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 (e MBB), Ultra-Reliable and Low Latency Communication (URLL), and Massive Machine Type Communication (m MTC).
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.
Table des matières
Preface
List of Abbreviations
Part I. Mathematic Methods and Optimization Theories for Wireless Communications
1 Historical Sketch of Cellular Communications and Networks
1.1 Evolution of Cellular Communications and Networks 10
1.2 Evoluation to 5G Networks 10
1.3 References 10
2 5G Wireless Communication System Parameters and Requirements 10
2.1 5G Requirements 10
2.2 Trade-off of 5G System Metrics Density 10
2.3 Problems 10
2.4 References 10
3 Mathematical Methods for Wireless Communications 10
3.1 Signal Spaces 10
3.2 Approximation and Estimation in Signal Spaces 10
3.2.1 Approximation Problems 10
3.2.2 Lest Squares Estimation 10
3.2.3 Minimum Mean Squared Error Estimation 10
3.2.4 ML and MAP Estimation 10
3.3 Matrix Factorization 10
3.3.1 LU Decomposition 10
3.3.2 Cholesky Decomposition 10
3.3.3 QR Decomposition 10
3.3.4 SVD Decomposition 10
3.4 Problems 10
3.5 References 10
4 Mathematical Optimization Techniques for Wireless Communications 10
4.1 Mathematical Modelling and Optimiation Process 10
4.2 Linear Programming 10
4.3 Convex Optimization 10
4.3.1 Barrier Method 10
4.3.2 Primal-Dual Interiro Point Method 10
4.4 Gradient Descent Method 10
4.5 Problems 10
4.6 References 10
5 Machine Learning 10
5.1 Artificial Intelligence, Machine Learning and Deep Learning 10
5.2 Supervised and Unsupervised Learning 10
5.3 Reinforcement Learning 10
5.4 Problems 10
5.5 References 10
Part II. Design and Optimization for 5G Wireless Communications and Networks 1
6 Design Principles for 5G Communications and Networks 10
6.1 New Design Approaches and Key Challenges of 5G Communications and Networks 10
6.2 5G New Radio 10
6.3 5G Key Enabling Techniques 10
6.4 Problems 10
6.5 References 10
7 Enhanced Mobile Broadband Communication Systems 10
7.1 Design Approaches of e MBB Systems 10
7.2 MIMO 10
7.2.1 Capacity of MIMO Channel 10
7.2.2 Space Time Coding Design 10
7.2.3 Spatial Multiplexing Design 10
7.2.4 Massive MIMO 10
7.3 5G Multiple Access Techniques 10
7.3.1 OFDM System Design 10
7.3.2 FBMC, GFDM and UFMC 10
7.4 5G Channel Coding and Modulation 10
7.4.1 LDPC Codes 10
7.4.2 Coding and Modulation for High Spectral Efficiency 10
7.5 Problems 10
7.6 References 10
8 Ultra-Reliable and Low Latency Communication Systems 10
8.1 Design Approaches of URLLC Systems 10
8.2 Short Packet Transmission 10
8.3 Latency Analysis 10
8.4 Multiple Access Edge Computing 10
8.5 Problems 10
8.6 References 10
9 Massive Machine Type Communication Systems 10
9.1 Design Approaches of m MTC Systems 10
9.2 Robust Optimization 10
9.3 Power Control and Management 10
9.4 Wireless Sensor Networks 10
9.5 Problems 10
9.6 References 10Index 1
A propos de l’auteur
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.