LEADER 10912nam 2200685 450 001 9910155224703321 005 20231026155808.0 010 $a1-119-20683-9 010 $a1-119-20682-0 010 $a1-119-20684-7 024 7 $a10.1002/9781119206842 035 $a(CKB)4330000000009309 035 $a(CaBNVSL)mat07753055 035 $a(IDAMS)0b0000648585c6ca 035 $a(IEEE)7753055 035 $a(DLC) 2016048578 035 $a(Au-PeEL)EBL4772131 035 $a(CaPaEBR)ebr11319578 035 $a(CaONFJC)MIL979364 035 $a(OCoLC)967521566 035 $a(MiAaPQ)EBC4772131 035 $a(PPN)27177066X 035 $a(EXLCZ)994330000000009309 100 $a20170209d2016 uy 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aEnterprise content and search management for building digital platforms /$fShailesh Shivakumar 210 1$aHoboken :$cWiley,$d2016. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2016] 215 $a1 online resource (472 pages) $cillustrations 225 0 $aTHEi Wiley ebooks. 300 $aIncludes index. 311 $a1-119-20681-2 320 $aIncludes bibliographical references and index. 327 $aPreface xvii -- Acknowledgments xxvii -- About the Author xxix -- About the Companion Website xxxi -- Part 1 Content Management Basics for Digital Platforms -- 1 Introduction to Digital Platforms 3 -- 1.1 Enterprise Digital Ecosystem 4 -- 1.2 Concepts of Enterprise Content Management (ECM) 15 -- 1.3 Enterprise Digital Strategy and Content Strategy 20 -- 1.4 Digital Content Management and Enterprise Search: An Overview 28 -- 1.5 Chapter Summary 30 -- 2 Content Strategy 32 -- 2.1 Overview of Content Strategy 32 -- 2.2 Prerequisites for Content Strategy 38 -- 2.3 Defining Content Strategy 41 -- 2.4 Content Strategy Case Study 73 -- 2.5 Chapter Summary 79 -- 3 Basics of Content Management Systems 82 -- 3.1 What Is a Content Management System? 82 -- 3.2 CMS Key Design Principles 89 -- 3.3 CMS Capabilities and Attributes 92 -- 3.4 Content Lifecycle Management in CMS 98 -- 3.5 A Brief Description of Open Source CMS and JCR 100 -- 3.6 Chapter Summary 102 -- 4 Content Management System Architecture 104 -- 4.1 CMS Design and Architecture 104 -- 4.2 Modern CMS Architecture Patterns 106 -- 4.3 CMS Value Articulation and Solution Principles 113 -- 4.4 CMS Solution Design Principles 114 -- 4.5 Design of CMS Solution Components 118 -- 4.6 CMS Operations Management 130 -- 4.7 Realizing Content Strategy with CMS 137 -- 4.8 CMS Reference Architectures 137 -- 4.9 Chapter Summary 152 -- 5 Development Using Templates and Workflows 154 -- 5.1 CMS Template Design 154 -- 5.2 Authoring Content Using an Authoring Template 160 -- 5.3 Chunking and Templates for Chunks 165 -- 5.4 Template Support among Various CMS 178 -- 5.5 Case Study: Building Content Templates for a Web Support Site 179 -- 5.6 Content Workflows 183 -- 5.7 Case Study: Modeling Workflow for a Knowledge Management System 189 -- 5.8 Chapter Summary 192 -- 6 Content Information Architecture, Taxonomy, and Metadata 195 -- 6.1 Intuitive Information Architecture 196 -- 6.2 Introduction to Taxonomy and Metadata 202 -- 6.3 Metadata Usage in Relevant Content Discovery 208. 