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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.  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.

In 2008, when the professional paths of the authors crossed, they worked together on many projects including an end-to-end LTE and HSPA+ networks. Since then, they have teamed up to study, design and evaluate the technical aspects of the state-of-the-art cellular technologies. Combining their practical expertise in diverse topics, they bridge the theory and the best practice of 4G networks into this book. Written by experts in the field with vast hands-on experience in cellular technologies, this book delves deeper into the practical aspects of design and deployment of a commercial LTE network, combining compelling research and field results to cut through the end-to-end architecture, design, and deployment scenarios. The book presents the LTE network performance analysis and optimization and advanced features. Furthermore, the reader is provided with a detailed explanation for coverage and capacity planning of the LTE network. The book also provides an in-depth explanation on how to provision Carrier-Grade VoLTE with a comprehensive coverage of the IMS and RAN advanced features. The LTE analysis is presented in a comparative manner with reference to the HSPA+ network. Key Features:  Conveys the theoretical background of 4G networks Presents key aspects and best practice of 4G networks design and deployment Addresses strategies for performance evaluation and troubleshooting processes Demonstrates assessments of various LTE features including C-DRX, CSFB, MIMO techniques,    and VoLTE Provides practical examples and case studies on coverage prediction, link budget, and capacity dimensioning for voice and data services.  Special focus on the essential capabilities necessary for any operator to test & deploy, before commercializing VoLTE Includes a realistic roadmap for evolution of a deployed 4G networks, including carrier aggregation, HetNet, enhanced MIMO and SON. Design, Deployment and Performance of 4G-LTE Networks will be an invaluable guide for 4G engineers in network operators and vendors, network deployment engineers, R&D engineers, planning and optimization engineers, measurement/performance tools firms, consulting firms, undergraduate and graduate students interested in understanding the LTE system.

Internet of Things (IoT) is one of the key components of Digital Transformation, along with big data and analytics. IoT, together with the cloud, big data, analytics, machine learning (ML), and deep ML, can help create numerous possibilities and new opportunities. These possibilities will impact our daily lives substantially and open new business models for consumers and enterprises where the number of connected IoT devices could go up to 50 Billion by 2022. The ecosystem of IoT consists mainly of sensors/devices layer, connectivity layer and IoT platform. The main value of IoT is in creating use cases for efficiency, monitoring and management of the things/devices. IoT connects the things through the Internet to the IoT platform which equipped with device management and with the possibility of creating new use cases along with data analytics and ML that provide 360 view through data insight

This paper covers the practical aspects of commercial long term evolution (LTE) network design and deployment. The end-to-end architecture of the LTE network and different deployment scenarios are presented. Moreover, the LTE coverage and link budget aspects are discussed in details. Theoretical and practical throughputs of LTE system are analyzed. In addition, capacity dimensioning of LTE system is explained in details and compared with the evolved high-speed packet access (HSPA+) system. Additionally, the quality of service (QoS) of the LTE system and the end-to-end implementation scenarios along with testing results are presented. Finally, the latency of the LTE system is analyzed and compared with the HSPA+ system. This paper can be used as a reference for best practices in LTE network design and deployment.

This chapter develops and implements linearly constrained IQRD‐recursive least squares (RLS) algorithms with multiple constraints for multiuser detection (MUD) in DS/CDMA systems. In the conventional RLS algorithm, the calculation of the Kalman gain requires inversion of the autocovariance matrix of the received signal. The IQRD algorithm acts as a core to the proposed receivers, which facilitates real‐time implementation through systolic implementation. Systolic array implementation of IQRD‐based algorithms has important merits. The chapter considers two approaches: with a constant constrained vector and with an optimized constrained vector. It also implements the channel estimation techniques in adaptive fashion based on the IQRD‐RLS algorithm. Consolidating the channel estimation and the quadratic constraint technique gives an extremely robust detector, especially at low signal‐to‐noise ratios (SNR). Quadratic inequality (QI) constraint value could be selected based on some preliminary knowledge about wireless channels or using a Monte Carlo simulation.

