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Simplified Robust Adaptive Detection and Beamforming for Wireless Communications [Kõva köide]

  • Formaat: Hardback, 424 pages, kõrgus x laius x paksus: 213x127x25 mm, kaal: 567 g
  • Ilmumisaeg: 10-Aug-2018
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118938240
  • ISBN-13: 9781118938249
Teised raamatud teemal:
Simplified Robust Adaptive Detection and Beamforming for Wireless CommunicationsSuurem pilt
  • Formaat: Hardback, 424 pages, kõrgus x laius x paksus: 213x127x25 mm, kaal: 567 g
  • Ilmumisaeg: 10-Aug-2018
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118938240
  • ISBN-13: 9781118938249
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118938249.html
Märksõnad:
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.
About the Author xiii
About the Companion Website xiv
1 Introduction 1(12)
1.1 Motivation
1(3)
1.2 Book Overview
4(9)
2 Wireless System Models 13(76)
2.1 Introduction
13(14)
2.1.1 Modulation and Coding Scheme and Link Adaptation
24(2)
2.1.2 Link Adaptation
26(1)
2.2 DS-CDMA Basic Formulation
27(16)
2.2.1 Pulse-shaping Filter
30(1)
2.2.2 Discrete Time Model
30(2)
2.2.3 Channel Model
32(6)
2.2.4 Matrix Formulation for DS/CDMA System Model
38(3)
2.2.5 Synchronous DS/CDMA System
41(2)
2.3 Performance Evaluation
43(3)
2.3.1 Signal to Interference plus Noise Ratio
43(1)
2.3.2 Bit Error Rate
44(2)
2.4 MIMO/OFDM System Model
46(27)
2.4.1 FFT and IFFT
49(3)
2.4.2 Cyclic Prefix
52(1)
2.4.3 Single-user MIMO/OFDM
53(11)
2.4.3.1 3GPP LTE MIMO
55(9)
2.4.4 Adaptive Resource Management
64(5)
2.4.5 Multi-User MIMO/OFDM
69(2)
2.4.6 Adaptive filtering in MIMO/OFDM System
71(1)
2.4.7 Performance Evaluation of MIMO/MBER System
71(2)
2.5 Adaptive Antenna Array
73(7)
2.5.1 Uniform Linear Array
73(5)
2.5.2 DS/CDMA with Antenna Array
78(2)
2.6 Simulation Software
80(2)
References
82(7)
3 Adaptive Detection Algorithms 89(38)
3.1 Introduction
89(1)
3.2 The Conventional Detector
90(1)
3.3 Multiuser Detection
91(18)
3.3.1 Decorrelating Detector
93(1)
3.3.2 Minimum Mean-squared Error Detector
93(2)
3.3.3 Adaptive Detection
95(1)
3.3.4 Blind Detection
95(10)
3.3.4.1 Constrained Optimization
96(9)
3.3.5 Constant Modulus Approach
105(2)
3.3.6 Subspace Approach
107(2)
3.4 Simulation Results
109(9)
3.4.1 Linear Detectors
109(2)
3.4.2 MOE Detectors
111(2)
3.4.2.1 MOE Detector with Single Constraint
111(1)
3.4.2.2 MOE Detector with Multiple Constraints
112(1)
3.4.3 Channel Estimation Techniques
113(2)
3.4.4 LCCMA Detector
115(3)
References
118(9)
4 Robust RLS Adaptive Algorithms 127(54)
4.1 Introduction
127(4)
4.2 IQRD-RLS Algorithm
131(1)
4.3 IQRD-Based Receivers with Fixed Constraints
132(3)
4.3.1 Direct-form MOE Detector
132(1)
4.3.2 MOE Detector based on IQRD-RLS and PLIC
133(2)
4.4 IQRD-based Receiver with Optimized Constraints
135(4)
4.5 Channel Estimation Techniques
139(5)
4.5.1 Noise Cancellation Schemes
139(2)
4.5.1.1 Adaptive Implementation of Improved cost function
139(1)
4.5.1.2 Adaptive Implementation of Modified Cost Function
140(1)
4.5.2 Adaptive Implementation of POR Method
141(1)
4.5.3 Adaptive Implementation of Capon Method
142(2)
4.6 New Robust Detection Technique
144(4)
4.7 Systolic Array Implementation
148(5)
4.8 Simulation Results
153(10)
4.8.1 Experiment 1
153(2)
4.8.2 Experiment 2
155(3)
4.8.3 Experiment 3
158(2)
4.8.4 Experiment 4
160(2)
4.8.5 Experiment 5
162(1)
4.9 Complexity Analysis
163(4)
Appendix 4.A Summary of Inverse QR Algorithm with Inverse Updating
167(2)
Appendix 4.B QR Decomposition Algorithms
169(2)
Appendix 4.C Subspace Tracking Algorithms
171(2)
References