327 $a6.4 Integration of Metadata with CMS 208 -- 6.5 Metadata Standards and Formats 210 -- 6.6 Case Study: Content Metadata to Increase Search Effectiveness 212 -- 6.7 Other Utilities of Content Metadata 214 -- 6.8 Taxonomy Governance 216 -- 6.9 Chapter Summary 217 -- Part 2 Advanced Content Management -- 7 Content Integration and Content Standards 221 -- 7.1 Content Integration Requirements 221 -- 7.2 CMS Integration View 222 -- 7.3 CMS Integrations 225 -- 7.4 CMIS-Based Integration 235 -- 7.5 CMS Integration with Other Systems 237 -- 7.6 Content Standards 237 -- 7.7 Chapter Summary 250 -- 8 Digital Asset Management and Document Management 253 -- 8.1 Digital Asset Management (DAM) 254 -- 8.2 Document Management 263 -- 8.3 Chapter Summary 270 -- 9 Content Migration 272 -- 9.1 Content Migration 272 -- 9.2 Chapter Summary 295 -- 10 Content Governance: Validation, Analytics, KPIs, SEO, and Evaluation 297 -- 10.1 Content Validation 298 -- 10.2 Content Analytics and KPIs 304 -- 10.3 Content SEO 312 -- 10.4 CMS Evaluation Framework 315 -- 10.5 Appendix: WCMS Features 322 -- 10.6 Chapter Summary 325 -- 11 Content Security 327 -- 11.1 Content Security Vulnerabilities and Mitigation Steps 327 -- 11.2 Generic Content Security Scenarios 333 -- 11.3 Security Testing 337 -- 11.4 Security Best Practices 339 -- 11.5 Case Study: Security Testing for a CMS Application 344 -- 11.6 Chapter Summary 350 -- 12 Content Infrastructure and Performance Optimization 352 -- 12.1 CMS Infrastructure Architecture 352 -- 12.2 Content Performance Optimization 358 -- 12.3 Content Performance Key Performance Indicators (KPIs) 364 -- 12.4 Content Performance Validation 365 -- 12.5 Content-Related Best Practices 366 -- 12.6 Chapter Summary 373 -- Part 3 Enterprise Search Technologies -- 13 Introduction to Enterprise Search 377 -- 13.1 Introduction to Enterprise Search 378 -- 13.2 Enterprise Search Overview 383 -- 13.3 Enterprise Search capabilities 389 -- 13.4 Enterprise Search Features 392 -- 13.5 Chapter Summary 397. 327 $a14 Advanced Enterprise Search 398 -- 14.1 Federated Search 398 -- 14.2 Advanced Search Features 403 -- 14.3 Enterprise Semantic Search 409 -- 14.4 People Search and Social Search 412 -- 14.5 Mobile Search 415 -- 14.6 Big Data Search 415 -- 14.7 Search Engine Optimization (SEO) 417 -- 14.8 Case Study: Information Management Portal Driven by Apache Solr 422 -- 14.9 Chapter Summary 424 -- Further Reading 427 -- Index 429. 330 $a"Provides modern enterprises with the tools to create a robust digital platform utilizing proven best practices, practical models, and time-tested techniques Contemporary business organizations can either embrace the digital revolution—or be left behind. Enterprise Content and Search Management for Building Digital Platforms provides modern enterprises with the necessary tools to create a robust digital platform utilizing proven best practices, practical models, and time-tested techniques to compete in the today’s digital world. Features include comprehensive discussions on content strategy, content key performance indicators (KPIs), mobile-first strategy, content assessment models, various practical techniques and methodologies successfully used in real-world digital programs, relevant case studies, and more. Initial chapters cover core concepts of a content management system (CMS), including content strategy; CMS architecture, templates, and workflow; reference architectures, information architecture, taxonomy, and content metadata. Advanced CMS topics are then covered, with chapters on