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6G key technologies : a comprehensive guide / / Wei Jiang and Fa-Long Luo
6G key technologies : a comprehensive guide / / Wei Jiang and Fa-Long Luo
Autore Jiang Wei (Writer on 6G networks)
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]
Descrizione fisica 1 online resource (579 pages)
Disciplina 621.384
Collana IEEE Press
Soggetto topico Wireless communication systems
ISBN 1-119-84750-8
1-119-84748-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Preface -- List of Abbreviations -- Part I The Vision of 6G and Technical Evolution -- Chapter 1 Standards History of Cellular Systems Toward 6G -- 1.1 0G: Pre‐Cellular Systems -- 1.2 1G: The Birth of Cellular Network -- 1.2.1 Nordic Mobile Telephone (NMT) -- 1.2.2 Advanced Mobile Phone System (AMPS) -- 1.3 2G: From Analog to Digital -- 1.3.1 Global System for Mobile Communications (GSM) -- 1.3.2 Digital Advanced Mobile Phone System (D‐AMPS) -- 1.3.3 Interim Standard 95 (IS‐95) -- 1.3.4 Personal Digital Cellular (PDC) -- 1.3.5 General Packet Radio Service (GPRS) -- 1.3.6 Enhanced Data Rates for GSM Evolution (EDGE) -- 1.4 3G: From Voice to Data‐Centric -- 1.4.1 Wideband Code‐Division Multiple Access (WCDMA) -- 1.4.2 Code‐Division Multiple Access 2000 (CDMA2000) -- 1.4.3 Time Division‐Synchronous Code‐Division Multiple Access (TD‐SCDMA) -- 1.4.4 Worldwide Interoperability for Microwave Access (WiMAX) -- 1.5 4G: Mobile Internet -- 1.5.1 Long‐Term Evolution‐Advanced (LTE‐Advanced) -- 1.5.2 WirelessMAN‐Advanced -- 1.6 5G: From Human to Machine -- 1.7 Beyond 5G -- 1.8 Conclusions -- References -- Chapter 2 Pre‐6G Technology and System Evolution -- 2.1 1G - AMPS -- 2.1.1 System Architecture -- 2.1.2 Key Technologies -- 2.1.2.1 Frequency Reuse -- 2.1.2.2 Cell Splitting -- 2.1.2.3 Sectorization -- 2.1.2.4 Handover -- 2.1.2.5 Frequency‐Division Multiple Access -- 2.2 2G - GSM -- 2.2.1 System Architecture -- 2.2.1.1 Mobile Station Subsystem -- 2.2.1.2 Bases Station Subsystem -- 2.2.1.3 Network and Switching Subsystem -- 2.2.1.4 Operation and Support Subsystem -- 2.2.1.5 General Packet Radio Service -- 2.2.1.6 Gateway GPRS Support Node -- 2.2.2 Key Technologies -- 2.2.2.1 Time‐Division Multiple Access -- 2.2.2.2 Frequency Hopping -- 2.2.2.3 Speech Compression -- 2.2.2.4 Channel Coding.
2.2.2.5 Digital Modulation -- 2.2.2.6 Discontinuous Transmission (DXT) -- 2.3 3G - WCDMA -- 2.3.1 System Architecture -- 2.3.1.1 User Equipment -- 2.3.1.2 UMTS Terrestrial Radio Access Network -- 2.3.1.3 Core Network -- 2.3.2 Key Technologies -- 2.3.2.1 Code‐Division Multiple Access -- 2.3.2.2 Rake Receiver -- 2.3.2.3 Turbo Codes -- 2.4 4G - LTE -- 2.4.1 System Architecture -- 2.4.1.1 Evolved Universal Terrestrial Radio Access Network -- 2.4.1.2 Evolved Packet Core -- 2.4.2 Key Technologies -- 2.4.2.1 Orthogonal Frequency‐Division Multiplexing -- 2.4.2.2 Carrier Aggregation -- 2.4.2.3 Relaying -- 2.4.2.4 Heterogeneous Network -- 2.4.2.5 Coordinated Multi‐Point Transmission and Reception -- 2.4.2.6 Device‐to‐Device Communications -- 2.4.2.7 License‐Assisted Access -- 2.5 5G - New Radio -- 2.5.1 System Architecture -- 2.5.1.1 5G Core Network -- 2.5.1.2 Next Generation Radio Access Network -- 2.5.2 Key Technologies -- 2.5.2.1 Massive MIMO -- 2.5.2.2 Millimeter Wave -- 2.5.2.3 Non‐Orthogonal Multiple Access -- 2.5.2.4 SDN/NFV -- 2.5.2.5 Network Slicing -- 2.5.2.6 Polar Codes -- 2.6 Conclusions -- References -- Chapter 3 The Vision of 6G: Drivers, Enablers, Uses, and Roadmap -- 3.1 Background -- 3.2 Explosive Mobile Traffic -- 3.3 Use Cases -- 3.4 Usage Scenarios -- 3.5 Performance Requirements -- 3.6 Research Initiatives and Roadmap -- 3.6.1 ITU -- 3.6.2 Third Generation Partnership Project -- 3.6.3 Industry -- 3.6.4 Europe -- 3.6.5 The United States -- 3.6.6 China -- 3.6.7 Japan -- 3.6.8 South Korea -- 3.7 Key Technologies -- 3.7.1 Millimeter Wave -- 3.7.2 Terahertz Communications -- 3.7.3 Optical Wireless Communications -- 3.7.4 Massive MIMO -- 3.7.5 Intelligent Reflecting Surfaces -- 3.7.6 Next‐Generation Multiple Access -- 3.7.7 Open Radio Access Network -- 3.7.8 Non‐Terrestrial Networks -- 3.7.9 Artificial Intelligence.