173(8)
5 Quadratically Constrained Simplified Robust Adaptive Detection 181(44)
5.1 Introduction
181(6)
5.2 Robust Receiver Design
187(12)
5.2.1 Quadratic Inequality Constraint
187(7)
5.2.1.1 SP Approach
188(1)
5.2.1.2 Tian Approach
189(2)
5.2.1.3 A Simplified VL Approach
191(3)
5.2.2 Optimum Step-size Estimation
194(1)
5.2.3 Low-complexity Recursive Implementation based on PLIC
195(3)
5.2.4 Convergence Analysis
198(1)
5.3 Geometric Approach
199(3)
5.4 Simulation Results
202(11)
5.5 Complexity Analysis
213(2)
Appendix 5.A Robust Recursive Conjugate Gradient (RCG) Algorithm
215(2)
References
217(8)
6 Robust Constant Modulus Algorithms 225(38)
6.1 Introduction
225(7)
6.2 Robust LCCMA Formulation
232(2)
6.3 Low-complexity Recursive Implementation of LCCMA
234(3)
6.4 BSCMA Algorithm
237(2)
6.5 BSCMA with Quadratic Inequality Constraint
239(2)
6.6 Block Processing and Adaptive Implementation
241(2)
6.7 Simulation Results for Robust LCCMA
243(3)
6.8 Simulation Results for Robust BSCMA
246(4)
6.9 Complexity Analysis
250(3)
References
253(10)
7 Robust Adaptive Beamforming 263(82)
7.1 Introduction
263(16)
7.2 Beamforming Formulation
279(4)
7.2.1 Capon Beamforming
279(2)
7.2.2 LCMV Beamforming
281(2)
7.3 Robust Beamforming Design
283(9)
7.3.1 Adaptive Implementation
288(4)
7.4 Cooperative Joint Constraint Robust Beamforming
292(4)
7.4.1 Adaptive Implementation
295(1)
7.5 Robust Adaptive MVDR Beamformer with Single WC Constraint
296(8)
7.5.1 Lagrange Approach
299(1)
7.5.2 Eigendecomposition Method
299(1)
7.5.3 Taylor Series Approximation Method
300(1)
7.5.4 Adaptive MVDR Beamformer with Single WC Constraint
300(6)
7.5.4.1 Lagrange Multiplier Estimation
301(2)
7.5.4.2 Recursive Implementation
303(1)
7.6 Robust LCMV Beamforming with MBWC Constraints
304(2)
7.7 Geometric Interpretation
306(4)
7.7.1 Ellipsoidal Constraint Beamforming
306(2)
7.7.2 Worst-case Constraint Beamforming
308(2)
7.8 Simulation Results
310(22)
7.8.1 Simulations Results for Ellipsoidal Constraint Beamforming
310(12)
7.8.2 Simulation for WC Constraint Beamforming
322(25)
7.8.2.1 DOA Mismatch Scenario
322(6)
7.8.2.2 Small Angular Spread Scenario
328(3)
7.8.2.3 Large Angular Spread Scenario
331(1)
7.9 Summary
332(1)
References
333(12)
8 Minimum BER Adaptive Detection and Beamforming 345(50)
8.1 Introduction
345(2)
8.2 MBER Beamformer
347(13)
8.2.1 AMBER
351(1)
8.2.2 LMBER
352(1)
8.2.3 Gradient Newton Algorithms
353(1)
8.2.3.1 Newton-AMBER
354(1)
8.2.3.2 Newton-LMBER
354(1)
8.2.4 Normalized Gradient Algorithms
354(1)
8.2.4.1 Normalized-AMBER
355(1)
8.2.4.2 Normalized-LMBER
355(1)
8.2.5 Normalized Newton Gradient Algorithms
355(1)
8.2.5.1 Normalized-Newton-AMBER
355(1)
8.2.5.2 Normalized-Newton-LMBER
356(1)
8.2.6 Block-Shanno MBER
356(4)
8.3 MBER Simulation Results
360(12)
8.3.1 BER Performance versus SNR
361(5)
8.3.2 Convergence Rate Comparison
366(4)
8.3.3 BER Performance versus Number of Subscribers
370(1)
8.3.4 Computational Complexity
371(1)
8.4 MBER Spatial MUD in MIMO/OFDM Systems
372(9)
8.4.1 AMBER
375(1)
8.4.2 LMBER
376(1)
8.4.3 Gradient Newton Algorithms
376(1)
8.4.3.1 Newton-AMBER
377(1)
8.4.3.2 Newton-LMBER
377(1)
8.4.4 Normalized Gradient Algorithms
377(1)
8.4.4.1 Normalized-AMBER
378(1)
8.4.4.2 Normalized-LMBER
378(1)
8.4.5 Normalized Newton Gradient Algorithms
378(1)
8.4.5.1 Normalized-Newton-AMBER
378(1)
8.4.5.2 Normalized-Newton-LMBER
379(1)
8.4.6 Block-Shanno MBER
379(2)
8.5 MBER Simulation Results
381(5)
8.5.1 Convergence Rate Comparison
382(2)
8.5.2 BER Performance versus SNR
384(2)
8.6 Summary
386(1)
References
387(8)
Index 395
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.