This chapter presents mathematical models of DS/CDMA and orthogonal frequency division multiplexing (OFDM) systems. OFDM has become a most favored technique for broadband wireless systems due to the susceptibility to signal spread under multipath conditions. SC‐FDMA is a modified form of OFDM, with similar throughput performance and complexity. Multiple‐input, multiple‐output (MIMO)‐OFDM is a key technology for next‐generation cellular communications as well as wireless LAN, wireless PAN, and broadcasting. The chapter outlines the modulation and coding scheme for LTE FDD. The multiuser algorithms are based on the DS/CDMA simulation software system and the system model developed by D. L. Anair. The chapter illustrates a complete DS/CDMA system employing an antenna array at the receiver. Combined with multiple antennas at the transmitter and receiver, as well as adaptive modulation, OFDM proves to be robust against channel delay spread.

This chapter presents robust adaptive beamforming techniques that are generally suitable for wireless communications and particularly for base stations in cellular systems. For simplicity, and without losing the generality, it considers simple beamforming formulations in order to develop robust beamforming techniques. There are several existing approaches to robust adaptive beamforming. The so‐called linearly constrained minimum variance (LCMV) beamformer, also known as Capon's method, has been a popular beamforming technique. The chapter summarizes the standard minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) beamformers with single and multiple constraints in the context of a single point source and a source with multipath rays, respectively. It introduces the worst‐case (WC) optimization formulation by summarizing general and special formulations for the steering vector uncertainty set. The chapter analyzes efficient implementations of single‐ and multiple‐WC formulations and presents a geometric illustration for the single‐WC implementation. It further provides simulations and a performance analysis.

This chapter presents an alternative approach to robust adaptive detection. This is based on the recursive fast steepest descent (RSD) adaptive algorithm, with an accurate technique for precisely computing the diagonal loading level without approximation or eigendecomposition. The chapter combines the quadratic inequality (QI) constraint with the RSD algorithm to produce a robust recursive implementation with O(N 2 ) complexity. It develops and integrates a new optimal variable loading (VL) technique into the RSD adaptive algorithm. In addition, the diagonal loading term is optimally computed, with O(N) complexity, using a simple quadratic equation. The chapter illustrates and clarifies geometrical interpretations of the scaled projection (SP) and VL techniques along with recursive least‐squares (RLS) and RSD algorithms. It investigates and compares the performance of the simplified robust detector with the traditional blind minimum output energy (MOE) detector updated using the RLS algorithm and a robust MOE‐RLS w. quadratic constraint (QC) detector.

This chapter presents a survey of adaptive detection algorithms based on the DS/CDMA model. However, the adaptive techniques that are summarized in this survey can be easily extended to MIMO/OFDM and smart antenna arrays. A conventional DS/CDMA receiver treats each user separately, as a signal, with other users considered as noise or multiple access interference (MAI). A major drawback of such conventional DS/CDMA systems is the near‐far problem: degradation in performance due to the sensitivity to the power of desired user against interference power. Reliable demodulation is impossible unless tight power control algorithms are used. The near‐far problem can significantly reduce the capacity. Multiuser detection (MUD) algorithms have been developed to improve capacity dramatically over that achievable with conventional single‐user detection techniques. The performance and stability of the detectors can be analyzed by numerical simulations. All simulation results can be easily obtained using the first software package for MUD.

In addition to blind equalization and blind beamforming, the constant modulus algorithm (CMA) can be used also for code acquisition in the context of DS‐CDMA systems. This chapter presents an alternative approach to robust adaptive blind multiuser detection. This is based on the linearly constrained CMA (LCCMA) and with a quadratic inequality (QI) constraint on the weight vector norm. The chapter provides the robust LCCMA detector design and its adaptive implementation of robust LCCMA. It outlines the block Shanno CMA (BSCMA) algorithm and also introduces the robust BSCMA with the QI constrained. The LCCMA and BSCMA algorithms are used to update the adaptive vector of the partition linear interference canceller (PLIC) structure. The PLIC structure with multiple constraints is employed to identify the multiple access interference (MAI) and hence help prevent interference capture. The chapter describes block processing and adaptive implementation of robust BSCMA. Finally, the chapter also presents computer simulations and performance comparison.