3.7.10 Communication‐Computing‐Sensing Convergence -- 3.8 Conclusions -- References -- Part II Full‐Spectra Wireless Communications in 6G -- Chapter 4 Enhanced Millimeter‐Wave Wireless Communications in 6G -- 4.1 Spectrum Shortage -- 4.2 mmWave Propagation Characteristics -- 4.2.1 Large‐Scale Propagation Effects -- 4.2.1.1 Free‐Space Propagation Loss -- 4.2.1.2 NLOS Propagation and Shadowing -- 4.2.1.3 Atmospheric Attenuation -- 4.2.2 Small‐Scale Propagation Effects -- 4.2.3 Delay Spread and Coherence Bandwidth -- 4.2.4 Doppler Spread and Coherence Time -- 4.2.5 Angular Spread -- 4.3 Millimeter‐Wave Channel Models -- 4.3.1 Large‐Scale Fading -- 4.3.2 3GPP Channel Models -- 4.3.2.1 Urban Micro Scenario -- 4.3.2.2 Urban Macro Scenario -- 4.3.2.3 Indoor Scenario -- 4.3.3 Small‐Scale Fading -- 4.4 mmWave Transmission Technologies -- 4.4.1 Beamforming -- 4.4.1.1 Digital Beamforming -- 4.4.1.2 Analog Beamforming -- 4.4.1.3 Hybrid Beamforming -- 4.4.1.4 3D Beamforming -- 4.4.2 Initial Access -- 4.4.2.1 Multi‐Beam Synchronization and Broadcasting -- 4.4.2.2 Conventional Initial Access in LTE -- 4.4.2.3 Beam‐Sweeping Initial Access in NR -- 4.4.3 Omnidirectional Beamforming -- 4.4.3.1 Random Beamforming -- 4.4.3.2 Enhanced Random Beamforming -- 4.4.3.3 Complementary Random Beamforming -- 4.5 Summary -- References -- Chapter 5 Terahertz Technologies and Systems for 6G -- 5.1 Potential of Terahertz Band -- 5.1.1 Spectrum Limit -- 5.1.2 The Need of Exploiting Terahertz Band -- 5.1.3 Spectrum Regulation on Terahertz Band -- 5.2 Terahertz Applications -- 5.2.1 Terahertz Wireless Communications -- 5.2.1.1 Terabit Cellular Hotspot -- 5.2.1.2 Terabit Wireless Local‐Area Network -- 5.2.1.3 Terabit Device‐To‐Device Link -- 5.2.1.4 Secure Wireless Communication -- 5.2.1.5 Terabit Wireless Backhaul -- 5.2.1.6 Terahertz Nano‐Communications.
5.2.2 Non‐Communication Terahertz Applications -- 5.2.2.1 Terahertz Sensing -- 5.2.2.2 Terahertz Imaging -- 5.2.2.3 Terahertz Positioning -- 5.3 Challenges of Terahertz Communications -- 5.3.1 High Free‐Space Path Loss -- 5.3.2 Atmospheric Attenuation -- 5.3.3 Weather Effects -- 5.3.4 Blockage -- 5.3.5 High Channel Fluctuation -- 5.4 Array‐of‐Subarrays Beamforming -- 5.5 Lens Antenna -- 5.5.1 Refraction of Radio Waves -- 5.5.2 Lens Antenna Array -- 5.6 Case Study - IEEE 802.15.3d -- 5.6.1 IEEE 802.15.3d Usage Scenarios -- 5.6.2 Physical Layer -- 5.6.2.1 Channelization -- 5.6.2.2 Modulation -- 5.6.2.3 Forward Error Correction -- 5.6.3 Medium Access Control -- 5.6.4 Frame Structure -- 5.6.4.1 Preamble -- 5.6.4.2 PHY Header -- 5.6.4.3 MAC Header -- 5.6.4.4 Construction Process of Frame Header -- 5.7 Summary -- References -- Chapter 6 Optical and Visible Light Wireless Communications in 6G -- 6.1 The Optical Spectrum -- 6.1.1 Infrared -- 6.1.2 Visible Light -- 6.1.3 Ultraviolet -- 6.2 Advantages and Challenges -- 6.3 OWC Applications -- 6.4 Evolution of Optical Wireless Communications -- 6.4.1 Wireless Infrared Communications -- 6.4.2 Visible Light Communications -- 6.4.3 Wireless Ultraviolet Communications -- 6.4.4 Free‐Space Optical Communications -- 6.5 Optical Transceiver -- 6.6 Optical Sources and Detectors -- 6.6.1 Light‐Emitting Diode -- 6.6.2 Laser Diode -- 6.6.3 Photodiode -- 6.7 Optical Link Configuration -- 6.8 Optical MIMO -- 6.8.1 Spatial Multiplexing -- 6.8.2 Spatial Modulation -- 6.9 Summary -- References -- Part III Smart Radio Networks and Air Interface Technologies for 6G -- Chapter 7 Intelligent Reflecting Surface‐Aided Communications for 6G -- 7.1 Basic Concept -- 7.2 IRS‐Aided Single‐Antenna Transmission -- 7.2.1 Signal Model -- 7.2.2 Passive Beamforming -- 7.2.3 Product‐Distance Path Loss -- 7.3 IRS‐Aided Multi‐Antenna Transmission.
7.3.1 Joint Active and Passive Beamforming -- 7.3.1.1 SDR Solution -- 7.3.1.2 Alternating Optimization -- 7.3.2 Joint Precoding and Reflecting -- 7.4 Dual‐Beam Intelligent Reflecting Surface -- 7.4.1 Dual Beams Over Hybrid Beamforming -- 7.4.2 Dual‐Beam IRS -- 7.4.3 Optimization Design -- 7.5 IRS‐Aided Wideband Communications -- 7.5.1 Cascaded Frequency‐Selective Channel -- 7.5.2 IRS‐Aided OFDM System -- 7.5.3 Rate Maximization -- 7.6 Multi‐User IRS Communications -- 7.6.1 Multiple Access Model -- 7.6.2 Orthogonal Multiple Access -- 7.6.2.1 Time‐Division Multiple Access -- 7.6.2.2 Frequency‐Division Multiple Access -- 7.6.3 Non‐Orthogonal Multiple Access -- 7.7 Channel Aging and Prediction -- 7.7.1 Outdated Channel State Information -- 7.7.1.1 Doppler Shift -- 7.7.1.2 Phase Noise -- 7.7.2 Impact of Channel Aging on IRS -- 7.7.3 Classical Channel Prediction -- 7.7.3.1 Autoregressive Model -- 7.7.3.2 Parametric Model -- 7.7.4 Recurrent Neural Network -- 7.7.5 RNN‐Based Channel Prediction -- 7.7.5.1 Flat‐Fading Channel Prediction -- 7.7.5.2 Frequency‐Selective Fading Channel Prediction -- 7.7.6 Long‐Short Term Memory -- 7.7.7 Deep Learning‐Based Channel Prediction -- 7.8 Summary -- References -- Chapter 8 Multiple Dimensional and Antenna Techniques for 6G -- 8.1 Spatial Diversity -- 8.2 Receive Combining -- 8.2.1 Selection Combining -- 8.2.2 Maximal Ratio Combining -- 8.2.3 Equal‐Gain Combining -- 8.3 Space‐Time Coding -- 8.3.1 Repetition Coding -- 8.3.2 Space‐Time Trellis Codes -- 8.3.3 Alamouti Coding -- 8.3.4 Space‐Time Block Codes -- 8.4 Transmit Antenna Selection -- 8.5 Beamforming -- 8.5.1 Classical Beamforming -- 8.5.2 Single‐Stream Precoding -- 8.6 Spatial Multiplexing -- 8.6.1 Single‐User MIMO -- 8.6.2 MIMO Precoding -- 8.6.2.1 Full CSI at the Transmitter -- 8.6.2.2 Limited CSI at the Transmitter -- 8.6.3 MIMO Detection.
