Simplified Robust Adaptive Detection and Beamforming for Wireless Communications (eBook)
John Wiley & Sons (Verlag)
978-1-118-93822-5 (ISBN)
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.
Ayman Elnashar, PhD, has 20+ years of experience in the telecoms industry, including 2G/3G/LTE/WiFi/IoT/5G/Wireless Networks. He was part of three major start-up telecom operators in the MENA region (Orange/Egypt, Mobily/KSA, and du/UAE). Currently, he is Head of Core and Cloud planning with the Emirates Integrated Telecommunications Co. 'du', UAE. He is the founder of the Terminal Innovation Lab and UAE 5G Innovation Gate (U5GIG). Prior to this, he was Sr. Director - Wireless Networks, Terminals and IoT, where he managed and directed the evolution, evaluation, and introduction of du wireless networks, terminals and IoT, including LTE/LTE-A, HSPA+, WiFi, NB-IoT, and is currently working towards deploying 5G network in the UAE.
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.
Ayman Elnashar, PhD, has 20+ years of experience in the telecoms industry, including 2G/3G/LTE/WiFi/IoT/5G/Wireless Networks. He was part of three major start-up telecom operators in the MENA region (Orange/Egypt, Mobily/KSA, and du/UAE). Currently, he is Head of Core and Cloud planning with the Emirates Integrated Telecommunications Co. "du", UAE. He is the founder of the Terminal Innovation Lab and UAE 5G Innovation Gate (U5GIG). Prior to this, he was Sr. Director - Wireless Networks, Terminals and IoT, where he managed and directed the evolution, evaluation, and introduction of du wireless networks, terminals and IoT, including LTE/LTE-A, HSPA+, WiFi, NB-IoT, and is currently working towards deploying 5G network in the UAE.
Chapter 1 - Introduction
Chapter 2 - Wireless Systems Models
Chapter 3 - Adaptive Detection Algorithms
Chapter 4 - Robust RLS Adaptive Algorithms
Chapter 5 - Quadratically Constrained Robust Detection
Chapter 6 - Robust Constant Modulus Algorithms
Chapter 7 - Robust Adaptive Beamforming
Chapter 8 - Minimum BER Adaptive Detection and Beamforming
1
Introduction
1.1 Motivation
This book presents alternative and simplified approaches for the robust adaptive detection and beamforming in wireless communications. The book adopts several system models, including:
- DS/CDMA, with and without antenna array
- MIMO‐OFDM with antenna array
- general smart antenna array model.
Recently developed detection and beamforming algorithms are presented and analyzed with an emphasis on robustness. In addition, simplified and efficient robust adaptive detection and beamforming techniques are developed and compared with existing techniques. The robust detectors and beamformers are implemented using well‐known algorithms including, but not limited to:
- least‐mean‐square
- recursive least‐squares (RLS)
- inverse QR decomposition RLS (IQRD‐RLS)
- fast recursive steepest descent (RSD)
- block‐Shanno constant modulus (BSCMA)
- conjugate gradient (CG)
- steepest descent (SD).
The robust detection and beamforming methods are derived from existing detectors/beamformers including, but not limited to:
- the robust minimum output energy (MOE) detector
- partition linear interference canceller (PLIC) detector
- linearly constrained constant modulus (CM) algorithm (LCCMA),
- linearly constrained minimum variance (LCMV) beamforming with single constraint,
- minimum variance distortionless response (MVDR) beamformer with multiple constraint
- block Shanno constant modulus algorithm (BSCMA) based detector/beamformer
- adaptive minimum bit error rate (BER) based detectors.
The adopted cost functions include the mean square error (MSE), BER, CM, MV and the signal‐to‐noise or signal‐to‐interference‐plus‐noise ratios (SINR/SNR). The presented robust adaptive techniques include:
- quadratic inequality constraint (QIC)
- diagonal loading techniques
- single and multiple worst‐case (WC) constraint(s)
- ellipsoidal constraint
- joint constraints
Detailed performance analysis in terms of MSE, SINR, BER, computational complexity, and robustness are conducted for all the presented detectors and beamformers. Practical examples based on the above system 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 allow the readers to develop and analyze the presented algorithms. The developed algorithms will be presented in the context of DS/CDMA, MIMO‐OFDM, and smart antenna arrays, but they can be easily extended to other domains and other applications. Figure 1.1 provides a high‐level description of the book.
Figure 1.1 Summary of the book.
Recently, robust adaptive detection/beamforming has become a hot topic. Researchers seek to provide robustness against uncertainty in the direction of arrival (DOA) or the signature waveform, accuracy errors, calibration errors, small sample sizes, mutual coupling in antenna arrays, and so on. The major concern with the robust algorithms is the compromises involving robustness, complexity, and optimality. This book is aims to efficiently address this concern by presenting alternative and simplified approaches for robust adaptive detection and beamforming in wireless communications systems. The presented algorithms have low computational complexity while offering optimal or close‐to‐optimal performance and can be practically implemented. Wireless communication applications using DS/CDMA, MIMO‐OFDM, and smart antenna systems are presented to demonstrate their robustness and to compare their complexity with established techniques and optimal detectors/beamformers.