integration, content standards, digital asset management (DAM), document management, and content migration, evaluation, validation, maintenance, analytics, SEO, security, infrastructure, and performance. The basics of enterprise search technologies are explored next, and address enterprise search architecture, advanced search, operations, and governance. Final chapters then focus on enterprise program management and feature coverage of various concepts of digital program management and best practices—along with an illuminating end-to-end digital program case study.  Offers a comprehensive guide to the understanding and learning of new methodologies, techniques, and models for the creation of an end-to-end digital system Addresses a wide variety of proven best practices and deployed techniques in content management and enterprise search space which can be readily used for digital programs Covers the latest digital trends such as mobile-first strategy, responsive design, adaptive content design, micro services architecture, semantic search and such and also utilizes sample reference architecture for implementing solutions Features numerous case studies to enhance comprehension, including a complete end-to-end digital program case study Provides readily usable content management checklists and templates for defining content strategy, CMS evaluation, search evaluation and DAM evaluation Comprehensive and cutting-edge, Enterprise Content and Search Management for Building Digital Platforms is an invaluable reference resource for creating an optimal enterprise digital eco-system to meet the challenges of today’s hyper-connected world"--$cProvided by publisher. 330 $a"Contemporary business organizations can either embrace the digital revolution--or be left behind. Enterprise Content and Search Management for Building Digital Platforms provides modern enterprises with the necessary tools to create a robust digital platform utilizing proven best practices, practical models, and time-tested techniques to compete in the today's digital world. Features include comprehensive discussions on content strategy, content key performance indicators (KPIs), mobile-first strategy, content assessment models, various practical techniques and methodologies successfully used in real-world digital programs, relevant case studies, and more. Initial chapters cover core concepts of a content management system (CMS), including content strategy; CMS architecture, templates, and workflow; reference architectures, information architecture, taxonomy, and content metadata. Advanced CMS topics are then covered, with chapters on integration, content standards, digital asset management (DAM), document management, and content migration, evaluation, validation, maintenance, analytics, SEO, security, infrastructure, and performance. The basics of enterprise search technologies are explored next, and address enterprise search architecture, advanced search, operations, and governance. Final chapters then focus on enterprise program management and feature coverage of various concepts of digital program management and best practices--along with an illuminating end-to-end digital program case study"--$cProvided by publisher. 