8.6.3.1 Maximum‐Likelihood Detection.
Record Nr. UNINA-9910830581603321
Jiang Wei (Writer on 6G networks)  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
6G Key Technologies : A Comprehensive Guide
6G Key Technologies : A Comprehensive Guide
Autore Jiang Wei
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (579 pages)
Altri autori (Persone) LuoFa-Long
Collana IEEE Press Ser.
ISBN 1-119-84750-8
1-119-84748-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Preface -- List of Abbreviations -- Part I The Vision of 6G and Technical Evolution -- Chapter 1 Standards History of Cellular Systems Toward 6G -- 1.1 0G: Pre‐Cellular Systems -- 1.2 1G: The Birth of Cellular Network -- 1.2.1 Nordic Mobile Telephone (NMT) -- 1.2.2 Advanced Mobile Phone System (AMPS) -- 1.3 2G: From Analog to Digital -- 1.3.1 Global System for Mobile Communications (GSM) -- 1.3.2 Digital Advanced Mobile Phone System (D‐AMPS) -- 1.3.3 Interim Standard 95 (IS‐95) -- 1.3.4 Personal Digital Cellular (PDC) -- 1.3.5 General Packet Radio Service (GPRS) -- 1.3.6 Enhanced Data Rates for GSM Evolution (EDGE) -- 1.4 3G: From Voice to Data‐Centric -- 1.4.1 Wideband Code‐Division Multiple Access (WCDMA) -- 1.4.2 Code‐Division Multiple Access 2000 (CDMA2000) -- 1.4.3 Time Division‐Synchronous Code‐Division Multiple Access (TD‐SCDMA) -- 1.4.4 Worldwide Interoperability for Microwave Access (WiMAX) -- 1.5 4G: Mobile Internet -- 1.5.1 Long‐Term Evolution‐Advanced (LTE‐Advanced) -- 1.5.2 WirelessMAN‐Advanced -- 1.6 5G: From Human to Machine -- 1.7 Beyond 5G -- 1.8 Conclusions -- References -- Chapter 2 Pre‐6G Technology and System Evolution -- 2.1 1G - AMPS -- 2.1.1 System Architecture -- 2.1.2 Key Technologies -- 2.1.2.1 Frequency Reuse -- 2.1.2.2 Cell Splitting -- 2.1.2.3 Sectorization -- 2.1.2.4 Handover -- 2.1.2.5 Frequency‐Division Multiple Access -- 2.2 2G - GSM -- 2.2.1 System Architecture -- 2.2.1.1 Mobile Station Subsystem -- 2.2.1.2 Bases Station Subsystem -- 2.2.1.3 Network and Switching Subsystem -- 2.2.1.4 Operation and Support Subsystem -- 2.2.1.5 General Packet Radio Service -- 2.2.1.6 Gateway GPRS Support Node -- 2.2.2 Key Technologies -- 2.2.2.1 Time‐Division Multiple Access -- 2.2.2.2 Frequency Hopping -- 2.2.2.3 Speech Compression -- 2.2.2.4 Channel Coding.
2.2.2.5 Digital Modulation -- 2.2.2.6 Discontinuous Transmission (DXT) -- 2.3 3G - WCDMA -- 2.3.1 System Architecture -- 2.3.1.1 User Equipment -- 2.3.1.2 UMTS Terrestrial Radio Access Network -- 2.3.1.3 Core Network -- 2.3.2 Key Technologies -- 2.3.2.1 Code‐Division Multiple Access -- 2.3.2.2 Rake Receiver -- 2.3.2.3 Turbo Codes -- 2.4 4G - LTE -- 2.4.1 System Architecture -- 2.4.1.1 Evolved Universal Terrestrial Radio Access Network -- 2.4.1.2 Evolved Packet Core -- 2.4.2 Key Technologies -- 2.4.2.1 Orthogonal Frequency‐Division Multiplexing -- 2.4.2.2 Carrier Aggregation -- 2.4.2.3 Relaying -- 2.4.2.4 Heterogeneous Network -- 2.4.2.5 Coordinated Multi‐Point Transmission and Reception -- 2.4.2.6 Device‐to‐Device Communications -- 2.4.2.7 License‐Assisted Access -- 2.5 5G - New Radio -- 2.5.1 System Architecture -- 2.5.1.1 5G Core Network -- 2.5.1.2 Next Generation Radio Access Network -- 2.5.2 Key Technologies -- 2.5.2.1 Massive MIMO -- 2.5.2.2 Millimeter Wave -- 2.5.2.3 Non‐Orthogonal Multiple Access -- 2.5.2.4 SDN/NFV -- 2.5.2.5 Network Slicing -- 2.5.2.6 Polar Codes -- 2.6 Conclusions -- References -- Chapter 3 The Vision of 6G: Drivers, Enablers, Uses, and Roadmap -- 3.1 Background -- 3.2 Explosive Mobile Traffic -- 3.3 Use Cases -- 3.4 Usage Scenarios -- 3.5 Performance Requirements -- 3.6 Research Initiatives and Roadmap -- 3.6.1 ITU -- 3.6.2 Third Generation Partnership Project -- 3.6.3 Industry -- 3.6.4 Europe -- 3.6.5 The United States -- 3.6.6 China -- 3.6.7 Japan -- 3.6.8 South Korea -- 3.7 Key Technologies -- 3.7.1 Millimeter Wave -- 3.7.2 Terahertz Communications -- 3.7.3 Optical Wireless Communications -- 3.7.4 Massive MIMO -- 3.7.5 Intelligent Reflecting Surfaces -- 3.7.6 Next‐Generation Multiple Access -- 3.7.7 Open Radio Access Network -- 3.7.8 Non‐Terrestrial Networks -- 3.7.9 Artificial Intelligence.
