000			00619nam a2200193Ia 4500
999			_c62529 _d62529
003			OSt
005			20181023043908.0
008			24419b pe ||||| |||| 00| 0 spa d
040			_cTranscribing agency
082			_a621.38456.KIM.00
100			_aKim, Haesik
100			_eAutor
245		0	_aDesign And Optimization For 5G Wireless Communications
250			_a1A. ed
260			_aINDIA
260			_bWILEY
260			_c2020
300			_a400
300			_c25.0
505			_aPreface . -- List Of Abbreviations . -- Part I Mathematical Methods And Optimization Theories For Wireless Communications . -- 1 Historical Sketch Of Cellular Communications And Networks . -- 1.1 Evolution Of Cellular Communications And Networks . -- 1.2 Evolution To 5G Networks . -- References . -- 2. 5G Wireless Communication System Parameters And Requirements . -- 2.1 5G Requirements . -- 2.2 Trade-Off Of 5G System Metrics . -- Problems . -- References . -- 3 Mathematical Methods For Wireless Communications . -- 3.1 Signal Spaces . -- 3.2 Approximation And Estimation In Signal Spaces . -- 3.2.1 Approximation Problems . -- 3.2.2 Least Squares Estimation . -- 3.2.3 Minimum Mean-Squared Error Estimation . -- 3.2.4 Maximum Likelihood And Maximum a Posteriori Estimation . -- 3.3 Matrix Factorization . -- 3.3.1 Lu Decomposition . -- 3.3.2 Cholesky Decomposition . -- 3.3.3 Qr Decomposition . -- 3.3.4 Svd Decomposition . -- Problems . -- References . -- 4 Mathematical Optimization Techniques For Wireless Communications . -- 4.1 Introduction . -- 4.2 Mathematical Modeling And Optimization Process . -- 4.3 Linear Programming . -- 4.4 Convex Optimization . -- 4.4.1 Barrier Method . -- 4.4.2 Primal-Dual Interior Point Method . -- 4.5 Gradient Descent Method . -- Problems . -- References . -- 5 Machine Learning . -- 5.1 Artificial Intelligence, Machine Learning, And Deep Learning . -- 5.2 Supervised And Unsupervised Learning 5.3 Reinforcement Learning Problems References Part Ii Design And Optimization For 5G Wireless Communications And Networks 6 Design Principles For 5G Communications And Networks 6.1 New Design Approaches And Key Challenges Of 5G Communications And Networks 6.1.1 5G Frequency Bands 6.1.2 Low Latency 6.1.3 More Efficient Radio Resource Utilization 6.1.4 Small Cells And Ultra-Dense Networks 6.1.5 Higher Flexibility 6.1.6 Virtualization 6.1.7 Distributed Network Architecture 6.1.8 Device-Centric Communications 6.1.9 New Air Interfaces 6.1.10 Big Data Management 6.2 5G New Radio 6.2.1 5G Radio Access Network Architecture 6.2.2 5G Nr Deployment Scenarios 6.2.3 Frame Structure 6.2.4 5G Logical, Transport, And Physical Channels 6.2.5 5G Protocol Layers 6.2.6 5G Nr Physical Layer Processing 6.2.7 5G Initial Access Procedure And Beam Management 6.3 5G Key Enabling Techniques 6.3.1 5Gwaveforms 6.3.2 5G Multiple Access Schemes 6.3.3 Channel Coding Schemes 6.3.4 Mimo 6.3.5 Mmwave 6.3.6 Network Slicing 6.3.7 Multi-Access Edge Computing Problems References 7 Enhanced Mobile Broadband Communication Systems 7.1 Introduction 7.2 Design Approaches Of Embb Systems 7.3 Mimo 7.3.1 Capacity Of Mimo Channel 7.3.2 Space–Time Coding Design 7.3.3 Spatial Multiplexing Design 7.3.4 Massive Mimo 7.4 5G Multiple Access Techniques 7.4.1 Ofdm System Design 7.4.2 Fbmc, Gfdm, And Ufmc 7.5 5G Channel Coding And Modulation 7.5.1 Ldpc Codes 7.5.2 Coding And Modulation For High Spectral Efficiency Problems References . -- 8 Ultra-Reliable And Low Latency Communication Systems 8.1 Design Approaches Of Urllc Systems 8.2 Short Packet Transmission 8.3 Latency Analysis 8.4 Multi-Access Edge Computing Problems References 9 Massive Machine Type Communication Systems 9.1 Introduction 9.2 Design Approaches Of Mmtc Systems 9.3 Robust Optimization 9.4 Power Control And Management 9.4.1 Linear Programming For Power Control In Distributed Networks 9.4.2 Power Control Problem Formulations 9.4.3 Beamforming For Transmit Power Minimization 9.5 Wireless Sensor Networks Problems References Index
650			_aOPTIMIZACIÓN DE SISTEMAS
650			_aCOMUNICACIÓN INALÁMBRICA
650			_aMÉTODOS MATEMÁTICOS
650			_aALGORITMOS
700			_aKim, Haesik
942			_cTM001 _2ddc