606 $aManagement$xTechnological innovations 606 $aDigital media$xManagement 606 $aMultimedia systems$xManagement 606 $aPerformance technology 615 0$aManagement$xTechnological innovations. 615 0$aDigital media$xManagement. 615 0$aMultimedia systems$xManagement. 615 0$aPerformance technology. 676 $a658.4/038011 686 $aCOM060130$2bisacsh 700 $aShivakumar$b Shailesh Kumar$0849514 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910155224703321 996 $aEnterprise content and search management for building digital platforms$91897065 997 $aUNINA LEADER 22605nam 2200613 450 001 9910813591503321 005 20240219172013.0 010 $a1-119-56231-7 010 $a1-119-56227-9 010 $a1-119-56230-9 024 7 $a10.1002/9781119562306 035 $a(CKB)4100000009952500 035 $a(MiAaPQ)EBC5996888 035 $a(CaBNVSL)mat08958790 035 $a(IDAMS)0b0000648bcb044f 035 $a(IEEE)8958790 035 $a(PPN)271784385 035 $a(OCoLC)1131862601 035 $a(EXLCZ)994100000009952500 100 $a20200313d2019 uy 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning for future wireless communications /$fedited by Fa-Long Luo 210 1$aHoboken, New Jersey :$cWiley-IEEE,$d2020. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2019] 215 $a1 online resource (493 pages) 311 $a1-119-56225-2 320 $aIncludes bibliographical references and index. 327 $aList of Contributors xv -- Preface xxi -- Part I Spectrum Intelligence and Adaptive Resource Management 1 -- 1 Machine Learning for Spectrum Access and Sharing 3 /Kobi Cohen -- 1.1 Introduction 3 -- 1.2 Online Learning Algorithms for Opportunistic Spectrum Access 4 -- 1.2.1 The Network Model 4 -- 1.2.2 Performance Measures of the Online Learning Algorithms 5 -- 1.2.3 The Objective 6 -- 1.2.4 Random and Deterministic Approaches 6 -- 1.2.5 The Adaptive Sequencing Rules Approach 7 -- 1.2.5.1 Structure of Transmission Epochs 7 -- 1.2.5.2 Selection Rule under the ASR Algorithm 8 -- 1.2.5.3 High-Level Pseudocode and Implementation Discussion 9 -- 1.3 Learning Algorithms for Channel Allocation 9 -- 1.3.1 The Network Model 10 -- 1.3.2 Distributed Learning, Game-Theoretic, and Matching Approaches 11 -- 1.3.3 Deep Reinforcement Learning for DSA 13 -- 1.3.3.1 Background on Q-learning and Deep Reinforcement Learning (DRL): 13 -- 1.3.4 Existing DRL-Based Methods for DSA 14 -- 1.3.5 Deep Q-Learning for Spectrum Access (DQSA) Algorithm 15 -- 1.3.5.1 Architecture of the DQN Used in the DQSA Algorithm 15 -- 1.3.5.2 Training the DQN and Online Spectrum Access 16 -- 1.3.5.3 Simulation Results 17 -- 1.4 Conclusions 19 -- Acknowledgments 20 -- Bibliography 20 -- 2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27 /Andres Kwasinski, Wenbo Wang, and Fatemeh Shah Mohammadi -- 2.1 Use of Q-Learning for Cross-layer Resource Allocation 29 -- 2.2 Deep Q-Learning and Resource Allocation 33 -- 2.3 Cooperative Learning and Resource Allocation 36 -- 2.4 Conclusions 42 -- Bibliography 43 -- 3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 45 /Hadi Ghauch, Hossein Shokri-Ghadikolaei, Gabor Fodor, Carlo Fischione, and Mikael Skoglund -- 3.1 Background and Motivation 45 -- 3.1.1 Review of Cellular Network Evolution 45 -- 3.1.2 Millimeter-Wave and Large-Scale Antenna Systems 46 -- 3.1.3 Review of Spectrum Sharing 47 -- 3.1.4 Model-Based vs. Data-Driven Approaches 48. 