3.7.10 Communication‐Computing‐Sensing Convergence -- 3.8 Conclusions -- References -- Part II Full‐Spectra Wireless Communications in 6G -- Chapter 4 Enhanced Millimeter‐Wave Wireless Communications in 6G -- 4.1 Spectrum Shortage -- 4.2 mmWave Propagation Characteristics -- 4.2.1 Large‐Scale Propagation Effects -- 4.2.1.1 Free‐Space Propagation Loss -- 4.2.1.2 NLOS Propagation and Shadowing -- 4.2.1.3 Atmospheric Attenuation -- 4.2.2 Small‐Scale Propagation Effects -- 4.2.3 Delay Spread and Coherence Bandwidth -- 4.2.4 Doppler Spread and Coherence Time -- 4.2.5 Angular Spread -- 4.3 Millimeter‐Wave Channel Models -- 4.3.1 Large‐Scale Fading -- 4.3.2 3GPP Channel Models -- 4.3.2.1 Urban Micro Scenario -- 4.3.2.2 Urban Macro Scenario -- 4.3.2.3 Indoor Scenario -- 4.3.3 Small‐Scale Fading -- 4.4 mmWave Transmission Technologies -- 4.4.1 Beamforming -- 4.4.1.1 Digital Beamforming -- 4.4.1.2 Analog Beamforming -- 4.4.1.3 Hybrid Beamforming -- 4.4.1.4 3D Beamforming -- 4.4.2 Initial Access -- 4.4.2.1 Multi‐Beam Synchronization and Broadcasting -- 4.4.2.2 Conventional Initial Access in LTE -- 4.4.2.3 Beam‐Sweeping Initial Access in NR -- 4.4.3 Omnidirectional Beamforming -- 4.4.3.1 Random Beamforming -- 4.4.3.2 Enhanced Random Beamforming -- 4.4.3.3 Complementary Random Beamforming -- 4.5 Summary -- References -- Chapter 5 Terahertz Technologies and Systems for 6G -- 5.1 Potential of Terahertz Band -- 5.1.1 Spectrum Limit -- 5.1.2 The Need of Exploiting Terahertz Band -- 5.1.3 Spectrum Regulation on Terahertz Band -- 5.2 Terahertz Applications -- 5.2.1 Terahertz Wireless Communications -- 5.2.1.1 Terabit Cellular Hotspot -- 5.2.1.2 Terabit Wireless Local‐Area Network -- 5.2.1.3 Terabit Device‐To‐Device Link -- 5.2.1.4 Secure Wireless Communication -- 5.2.1.5 Terabit Wireless Backhaul -- 5.2.1.6 Terahertz Nano‐Communications.
5.2.2 Non‐Communication Terahertz Applications -- 5.2.2.1 Terahertz Sensing -- 5.2.2.2 Terahertz Imaging -- 5.2.2.3 Terahertz Positioning -- 5.3 Challenges of Terahertz Communications -- 5.3.1 High Free‐Space Path Loss -- 5.3.2 Atmospheric Attenuation -- 5.3.3 Weather Effects -- 5.3.4 Blockage -- 5.3.5 High Channel Fluctuation -- 5.4 Array‐of‐Subarrays Beamforming -- 5.5 Lens Antenna -- 5.5.1 Refraction of Radio Waves -- 5.5.2 Lens Antenna Array -- 5.6 Case Study - IEEE 802.15.3d -- 5.6.1 IEEE 802.15.3d Usage Scenarios -- 5.6.2 Physical Layer -- 5.6.2.1 Channelization -- 5.6.2.2 Modulation -- 5.6.2.3 Forward Error Correction -- 5.6.3 Medium Access Control -- 5.6.4 Frame Structure -- 5.6.4.1 Preamble -- 5.6.4.2 PHY Header -- 5.6.4.3 MAC Header -- 5.6.4.4 Construction Process of Frame Header -- 5.7 Summary -- References -- Chapter 6 Optical and Visible Light Wireless Communications in 6G -- 6.1 The Optical Spectrum -- 6.1.1 Infrared -- 6.1.2 Visible Light -- 6.1.3 Ultraviolet -- 6.2 Advantages and Challenges -- 6.3 OWC Applications -- 6.4 Evolution of Optical Wireless Communications -- 6.4.1 Wireless Infrared Communications -- 6.4.2 Visible Light Communications -- 6.4.3 Wireless Ultraviolet Communications -- 6.4.4 Free‐Space Optical Communications -- 6.5 Optical Transceiver -- 6.6 Optical Sources and Detectors -- 6.6.1 Light‐Emitting Diode -- 6.6.2 Laser Diode -- 6.6.3 Photodiode -- 6.7 Optical Link Configuration -- 6.8 Optical MIMO -- 6.8.1 Spatial Multiplexing -- 6.8.2 Spatial Modulation -- 6.9 Summary -- References -- Part III Smart Radio Networks and Air Interface Technologies for 6G -- Chapter 7 Intelligent Reflecting Surface‐Aided Communications for 6G -- 7.1 Basic Concept -- 7.2 IRS‐Aided Single‐Antenna Transmission -- 7.2.1 Signal Model -- 7.2.2 Passive Beamforming -- 7.2.3 Product‐Distance Path Loss -- 7.3 IRS‐Aided Multi‐Antenna Transmission.
