-layer issues, including multipath, dispersion, interference, dynamism, andmultiple-antenna systems. Topics include blind, group-blind, space-time, and turbo multiuserdetection; narrowband interference suppression; Monte Carlo Bayesian signal processing; fast fadingchannels; advanced signal processing in coded OFDM systems, and more.

The area of personal and wireless communications is a burgeoning field. Technology advances and new frequency allocations for personal communication services (PCS) are creating numerous business and technical opportunities. It is becoming clear that an essential requirement for exploiting opportunities is the ability to track the dramatic changes in wireless technology, which is a principal aim of this book. Wireless Personal Communications: Research Developments places particular emphasis on the areas of signal processing, propagation and spread-spectrum, and emerging communication systems. This book contains new results on adaptive antennas for capacity improvements in wireless communication systems, as well as state-of-the-art information on the latest technical developments. Also included are several chapters which discuss the impact of defense conversion on the wireless industry, and related competitive issues. The six parts of the book each focus on a distinct issue in wireless communications. Part I contains several tutorial chapters on key areas in wireless communications. The first chapter is on radio wave propagation for emerging wireless personal communication systems. Chapter two contains a comprehensive study of emerging DSP-based interference rejection techniques for single channel (antenna) systems. Chapter three deals with spread spectrum wireless communications, explaining the concept of spread spectrum, modeling techniques for spread spectrum, and current applications and research issues for spread spectrum systems. Part II focuses on digital signal processing and spread spectrum, two means of creating interference and multipath robust communications. Part III concerns propagation aspects of wireless communications. Part IV discusses the performance of emerging wireless systems. Part V describes the opportunities and pitfalls of defense conversion from the perspective of several U.S. defense firms that have successfully made the transition to commercial wireless. The final section discusses a number of competitive issues regarding personal communication services.

This book investigates adaptive physical-layer beamforming and resource allocation that ensure reliable data transmission in the multi-antenna broadcast channel. The book provides an overview of robust optimization techniques and modelling approximations to deal with stochastic performance metrics. One key contribution of the book is a closed-form description of the achievable rates with unlimited transmit power for a rank-one channel error model. Additionally, the book provides a concise duality framework to transform mean square error (MSE) based beamformer designs, e.g., quality of service and balancing optimizations, into equivalent uplink filter designs. For the algorithmic solution, the book analyses the following paradigm: transmission to receivers with large MSE targets (low demands) is switched off if the transmit power is low. The book also studies chance constrained optimizations for limiting the outage probability. In this context, the book provides two novel conservative outage probability approximations, that result in convex beamformer optimizations. To compensate for the remaining inaccuracy, the book introduces a post-processing power allocation. Finally, the book applies the introduced beamformer designs for SatCom, where interference from neighboring spotbeams and channel fading are the main limitations.

Abstract: From a signal processing for communications perspective, three fundamental transceiver design components are the channel precoder, the channel estimator, and the channel equalizer. The optimal design of these blocks is typically formulated as an optimization problem with a certain objective function, and a given constraint set. However, besides the objective function and the constraint set, their optimal design crucially depends upon the adopted system model and the assumed system state. While, optimization under a perfect knowledge of these underlying parameters (system model and state) is relatively straight forward and well explored, the optimization under their imperfect (partial or uncertain) knowledge is more involved and cumbersome. Intuitively, the central question that arises here is: should we fully trust the available imperfect knowledge of the underlying parameters, should we just ignore it, or should we go for an “intermediate” approach? This thesis deals with three crucial transceiver design problems from a signal processing for communications perspective, and attempts to answer the fundamental question of how to handle the presence of uncertainty about the design parameters in the respective optimization problem formulations.