327 $a3.2 System Model and Problem Formulation 49 -- 3.2.1 Models 49 -- 3.2.1.1 Network Model 49 -- 3.2.1.2 Association Model 49 -- 3.2.1.3 Antenna and Channel Model 49 -- 3.2.1.4 Beamforming and Coordination Models 50 -- 3.2.1.5 Coordination Model 50 -- 3.2.2 Problem Formulation 51 -- 3.2.2.1 Rate Models 52 -- 3.2.3 Model-based Approach 52 -- 3.2.4 Data-driven Approach 53 -- 3.3 Hybrid Solution Approach 54 -- 3.3.1 Data-Driven Component 55 -- 3.3.2 Model-Based Component 56 -- 3.3.2.1 Illustrative Numerical Results 58 -- 3.3.3 Practical Considerations 58 -- 3.3.3.1 Implementing Training Frames 58 -- 3.3.3.2 Initializations 59 -- 3.3.3.3 Choice of the Penalty Matrix 59 -- 3.4 Conclusions and Discussions 59 -- Appendix A Appendix for Chapter 3 61 -- A.1 Overview of Reinforcement Learning 61 -- Bibliography 61 -- 4 Deep Learning?o?Based Coverage and Capacity Optimization 63 /Andrei Marinescu, Zhiyuan Jiang, Sheng Zhou, Luiz A. DaSilva, and Zhisheng Niu -- 4.1 Introduction 63 -- 4.2 Related Machine Learning Techniques for Autonomous Network Management 64 -- 4.2.1 Reinforcement Learning and Neural Networks 64 -- 4.2.2 Application to Mobile Networks 66 -- 4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning 67 -- 4.3.1 Deep Reinforcement Learning Architecture 67 -- 4.3.2 Deep Q-Learning Preliminary 68 -- 4.3.3 Applications to BS Sleeping Control 68 -- 4.3.3.1 Action-Wise Experience Replay 69 -- 4.3.3.2 Adaptive Reward Scaling 70 -- 4.3.3.3 Environment Models and Dyna Integration 70 -- 4.3.3.4 DeepNap Algorithm Description 71 -- 4.3.4 Experiments 71 -- 4.3.4.1 Algorithm Comparisons 71 -- 4.3.5 Summary 72 -- 4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach 72 -- 4.4.1 Multi-Agent System Architecture 73 -- 4.4.1.1 Cell Agent Architecture 75 -- 4.4.2 Application to Fractional Frequency Reuse 75 -- 4.4.3 Scenario Implementation 76 -- 4.4.3.1 Cell Agent Neural Network 76 -- 4.4.4 Evaluation 78 -- 4.4.4.1 Neural Network Performance 78. 327 $a4.4.4.2 Multi-Agent System Performance 79 -- 4.4.5 Summary 81 -- 4.5 Conclusions 81 -- Bibliography 82 -- 5 Machine Learning for Optimal Resource Allocation 85 /Marius Pesavento and Florian Bahlke -- 5.1 Introduction and Motivation 85 -- 5.1.1 Network Capacity and Densification 86 -- 5.1.2 Decentralized Resource Minimization 87 -- 5.1.3 Overview 88 -- 5.2 System Model 88 -- 5.2.1 Heterogeneous Wireless Networks 88 -- 5.2.2 Load Balancing 89 -- 5.3 Resource Minimization Approaches 90 -- 5.3.1 Optimized Allocation 91 -- 5.3.2 Feature Selection and Training 91 -- 5.3.3 Range Expansion Optimization 93 -- 5.3.4 Range Expansion Classifier Training 94 -- 5.3.5 Multi-Class Classification 94 -- 5.4 Numerical Results 96 -- 5.5 Concluding Remarks 99 -- Bibliography 100 -- 6 Machine Learning in Energy Efficiency Optimization 105 /Muhammad Ali Imran, Ana Flavia dos Reis, Glauber Brante, Paulo Valente Klaine, and Richard Demo Souza -- 6.1 Self-Organizing Wireless Networks 106 -- 6.2 Traffic Prediction and Machine Learning 110 -- 6.3 Cognitive Radio and Machine Learning 111 -- 6.4 Future Trends and Challenges 112 -- 6.4.1 Deep Learning 112 -- 6.4.2 Positioning of Unmanned Aerial Vehicles 113 -- 6.4.3 Learn-to-Optimize Approaches 113 -- 6.4.4 Some Challenges 114 -- 6.5 Conclusions 114 -- Bibliography 