7.3.1 Joint Active and Passive Beamforming -- 7.3.1.1 SDR Solution -- 7.3.1.2 Alternating Optimization -- 7.3.2 Joint Precoding and Reflecting -- 7.4 Dual‐Beam Intelligent Reflecting Surface -- 7.4.1 Dual Beams Over Hybrid Beamforming -- 7.4.2 Dual‐Beam IRS -- 7.4.3 Optimization Design -- 7.5 IRS‐Aided Wideband Communications -- 7.5.1 Cascaded Frequency‐Selective Channel -- 7.5.2 IRS‐Aided OFDM System -- 7.5.3 Rate Maximization -- 7.6 Multi‐User IRS Communications -- 7.6.1 Multiple Access Model -- 7.6.2 Orthogonal Multiple Access -- 7.6.2.1 Time‐Division Multiple Access -- 7.6.2.2 Frequency‐Division Multiple Access -- 7.6.3 Non‐Orthogonal Multiple Access -- 7.7 Channel Aging and Prediction -- 7.7.1 Outdated Channel State Information -- 7.7.1.1 Doppler Shift -- 7.7.1.2 Phase Noise -- 7.7.2 Impact of Channel Aging on IRS -- 7.7.3 Classical Channel Prediction -- 7.7.3.1 Autoregressive Model -- 7.7.3.2 Parametric Model -- 7.7.4 Recurrent Neural Network -- 7.7.5 RNN‐Based Channel Prediction -- 7.7.5.1 Flat‐Fading Channel Prediction -- 7.7.5.2 Frequency‐Selective Fading Channel Prediction -- 7.7.6 Long‐Short Term Memory -- 7.7.7 Deep Learning‐Based Channel Prediction -- 7.8 Summary -- References -- Chapter 8 Multiple Dimensional and Antenna Techniques for 6G -- 8.1 Spatial Diversity -- 8.2 Receive Combining -- 8.2.1 Selection Combining -- 8.2.2 Maximal Ratio Combining -- 8.2.3 Equal‐Gain Combining -- 8.3 Space‐Time Coding -- 8.3.1 Repetition Coding -- 8.3.2 Space‐Time Trellis Codes -- 8.3.3 Alamouti Coding -- 8.3.4 Space‐Time Block Codes -- 8.4 Transmit Antenna Selection -- 8.5 Beamforming -- 8.5.1 Classical Beamforming -- 8.5.2 Single‐Stream Precoding -- 8.6 Spatial Multiplexing -- 8.6.1 Single‐User MIMO -- 8.6.2 MIMO Precoding -- 8.6.2.1 Full CSI at the Transmitter -- 8.6.2.2 Limited CSI at the Transmitter -- 8.6.3 MIMO Detection.
8.6.3.1 Maximum‐Likelihood Detection.
Record Nr. UNINA-9910632494303321
Jiang Wei  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Audio, Language and Image Processing (ICALIP), 2014 International Conference on / / edited by Fa-Long Luo, Wanggen Wan, Xiaoqing Yu
Audio, Language and Image Processing (ICALIP), 2014 International Conference on / / edited by Fa-Long Luo, Wanggen Wan, Xiaoqing Yu
Pubbl/distr/stampa Piscataway, New Jersey : , : IEEE, , 2014
Descrizione fisica 1 online resource
Disciplina 621.367
Soggetto topico Computer vision
Image processing
ISBN 1-4799-3903-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 2014 International Conference on Audio, Language and Image Processing
Audio, Language and Image Processing
Record Nr. UNISA-996278341003316
Piscataway, New Jersey : , : IEEE, , 2014
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Audio, Language and Image Processing (ICALIP), 2014 International Conference on / / edited by Fa-Long Luo, Wanggen Wan, Xiaoqing Yu
Audio, Language and Image Processing (ICALIP), 2014 International Conference on / / edited by Fa-Long Luo, Wanggen Wan, Xiaoqing Yu
Pubbl/distr/stampa Piscataway, New Jersey : , : IEEE, , 2014
Descrizione fisica 1 online resource
Disciplina 621.367
Soggetto topico Computer vision
Image processing
ISBN 1-4799-3903-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti 2014 International Conference on Audio, Language and Image Processing
Audio, Language and Image Processing
Record Nr. UNINA-9910142034103321
Piscataway, New Jersey : , : IEEE, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine learning for future wireless communications / / edited by Fa-Long Luo
Machine learning for future wireless communications / / edited by Fa-Long Luo
Autore Luo Fa-Long
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE, , 2020
Descrizione fisica 1 online resource (493 pages)
Disciplina 006.31
Soggetto topico Machine learning
ISBN 1-119-56231-7
1-119-56227-9
1-119-56230-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto List 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.
3.2 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 LearninǵôBased 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.
4.4.4.2 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.
7.4.2.3 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 LearninǵôBased 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 LearninǵôBased 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.
10.3.1 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 MachinéôBased 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.
12.4.1.1 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.
14.2.3 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 NetworḱôBased 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.
16.5.2.3 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 ]]>̧ô̧ô 17.5 Support Vector Regression Approaches 368 -- 17.6 Further Discussion and Conclusions 373 -- Bibliography 374 -- 18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 383 /Alexios Balatsoukas-Stimming -- 18.1 Nonlinear Self-Interference Models 384 -- 18.1.1 Nonlinear Self-Interference Model 385 -- 18.2 Digital Self-Interference Cancellation 386 -- 18.2.1 Linear Cancellation 386 -- 18.2.2 Polynomial Nonlinear Cancellation 387 -- 18.2.3 Neural Network Nonlinear Cancellation 387 -- 18.2.4 Computational Complexity 389 -- 18.2.4.1 Linear Cancellation 389 -- 18.2.4.2 Polynomial Nonlinear Cancellation 390 -- 18.2.4.3 Neural Network Nonlinear Cancellation 390 -- 18.3 Experimental Results 391 -- 18.3.1 Experimental Setup 391 -- 18.3.2 Self-Interference Cancellation Results 391 -- 18.3.3 Computational Complexity 392 -- 18.4 Conclusions 393 -- 18.4.1 Open Problems 394 -- Bibliography 395 -- 19 Machine Learning for Context-Aware Cross-Layer Optimization 397 /Yang Yang, Zening Liu, Shuang Zhao, Ziyu Shao, and Kunlun Wang -- 19.1 Introduction 397 -- 19.2 System Model 399 -- 19.3 Problem Formulation and Analytical Framework 402 -- 19.3.1 Fog-Enabled Multi-Tier Operations Scheduling (FEMOS) Algorithm 403 -- 19.3.2 Theoretical and Numerical Analysis 405 -- 19.3.2.1 Theoretical Analysis 405 -- 19.3.2.2 Numerical Analysis 406 -- 19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409 -- 19.4.1 System Model 409 -- 19.4.2 Theoretical Analysis 411 -- 19.4.3 Numerical Analysis 413 -- 19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413 -- 19.5.1 System Model and Problem Formulation 413 -- 19.5.2 COUS Algorithm 416 -- 19.5.3 Performance Evaluation 418 -- 19.6 Conclusion 420 -- Bibliography 421 -- 20 Physical-Layer Location Verification by Machine Learning 425 /Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto -- 20.1 IRLV by Wireless Channel Features 427 -- 20.1.1 Optimal Test 428 -- 20.2 ML Classification for IRLV 428.
