Design and Optimization for 5G Wireless Communications

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.

• Preface xiList of Abbreviations xiiiPart I Mathematical Methods and Optimization Theories for Wireless Communications 11 Historical Sketch of Cellular Communications and Networks 31.1 Evolution of Cellular Communications and Networks 31.2 Evolution to 5G Networks 9References 112 5G Wireless Communication System Parameters and Requirements 132.1 5G Requirements 132.2 Trade-off of 5G System Metrics 16Problems 19References 203 Mathematical Methods for Wireless Communications 213.1 Signal Spaces 213.2 Approximation and Estimation in Signal Spaces 323.2.1 Approximation Problems 323.2.2 Least Squares Estimation 353.2.3 Minimum Mean-Squared Error Estimation 453.2.4 Maximum Likelihood and Maximum A Posteriori Estimation 653.3 Matrix Factorization 713.3.1 LU Decomposition 713.3.2 Cholesky Decomposition 763.3.3 QR Decomposition 773.3.4 SVD Decomposition 85Problems 92References 954 Mathematical Optimization Techniques for Wireless Communications 974.1 Introduction 974.2 Mathematical Modeling and Optimization Process 994.3 Linear Programming 1084.4 Convex Optimization 1204.4.1 Barrier Method 1244.4.2 Primal-Dual Interior Point Method 1304.5 Gradient Descent Method 138Problems 146References 1495 Machine Learning 1515.1 Artificial Intelligence, Machine Learning, and Deep Learning 1525.2 Supervised and Unsupervised Learning 1535.3 Reinforcement Learning 177Problems 191References 193Part II Design and Optimization for 5G Wireless Communications and Networks 1956 Design Principles for 5G Communications and Networks 1976.1 New Design Approaches and Key Challenges of 5G Communications and Networks 1986.1.1 5G Frequency Bands 1986.1.2 Low Latency 1996.1.3 More Efficient Radio Resource Utilization 2016.1.4 Small Cells and Ultra-Dense Networks 2026.1.5 Higher Flexibility 2026.1.6 Virtualization 2036.1.7 Distributed Network Architecture 2046.1.8 Device-Centric Communications 2056.1.9 New Air Interfaces 2066.1.10 Big Data Management 2066.2 5G New Radio 2076.2.1 5G Radio Access Network Architecture 2076.2.2 5G NR Deployment Scenarios 2086.2.3 Frame Structure 2096.2.4 5G Logical, Transport, and Physical Channels 2136.2.5 5G Protocol Layers 2176.2.6 5G NR Physical Layer Processing 2206.2.7 5G Initial Access Procedure and Beam Management 2226.3 5G Key Enabling Techniques 2266.3.1 5GWaveforms 2266.3.2 5G Multiple Access Schemes 2276.3.3 Channel Coding Schemes 2286.3.4 MIMO 2306.3.5 mmWAVE 2316.3.6 Network Slicing 2326.3.7 Multi-access Edge Computing 232Problems 235References 2377 Enhanced Mobile Broadband Communication Systems 2397.1 Introduction 2397.2 Design Approaches of eMBB Systems 2407.3 MIMO 2427.3.1 Capacity of MIMO Channel 2437.3.2 Space–Time Coding Design 2517.3.3 Spatial Multiplexing Design 2627.3.4 Massive MIMO 2687.4 5G Multiple Access Techniques 2717.4.1 OFDM System Design 2717.4.2 FBMC, GFDM, and UFMC 2807.5 5G Channel Coding and Modulation 2847.5.1 LDPC Codes 2857.5.2 Coding and Modulation for High Spectral Efficiency 291Problems 299References 3008 Ultra-Reliable and Low Latency Communication Systems 3038.1 Design Approaches of URLLC Systems 3048.2 Short Packet Transmission 3068.3 Latency Analysis 3178.4 Multi-Access Edge Computing 328Problems 339References 3409 Massive Machine Type Communication Systems 3439.1 Introduction 3439.2 Design Approaches of mMTC Systems 3449.3 Robust Optimization 3519.4 Power Control and Management 3629.4.1 Linear Programming for Power Control in Distributed Networks 3639.4.2 Power Control Problem Formulations 3669.4.3 Beamforming for Transmit Power Minimization 3709.5 Wireless Sensor Networks 376Problems 392References 393Index 397