114 -- 7 Deep Learning Based Traffic and Mobility Prediction 119 /Honggang Zhang, Yuxiu Hua, Chujie Wang, Rongpeng Li, and Zhifeng Zhao -- 7.1 Introduction 119 -- 7.2 Related Work 120 -- 7.2.1 Traffic Prediction 120 -- 7.2.2 Mobility Prediction 121 -- 7.3 Mathematical Background 122 -- 7.4 ANN-Based Models for Traffic and Mobility Prediction 124 -- 7.4.1 ANN for Traffic Prediction 124 -- 7.4.1.1 Long Short-Term Memory Network Solution 124 -- 7.4.1.2 Random Connectivity Long Short-Term Memory Network Solution 125 -- 7.4.2 ANN for Mobility Prediction 128 -- 7.4.2.1 Basic LSTM Network for Mobility Prediction 128 -- 7.4.2.2 Spatial-Information-Assisted LSTM-Based Framework of Individual Mobility Prediction 130. 327 $a7.4.2.3 Spatial-Information-Assisted LSTM-Based Framework of Group Mobility Prediction 131 -- 7.5 Conclusion 133 -- Bibliography 134 -- 8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137 /Benjamin Sliwa, Robert Falkenberg, and Christian Wietfeld -- 8.1 Mobile Crowdsensing 137 -- 8.1.1 Applications and Requirements 138 -- 8.1.2 Anticipatory Data Transmission 139 -- 8.2 ML-Based Context-Aware Data Transmission 140 -- 8.2.1 Groundwork: Channel-aware Transmission 140 -- 8.2.2 Groundwork: Predictive CAT 142 -- 8.2.3 ML-based CAT 144 -- 8.2.4 ML-based pCAT 146 -- 8.3 Methodology for Real-World Performance Evaluation 148 -- 8.3.1 Evaluation Scenario 148 -- 8.3.2 Power Consumption Analysis 148 -- 8.4 Results of the Real-World Performance Evaluation 149 -- 8.4.1 Statistical Properties of the Network Quality Indicators 149 -- 8.4.2 Comparison of the Transmission Schemes 149 -- 8.4.3 Summary 151 -- 8.5 Conclusion 152 -- Acknowledgments 154 -- Bibliography 154 -- Part II Transmission Intelligence and Adaptive Baseband Processing 157 -- 9 Machine Learning?o?Based Adaptive Modulation and Coding Design 159 /Lin Zhang and Zhiqiang Wu -- 9.1 Introduction and Motivation 159 -- 9.1.1 Overview of ML-Assisted AMC 160 -- 9.1.2 MCS Schemes Specified by IEEE 802.11n 161 -- 9.2 SL-Assisted AMC 162 -- 9.2.1 k-NN-Assisted AMC 162 -- 9.2.1.1 Algorithm for k-NN-Assisted AMC 163 -- 9.2.2 Performance Analysis of k-NN-Assisted AMC System 164 -- 9.2.3 SVM-Assisted AMC 166 -- 9.2.3.1 SVM Algorithm 166 -- 9.2.3.2 Simulation and Results 170 -- 9.3 RL-Assisted AMC 172 -- 9.3.1 Markov Decision Process 172 -- 9.3.2 Solution for the Markov Decision 173 -- 9.3.3 Actions, States, and Rewards 174 -- 9.3.4 Performance Analysis and Simulations 175 -- 9.4 Further Discussion and Conclusions 178 -- Bibliography 178 -- 10 Machine Learning?o?Based Nonlinear MIMO Detector 181 /Song-Nam Hong and Seonho Kim -- 10.1 Introduction 181 -- 10.2 A Multihop MIMO Channel Model 182 -- 10.3 Supervised-Learning-based MIMO Detector 184. 327 $a10.3.1 Non-Parametric Learning 184 -- 10.3.2 Parametric Learning 185 -- 10.4 Low-Complexity SL (LCSL) Detector 188 -- 10.5 Numerical Results 191 -- 10.6 Conclusions 193 -- Bibliography 193 -- 11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach 197 /Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahario Yukawa, and Slawomir Stanczak -- 11.1 Introduction 197 -- 11.2 Preliminaries 198 -- 11.2.1 Reproducing Kernel Hilbert Spaces 198 -- 11.2.2 Sum Spaces of Reproducing Kernel Hilbert Spaces 