20.2.1 Neural Networks 429 -- 20.2.2 Support Vector Machines 430 -- 20.2.3 ML Classification Optimality 431 -- 20.3 Learning Phase Convergence 431 -- 20.3.1 Fundamental Learning Theorem 431 -- 20.3.2 Simulation Results 432 -- 20.4 Experimental Results 433 -- 20.5 Conclusions 437 -- Bibliography 437 -- 21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439 /M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar -- 21.1 Introduction 439 -- 21.2 System Model 441 -- 21.2.1 Multi-Cell Network Model 441 -- 21.2.2 Single-Cell Network Model with D2D Communication 442 -- 21.2.3 Action Space 443 -- 21.3 Problem Formulation 443 -- 21.3.1 Cache Hit Rate 443 -- 21.3.2 Transmission Delay 444 -- 21.4 Deep Actor-Critic Framework for Content Caching 446 -- 21.5 Application to the Multi-Cell Network 448 -- 21.5.1 Experimental Settings 448 -- 21.5.2 Simulation Setup 448 -- 21.5.3 Simulation Results 449 -- 21.5.3.1 Cache Hit Rate 449 -- 21.5.3.2 Transmission Delay 450 -- 21.5.3.3 Time-Varying Scenario 451 -- 21.6 Application to the Single-Cell Network with D2D Communications 452 -- 21.6.1 Experimental Settings 452 -- 21.6.2 Simulation Setup 452 -- 21.6.3 Simulation Results 453 -- 21.6.3.1 Cache Hit Rate 453 -- 21.6.3.2 Transmission Delay 454 -- 21.7 Conclusion 454 -- Bibliography 455 -- Index 459.
Record Nr. UNINA-9910555156703321
Luo Fa-Long  
Hoboken, New Jersey : , : Wiley-IEEE, , 2020
Materiale a stampa
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Machine learning for future wireless communications / / edited by Fa-Long Luo
Machine learning for future wireless communications / / edited by Fa-Long Luo
Autore Luo Fa-Long
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley-IEEE, , 2020
Descrizione fisica 1 online resource (493 pages)
Disciplina 006.31
Soggetto topico Machine learning
ISBN 1-119-56231-7
1-119-56227-9
1-119-56230-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto List 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.
3.2 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 LearninǵôBased 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.
4.4.4.2 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.
7.4.2.3 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 LearninǵôBased 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 LearninǵôBased 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.
10.3.1 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 MachinéôBased 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.
12.4.1.1 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.
14.2.3 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 NetworḱôBased 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.
16.5.2.3 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 ]]>̧ô̧ô 17.5 Support Vector Regression Approaches 368 -- 17.6 Further Discussion and Conclusions 373 -- Bibliography 374 -- 18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 383 /Alexios Balatsoukas-Stimming -- 18.1 Nonlinear Self-Interference Models 384 -- 18.1.1 Nonlinear Self-Interference Model 385 -- 18.2 Digital Self-Interference Cancellation 386 -- 18.2.1 Linear Cancellation 386 -- 18.2.2 Polynomial Nonlinear Cancellation 387 -- 18.2.3 Neural Network Nonlinear Cancellation 387 -- 18.2.4 Computational Complexity 389 -- 18.2.4.1 Linear Cancellation 389 -- 18.2.4.2 Polynomial Nonlinear Cancellation 390 -- 18.2.4.3 Neural Network Nonlinear Cancellation 390 -- 18.3 Experimental Results 391 -- 18.3.1 Experimental Setup 391 -- 18.3.2 Self-Interference Cancellation Results 391 -- 18.3.3 Computational Complexity 392 -- 18.4 Conclusions 393 -- 18.4.1 Open Problems 394 -- Bibliography 395 -- 19 Machine Learning for Context-Aware Cross-Layer Optimization 397 /Yang Yang, Zening Liu, Shuang Zhao, Ziyu Shao, and Kunlun Wang -- 19.1 Introduction 397 -- 19.2 System Model 399 -- 19.3 Problem Formulation and Analytical Framework 402 -- 19.3.1 Fog-Enabled Multi-Tier Operations Scheduling (FEMOS) Algorithm 403 -- 19.3.2 Theoretical and Numerical Analysis 405 -- 19.3.2.1 Theoretical Analysis 405 -- 19.3.2.2 Numerical Analysis 406 -- 19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409 -- 19.4.1 System Model 409 -- 19.4.2 Theoretical Analysis 411 -- 19.4.3 Numerical Analysis 413 -- 19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413 -- 19.5.1 System Model and Problem Formulation 413 -- 19.5.2 COUS Algorithm 416 -- 19.5.3 Performance Evaluation 418 -- 19.6 Conclusion 420 -- Bibliography 421 -- 20 Physical-Layer Location Verification by Machine Learning 425 /Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto -- 20.1 IRLV by Wireless Channel Features 427 -- 20.1.1 Optimal Test 428 -- 20.2 ML Classification for IRLV 428.