The book presents and addresses current hot topics in adaptive signal processing: robustness and simplified adaptive implementation. It presents simplified approaches that add robustness to adaptive signal processing algorithms, with less computational complexity, while maintaining optimality. In addition, the presented algorithms are illustrated with practical examples and simulation results for major wireless communications systems, including DS/CDMA, MIMO‐OFDM, and smart antenna systems. Moreover, Matlab scripts are provided for further analysis and development. The reader can easily extend the techniques and approaches in this book to other areas and to different applications.
With the growth of mobile communication subscribers, the introduction of high data‐rate services, and the overall increase in user traffic, new ways are needed to increase the capacity of wireless networks. Smart antennae, MIMO and beamforming are some of the most promising technologies now being exploited to enhance the capacity of the cellular system. In wireless networks, the traditional omni and directional antennae of a base‐station cause higher interference than necessary. Additionally, they are wasteful, as most transmitted signals will not be received by the target user. Adaptive antennas are a multidiscipline technology area that has exhibited growth steadily over the last four decades, primarily due to the impressive advances in the field of digital processing. Exploiting the spatial dimension using adaptive antennae promises impressive increases in system performance in terms of capacity, coverage, and signal quality. This will ultimately lead to increased spectral efficiency and extended coverage, especially for higher‐frequency bands, such as millimetre waves (mmWave), that will be adopted for 5G evolution.
1.2 Book Overview
In Chapter 1, the mathematical models of DS/CDMA and MIMO‐OFDM systems are presented. These form the foundation for the robust adaptive detection and beamforming algorithms that will be presented and/or developed in this book. DS/CDMA and OFDM are used in 3GPP 3G and 4G systems respectively. The 5G system under development by 3GPP will use evolved versions of MIMO‐OFDM. The algorithms presented in this book may fit any of these systems and may also be extended to other systems. The focus of the 3G and 4G evolutions were on mobile broadband, as a result of widespread smartphone adoption. The internet of things (IoT) evolution will lead to billions of devices being connected to the internet and this has directed the 3GPP and mobile communications industry towards narrowband technologies. 3GPP has modified the LTE system to meet the IoT requirements by introducing NB‐IoT. Other proprietary technologies, such as low‐power wide‐area networks, have used narrowband or ultra‐narrowband technologies such as chirp spread spectrum. The focus of this book is not on certain technologies and readers will need to expend some effort in order to apply the detection and beamforming algorithms outlined here to specific systems. The focus of the book is the development and comparative analysis of robust adaptive detection and beamforming algorithms based on simplified system models. All the results in the book are simulated using Matlab and the developed scripts are provided along with the book. The reader may need to slightly modify the scripts depending on the Matlab version. In addition, some algorithms developed by other authors are provided as part of the software package with this book for the purpose of comparative analysis.
In Chapter 3, we will provide 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. The DS/CDMA model is the most complicated system model, because of its need for multiuser interference cancellation and since the channel is frequency selective, as explained in Chapter 2. Despite the various advantages of the DS/CDMA system, it is interference limited due to multiuser interference and it cannot be easily extended to ultra‐broadband systems. 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 the desired user against the power of the interference. A reliable demodulation is impossible unless tight power control algorithms are exercised. The near–far problem can significantly reduce the capacity. Multiuser detection (MUD) algorithms can give dramatically higher capacity than conventional single‐user detection techniques. MUD considers signals from all users, which leads to joint detection. MUD reduces interference and hence leads to a capacity increase, alleviating the near–far problem. Power control algorithms can be used but are not necessary.
Linear receiver design by minimization of some inverse filtering criterion is explained in Chapter 4. Appropriate constraints are used to avoid the trivial all‐zero solution. A well‐known cost function for the constrained optimization problem is the variance or the power of the output signal. An MOE detector for multiuser detection is developed, based on the constrained optimization approach. In an additive white Gaussian environment with no multipath, this detector provides a blind solution with MMSE performance. In Chapter 4, linearly constrained IQRD‐RLS algorithms with multiple constraints are developed and implemented...
| Erscheint lt. Verlag | 11.6.2018 |
|---|---|
| Sprache | englisch |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Technik ► Nachrichtentechnik | |
| Schlagworte | adaptive detection algorithms • Adaptive signal processing • adaptive signal processing applications • antenna array • array processing • Beamforming Algorithms • Broadband Networking • Communication technology • digital signal processing • Drahtlose Kommunikation • DS/CDMA • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • Kommunikationstechnik • minimum BER adaptive detection • Mobile & Wireless Communications • Mobile Communications • OFDM/MIMO • quadratically constrained robust detection • radar signal processing • robust adaptive beamforming • robust adaptive detection • robust adaptive detection/beamforming • robust constant modulus algorithms • robustness against uncertainty in DOA (direction-of-arrival) • robust RLS adaptive algorithms • sensor networks • Signal Processing • Signalverarbeitung • telecommunications • wireless communications • wireless systems models |
| ISBN-10 | 1-118-93822-4 / 1118938224 |
| ISBN-13 | 978-1-118-93822-5 / 9781118938225 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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