199 -- 11.3 System Model 200 -- 11.3.1 Symbol Detection in Multiuser Environments 201 -- 11.3.2 Detection of Complex-Valued Symbols in Real Hilbert Spaces 202 -- 11.4 The Proposed Learning Algorithm 203 -- 11.4.1 The Canonical Iteration 203 -- 11.4.2 Practical Issues 204 -- 11.4.3 Online Dictionary Learning 205 -- 11.4.3.1 Dictionary for the Linear Component 206 -- 11.4.3.2 Dictionary for the Gaussian Component 206 -- 11.4.4 The Online Learning Algorithm 206 -- 11.5 Simulation 207 -- 11.6 Conclusion 208 -- Appendix A Derivation of the Sparsification Metric and the Projections onto the Subspace Spanned by the Nonlinear Dictionary 210 -- Bibliography 211 -- 12 Machine Learning for Joint Channel Equalization and Signal Detection 213 /Lin Zhang and Lie-Liang Yang -- 12.1 Introduction 213 -- 12.2 Overview of Neural Network-Based Channel Equalization 214 -- 12.2.1 Multilayer Perceptron-Based Equalizers 215 -- 12.2.2 Functional Link Artificial Neutral Network-Based Equalizers 215 -- 12.2.3 Radial Basis Function-Based Equalizers 216 -- 12.2.4 Recurrent Neural Networks-Based Equalizers 216 -- 12.2.5 Self-Constructing Recurrent Fuzzy Neural Network-Based Equalizers 217 -- 12.2.6 Deep-Learning-Based Equalizers 217 -- 12.2.7 Extreme Learning Machine?o?Based Equalizers 218 -- 12.2.8 SVM- and GPR-Based Equalizers 218 -- 12.3 Principles of Equalization and Detection 219 -- 12.4 NN-Based Equalization and Detection 223 -- 12.4.1 Multilayer Perceptron Model 223. 327 $a12.4.1.1 Generalized Multilayer Perceptron Structure 224 -- 12.4.1.2 Gradient Descent Algorithm 225 -- 12.4.1.3 Forward and Backward Propagation 226 -- 12.4.2 Deep-Learning Neural Network-Based Equalizers 227 -- 12.4.2.1 System Model and Network Structure 227 -- 12.4.2.2 Network Training 228 -- 12.4.3 Convolutional Neural Network-Based Equalizers 229 -- 12.4.4 Recurrent Neural Network-Based Equalizers 231 -- 12.5 Performance of OFDM Systems With Neural Network-Based Equalization 232 -- 12.5.1 System Model and Network Structure 232 -- 12.5.2 DNN and CNN Network Structure 233 -- 12.5.3 Offline Training and Online Deployment 234 -- 12.5.4 Simulation Results and Analyses 235 -- 12.6 Conclusions and Discussion 236 -- Bibliography 237 -- 13 Neural Networks for Signal Intelligence: Theory and Practice 243 /Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, and Tommaso Melodia -- 13.1 Introduction 243 -- 13.2 Overview of Artificial Neural Networks 244 -- 13.2.1 Feedforward Neural Networks 244 -- 13.2.2 Convolutional Neural Networks 247 -- 13.3 Neural Networks for Signal Intelligence 248 -- 13.3.1 Modulation Classification 249 -- 13.3.2 Wireless Interference Classification 252 -- 13.4 Neural Networks for Spectrum Sensing 255 -- 13.4.1 Existing Work 256 -- 13.4.2 Background on System-on-Chip Computer Architecture 256 -- 13.4.3 A Design Framework for Real-Time RF Deep Learning 257 -- 13.4.3.1 High-Level Synthesis 257 -- 13.4.3.2 Design Steps 258 -- 13.5 Open Problems 259 -- 13.5.1 Lack of Large-Scale Wireless Signal Datasets 259 -- 13.5.2 Choice of I/Q Data Representation Format 259 -- 13.5.3 Choice of Learning Model and Architecture 260 -- 13.6 Conclusion 260 -- Bibliography 260 -- 14 Channel Coding with Deep Learning: An Overview 265 /Shugong Xu -- 14.1 Overview of Channel Coding and Deep Learning 265 -- 14.1.1 Channel Coding 265 -- 14.1.2 Deep Learning 266 -- 14.2 DNNs for Channel Coding 268 -- 14.2.1 Using DNNs to Decode Directly 269 -- 14.2.2 Scaling DL Method 271. 