20.2.1 Neural Networks 429 -- 20.2.2 Support Vector Machines 430 -- 20.2.3 ML Classification Optimality 431 -- 20.3 Learning Phase Convergence 431 -- 20.3.1 Fundamental Learning Theorem 431 -- 20.3.2 Simulation Results 432 -- 20.4 Experimental Results 433 -- 20.5 Conclusions 437 -- Bibliography 437 -- 21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439 /M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar -- 21.1 Introduction 439 -- 21.2 System Model 441 -- 21.2.1 Multi-Cell Network Model 441 -- 21.2.2 Single-Cell Network Model with D2D Communication 442 -- 21.2.3 Action Space 443 -- 21.3 Problem Formulation 443 -- 21.3.1 Cache Hit Rate 443 -- 21.3.2 Transmission Delay 444 -- 21.4 Deep Actor-Critic Framework for Content Caching 446 -- 21.5 Application to the Multi-Cell Network 448 -- 21.5.1 Experimental Settings 448 -- 21.5.2 Simulation Setup 448 -- 21.5.3 Simulation Results 449 -- 21.5.3.1 Cache Hit Rate 449 -- 21.5.3.2 Transmission Delay 450 -- 21.5.3.3 Time-Varying Scenario 451 -- 21.6 Application to the Single-Cell Network with D2D Communications 452 -- 21.6.1 Experimental Settings 452 -- 21.6.2 Simulation Setup 452 -- 21.6.3 Simulation Results 453 -- 21.6.3.1 Cache Hit Rate 453 -- 21.6.3.2 Transmission Delay 454 -- 21.7 Conclusion 454 -- Bibliography 455 -- Index 459.
Record Nr. UNINA-9910813591503321
Luo Fa-Long  
Hoboken, New Jersey : , : Wiley-IEEE, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Signal processing for 5G : algorithms and implementations / / edited by Fa-Long Luo, Charlie Zhang
Signal processing for 5G : algorithms and implementations / / edited by Fa-Long Luo, Charlie Zhang
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , [2016]
Descrizione fisica 1 online resource (612 pages) : illustrations
Disciplina 621.3845/6
Collana Wiley - IEEE
Soggetto topico Signal processing - Digital techniques - Mathematics
Mobile communication systems - Standards
Wireless communication systems - Standards
Computer algorithms
ISBN 1-119-11648-1
1-119-11647-3
1-119-11649-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- Preface xvii -- List of Contributors xxv -- Part I MODULATION, CODING AND WAVEFORM FOR 5G -- 1 An Introduction to Modulations and Waveforms for 5G Networks 3 /Stefano Buzzi, Alessandro Ugolini, Alessio Zappone and Giulio Colavolpe -- 1.1 Motivation and Background 3 -- 1.2 New Modulation Formats: FBMC, GFDM, BFDM, UFMC and TFP 7 -- 1.3 Waveform Choice 19 -- 1.4 Discussion and Concluding Remarks 20 -- References 22 -- 2 Faster-than-Nyquist Signaling for 5G Communication 24 /John B. Anderson -- 2.1 Introduction to FTN Signaling 25 -- 2.2 Time FTN: Receivers and Performance 32 -- 2.3 Frequency FTN Signaling 41 -- 2.4 Summary of the Chapter 45 -- References 46 -- 3 From OFDM to FBMC: Principles and Comparisons 47 /Wei Jiang and Thomas Kaiser -- 3.1 Introduction 47 -- 3.2 The Filter Bank 49 -- 3.3 Polyphase Implementation 53 -- 3.4 OFDM 55 -- 3.5 FBMC 61 -- 3.6 Comparison of FBMC and Filtered OFDM 62 -- 3.7 Conclusion 65 -- References 66 -- 4 Filter Bank Multicarrier for Massive MIMO 67 /Arman Farhang, Nicola Marchetti and Behrouz Farhang-Boroujeny -- 4.1 System Model and FBMC Formulation in Massive MIMO 69 -- 4.2 Self-equalization Property of FBMC in Massive MIMO 74 -- 4.3 Comparison with OFDM 80 -- 4.4 Blind Equalization and Pilot Decontamination 82 -- 4.5 Conclusion 87 -- References 88 -- 5 Bandwidth-compressed Multicarrier Communication: SEFDM 90 /Izzat Darwazeh, Tongyang Xu and Ryan C Grammenos -- 5.1 Introduction 91 -- 5.2 SEFDM Fundamentals 93 -- 5.3 Block-SEFDM 97 -- 5.4 Turbo-SEFDM 102 -- 5.5 Practical Considerations and Experimental Demonstration 106 -- 5.6 Summary 112 -- References 112 -- 6 Non-orthogonal Multi-User Superposition and Shared Access 115 /Yifei Yuan -- 6.1 Introduction 115 -- 6.2 Basic Principles and Features of Non-orthogonal Multi-user Access 116 -- 6.3 Downlink Non-orthogonal Multi-user Transmission 121 -- 6.4 Uplink Non-orthogonal Multi-user Access 129 -- 6.5 Summary and Future Work 140 -- References 142 -- 7 Non-Orthogonal Multiple Access (NOMA): Concept and Design 143 /Anass Benjebbour, Keisuke Saito, Anxin Li, Yoshihisa Kishiyama and Takehiro Nakamura.
