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E-grāmata: Design and Optimization for 5G Wireless Communications [Wiley Online]

  • Formāts: 424 pages
  • Sērija : IEEE Press
  • Izdošanas datums: 16-Apr-2020
  • Izdevniecība: Wiley-IEEE Press
  • ISBN-10: 1119494494
  • ISBN-13: 9781119494492
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  • Wiley Online
  • Cena: 146,61 €*
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Design and Optimization for 5G Wireless CommunicationsPalielināt
  • Formāts: 424 pages
  • Sērija : IEEE Press
  • Izdošanas datums: 16-Apr-2020
  • Izdevniecība: Wiley-IEEE Press
  • ISBN-10: 1119494494
  • ISBN-13: 9781119494492
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119494492

This book offers a technical background to the design and optimization of wireless communication systems, covering optimization algorithms for wireless and 5G communication systems design. The book introduces the design and optimization systems which target capacity, latency, and connection density; including Enhanced Mobile Broadband Communication (eMBB), Ultra-Reliable and Low Latency Communication (URLL), and Massive Machine Type Communication (mMTC).

The book is organized into two distinct parts: Part I, mathematical methods and optimization algorithms for wireless communications are introduced, providing the reader with the required mathematical background. In Part II, 5G communication systems are designed and optimized using the mathematical methods and optimization algorithms.

Preface xi
List of Abbreviations
xiii
Part I Mathematical Methods and Optimization Theories for Wireless Communications
1(194)
1 Historical Sketch of Cellular Communications and Networks
3(10)
1.1 Evolution of Cellular Communications and Networks
3(6)
1.2 Evolution to 5G Networks
9(4)
References
11(2)
2 SG Wireless Communication System Parameters and Requirements
13(8)
2.1 5G Requirements
13(3)
2.2 Trade-off of 5G System Metrics
16(5)
Problems
19(1)
References
20(1)
3 Mathematical Methods for Wireless Communications
21(76)
3.1 Signal Spaces
21(11)
3.2 Approximation and Estimation in Signal Spaces
32(39)
3.2.1 Approximation Problems
32(3)
3.2.2 Least Squares Estimation
35(10)
3.2.3 Minimum Mean-Squared Error Estimation
45(20)
3.2.4 Maximum Likelihood and Maximum A Posteriori Estimation
65(6)
3.3 Matrix Factorization
71(26)
3.3.1 LU Decomposition
71(5)
3.3.2 Cholesky Decomposition
76(1)
3.3.3 QR Decomposition
77(8)
3.3.4 SVD Decomposition
85(7)
Problems
92(3)
References
95(2)
4 Mathematical Optimization Techniques for Wireless Communications
97(54)
4.1 Introduction
97(2)
4.2 Mathematical Modeling and Optimization Process
99(9)
4.3 Linear Programming
108(12)
4.4 Convex Optimization
120(18)
4.4.1 Barrier Method
124(6)
4.4.2 Primal-Dual Interior Point Method
130(8)
4.5 Gradient Descent Method
138(13)
Problems
146(3)
References
149(2)
5 Machine Learning
151(44)
5.1 Artificial Intelligence, Machine Learning, and Deep Learning
152(1)
5.2 Supervised and Unsupervised Learning
153(24)
5.3 Reinforcement Learning
177(18)
Problems
191(2)
References
193(2)
Part II Design and Optimization for 5G Wireless Communications and Networks
195(202)
6 Design Principles for 5G Communications and Networks
197(42)
6.1 New Design Approaches and Key Challenges of 5G Communications and Networks
198(9)
6.1.1 5G Frequency Bands
198(1)
6.1.2 Low Latency
199(2)
6.1.3 More Efficient Radio Resource Utilization
201(1)
6.1.4 Small Cells and Ultra-Dense Networks
202(1)
6.1.5 Higher Flexibility
202(1)
6.1.6 Virtualization
203(1)
6.1.7 Distributed Network Architecture
204(1)
6.1.8 Device-Centric Communications
205(1)
6.1.9 New Air Interfaces
206(1)
6.1.10 Big Data Management
206(1)
6.2 5G New Radio
207(19)
6.2.1 5G Radio Access Network Architecture
207(1)
6.2.2 5G NR Deployment Scenarios
208(1)
6.2.3 Frame Structure
209(4)
6.2.4 5G Logical, Transport, and Physical Channels
213(4)
6.2.5 5G Protocol Layers
217(3)
6.2.6 5G NR Physical Layer Processing
220(2)
6.2.7 5G Initial Access Procedure and Beam Management
222(4)
6.3 5G Key Enabling Techniques
226(13)
6.3.1 5G Waveforms
226(1)
6.3.2 5G Multiple Access Schemes
227(1)
6.3.3 Channel Coding Schemes
228(2)
6.3.4 MIMO
230(1)
6.3.5 mmWAVE
231(1)
6.3.6 Network Slicing
232(1)
6.3.7 Multi-access Edge Computing
232(3)
Problems
235(2)
References
237(2)
7 Enhanced Mobile Broadband Communication Systems
239(64)
7.1 Introduction
239(1)
7.2 Design Approaches of eMBB Systems
240(2)
7.3 MIMO
242(29)
7.3.1 Capacity of MIMO Channel
243(8)
7.3.2 Space-Time Coding Design
251(11)
7.3.3 Spatial Multiplexing Design
262(6)
7.3.4 Massive MIMO
268(3)
7.4 5G Multiple Access Techniques
271(13)
7.4.1 OFDM System Design
271(9)
7.4.2 FBMC, GFDM, and UFMC
280(4)
7.5 5G Channel Coding and Modulation
284(19)
7.5.1 LDPC Codes
285(6)
7.5.2 Coding and Modulation for High Spectral Efficiency
291(8)
Problems
299(1)
References
300(3)
8 Ultra-Reliable and Low Latency Communication Systems
303(40)
8.1 Design Approaches of URLLC Systems
304(2)
8.2 Short Packet Transmission
306(11)
8.3 Latency Analysis
317(11)
8.4 Multi-Access Edge Computing
328(15)
Problems
339(1)
References
340(3)
9 Massive Machine Type Communication Systems
343(54)
9.1 Introduction
343(1)
9.2 Design Approaches of mMTC Systems
344(7)
9.3 Robust Optimization
351(11)
9.4 Power Control and Management
362(14)
9.4.1 Linear Programming for Power Control in Distributed Networks
363(3)
9.4.2 Power Control Problem Formulations
366(4)
9.4.3 Beamforming for Transmit Power Minimization
370(6)
9.5 Wireless Sensor Networks
376(21)
Problems
392(1)
References
393(4)
Index 397
DR. HAESIK KIM (IEEE Senior Member, Series Editor and Associate Technical Editor of IEEE Communications Magazine) is Senior Scientist of 5G and beyond network team in VTT Technical Research Centre of Finland. He is the recipient of the International Conference on Wireless Communications and Signal Processing (WCSP) Best Paper Award in 2010. His current research interests include PHY and MAC layer system design, advanced coding theory, advanced MIMO, multi-carrier system, interference mitigation techniques, resource allocation schemes, machine-type communications, ultra-reliable low latency communications, and machine learning.
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