327 $a14.2.3 DNNs for Joint Equalization and Channel Decoding 272 -- 14.2.4 A Unified Method to Decode Multiple Codes 274 -- 14.2.5 Summary 276 -- 14.3 CNNs for Decoding 277 -- 14.3.1 Decoding by Eliminating Correlated Channel Noise 277 -- 14.3.1.1 BP-CNN Reduces Decoding BER 279 -- 14.3.1.2 Multiple Iterations Between CNN and BP Further Improve Performance 279 -- 14.3.2 Summary 279 -- 14.4 RNNs for Decoding 279 -- 14.4.1 Using RNNs to Decode Sequential Codes 279 -- 14.4.2 Improving the Standard BP Algorithm with RNNs 281 -- 14.4.3 Summary 283 -- 14.5 Conclusions 283 -- Bibliography 283 -- 15 Deep Learning Techniques for Decoding Polar Codes 287 /Warren J. Gross, Nghia Doan, Elie Ngomseu Mambou, and Seyyed Ali Hashemi -- 15.1 Motivation and Background 287 -- 15.2 Decoding of Polar Codes: An Overview 289 -- 15.2.1 Problem Formulation of Polar Codes 289 -- 15.2.2 Successive-Cancellation Decoding 290 -- 15.2.3 Successive-Cancellation List Decoding 291 -- 15.2.4 Belief Propagation Decoding 291 -- 15.3 DL-Based Decoding for Polar Codes 292 -- 15.3.1 Off-the-Shelf DL Decoders for Polar Codes 292 -- 15.3.2 DL-Aided Decoders for Polar Codes 293 -- 15.3.2.1 Neural Belief Propagation Decoders 293 -- 15.3.2.2 Joint Decoder and Noise Estimator 295 -- 15.3.3 Evaluation 296 -- 15.4 Conclusions 299 -- Bibliography 299 -- 16 Neural Network?o?Based Wireless Channel Prediction 303 /Wei Jiang, Hans Dieter Schotten, and Ji-ying Xiang -- 16.1 Introduction 303 -- 16.2 Adaptive Transmission Systems 305 -- 16.2.1 Transmit Antenna Selection 305 -- 16.2.2 Opportunistic Relaying 306 -- 16.3 The Impact of Outdated CSI 307 -- 16.3.1 Modeling Outdated CSI 307 -- 16.3.2 Performance Impact 308 -- 16.4 Classical Channel Prediction 309 -- 16.4.1 Autoregressive Models 310 -- 16.4.2 Parametric Models 311 -- 16.5 NN-Based Prediction Schemes 313 -- 16.5.1 The RNN Architecture 313 -- 16.5.2 Flat-Fading SISO Prediction 314 -- 16.5.2.1 Channel Gain Prediction with a Complex-Valued RNN 314 -- 16.5.2.2 Channel Gain Prediction with a Real-Valued RNN 315. 327 $a16.5.2.3 Channel Envelope Prediction 315 -- 16.5.2.4 Multi-Step Prediction 316 -- 16.5.3 Flat-Fading MIMO Prediction 316 -- 16.5.3.1 Channel Gain Prediction 317 -- 16.5.3.2 Channel Envelope Prediction 317 -- 16.5.4 Frequency-Selective MIMO Prediction 317 -- 16.5.5 Prediction-Assisted MIMO-OFDM 319 -- 16.5.6 Performance and Complexity 320 -- 16.5.6.1 Computational Complexity 320 -- 16.5.6.2 Performance 321 -- 16.6 Summary 323 -- Bibliography 323 -- Part III Network Intelligence and Adaptive System Optimization 327 -- 17 Machine Learning for Digital Front-End: a Comprehensive Overview 329 /Pere L. Gilabert, David Lopez-Bueno, Thi Quynh Anh Pham, and Gabriel Montoro -- 17.1 Motivation and Background 329 -- 17.2 Overview of CFR and DPD 331 -- 17.2.1 Crest Factor Reduction Techniques 331 -- 17.2.2 Power Amplifier Behavioral Modeling 334 -- 17.2.3 Closed-Loop Digital Predistortion Linearization 335 -- 17.2.4 Regularization 337 -- 17.2.4.1 Ridge Regression or Tikhonov ]]>?o??o?