7.1 Introduction 143 -- 7.2 Concept 145 -- 7.3 Benefits and Motivations 148 -- 7.4 Interface Design 150 -- 7.5 MIMO Support 153 -- 7.6 Performance Evaluations 157 -- 7.7 Conclusion 166 -- References 167 -- 8 Major 5G Waveform Candidates: Overview and Comparison 169 /Hao Lin and Pierre Siohan -- 8.1 Why We Need New Waveforms 170 -- 8.2 Major Multicarrier Modulation Candidates 171 -- 8.3 High-level Comparison 178 -- 8.4 Conclusion 184 -- List of acronyms 185 -- References 186 -- Part II NEW SPATIAL SIGNAL PROCESSING FOR 5G -- 9 Massive MIMO for 5G: Theory, Implementation and Prototyping 191 /Ove Edfors, Liang Liu, Fredrik Tufvesson, Nikhil Kundargi and Karl Nieman -- 9.1 Introduction 192 -- 9.2 Massive MIMO Theory 194 -- 9.3 Massive MIMO Channels 199 -- 9.4 Massive MIMO Implementation 204 -- 9.5 Testbed Design 214 -- 9.6 Synchronization 224 -- 9.7 Future Challenges and Conclusion 227 -- Acknowledgments 228 -- References 228 -- 10 Millimeter-Wave MIMO Transceivers: Theory, Design and Implementation 231 /Akbar M. Sayeed and John H. Brady -- 10.1 Introduction 232 -- 10.2 Overview of Millimeter-Wave MIMO Transceiver Architectures 235 -- 10.3 Point-to-Point Single-User Systems 237 -- 10.4 Point-to-Multipoint Multiuser Systems 243 -- 10.5 Extensions 249 -- 10.6 Conclusion 250 -- References 251 -- 11 3D Propagation Channels: Modeling and Measurements 254 /Andreas F. Molisch -- 11.1 Introduction and Motivation 255 -- 11.2 Measurement Techniques 257 -- 11.3 Propagation Effects 260 -- 11.4 Measurement Results 263 -- 11.5 Channel Models 266 -- 11.6 Summary and Open Issues 268 -- Acknowledgements 269 -- Disclaimer 269 -- References 269 -- 12 3D-MIMO with Massive Antennas: Theory, Implementation and Testing 273 /Guangyi Liu, Xueying Hou, Fei Wang, Jing Jin and Hui Tong -- 12.1 Introduction 274 -- 12.2 Application Scenarios of 3D-MIMO with Massive Antennas 276 -- 12.3 Exploiting 3D-MIMO Gain Based on Techniques in Current Standards 277 -- 12.4 Evaluation by System-level Simulations 283.
12.5 Field Trials of 3D-MIMO with Massive Antennas 288 -- 12.6 Achieving 3D-MIMO with Massive Antennas from Theory to Practice 292 -- 12.7 Conclusions 294 -- References 295 -- 13 Orbital Angular Momentum-based Wireless Communications: Designs and Implementations 296 /Alan. E. Willner, Yan Yan, Yongxiong Ren, Nisar Ahmed and Guodong Xie -- 13.1 EM Waves Carrying OAM 297 -- 13.2 Application of OAM to RF Communications 298 -- 13.3 OAM Beam Generation, Multiplexing and Detection 300 -- 13.4 Wireless Communications Using OAM Multiplexing 303 -- 13.5 Summary and Perspective 315 -- References 316 -- Part III NEW SPECTRUM OPPORTUNITIES FOR 5G -- 14 MillimeterWaves for 5G: From Theory To Practice 321 /Malik Gul, Eckhard Ohlmer, Ahsan Aziz, Wes McCoy and Yong Rao -- 14.1 Introduction 321 -- 14.2 Building a mmWave PoC System 322 -- 14.3 Desirable Features of a mmWave Prototyping System 323 -- 14.4 Case Study: a mmWave Cellular PoC 326 -- 14.5 Conclusion 352 -- References 353 -- 15 *5G Millimeter-wave Communication Channel and Technology Overview 354 /Qian (Clara) Li, Hyejung Jung, Pingping Zong and Geng Wu -- 15.1 Introduction 354 -- 15.2 Millimeter-wave Channel Characteristics 355 -- 15.3 Requirements for a 5G mmWave Channel Model 357 -- 15.4 Millimeter-wave Channel Model for 5G 358 -- 15.5 Signal Processing for mmWave Band 5G RAT 365 -- 15.6 Summary 370 -- References 371 -- 16 General Principles and Basic Algorithms for Full-duplex Transmission 372 /Thomas Kaiser and Nidal Zarifeh -- 16.1 Introduction 373 -- 16.2 Self-interference: Basic Analyses and Models 374 -- 16.3 SIC Techniques and Algorithms 376 -- 16.4 Hardware Impairments and Implementation Challenges 386 -- 16.5 Looking Toward Full-duplex MIMO Systems 393 -- 16.6 Conclusion and Outlook 396 -- References 397 -- 17 Design and Implementation of Full-duplex Transceivers 402 /Katsuyuki Haneda, Mikko Valkama, Taneli Riihonen, Emilio Antonio-Rodriguez and Dani Korpi -- 17.1 Research Challenges 405 -- 17.2 Antenna Designs 409.
17.3 RF Self-interference Cancellation Methods 411 -- 17.4 Digital Self-interference Cancellation Algorithms 413 -- 17.5 Demonstration 423 -- 17.6 Summary 426 -- Acknowledgements 426 -- References 426 -- Part IV NEW SYSTEM-LEVEL ENABLING TECHNOLOGIES FOR 5G -- 18 Cloud Radio Access Networks: Uplink Channel Estimation and Downlink Precoding 431 /Osvaldo Simeone, Jinkyu Kang, Joonkhyuk Kang and Shlomo Shamai (Shitz) -- 18.1 Introduction 432 -- 18.2 Technology Background 432 -- 18.3 Uplink: Where to Perform Channel Estimation? 434 -- 18.4 Downlink: Where to Perform Channel Encoding and Precoding? 441 -- 18.5 Concluding Remarks 453 -- References 454 -- 19 Energy-efficient Resource Allocation in 5G with Application to D2D 456 /Alessio Zappone, Francesco Di Stasio, Stefano Buzzi and Eduard Jorswieck -- 19.1 Introduction 457 -- 19.2 Signal Model 459 -- 19.3 Resource Allocation 461 -- 19.4 Fractional Programming 462 -- 19.5 Algorithms 466 -- 19.6 Sequential Fractional Programming 469 -- 19.7 System Optimization 471 -- 19.8 Numerical Results 476 -- 19.9 Conclusion 480 -- References 481 -- 20 Ultra Dense Networks: General Introduction and Design Overview 483 /Jianchi Zhu, Xiaoming She and Peng Chen -- 20.1 Introduction 484 -- 20.2 Interference Management 487 -- 20.3 Mobility Management 495 -- 20.4 Architecture and Backhaul 499 -- 20.5 Other Issues in UDNs for 5G 503 -- 20.6 Conclusions 505 -- Acknowledgements 506 -- References 506 -- 21 Radio-resource Management and Optimization in 5G Networks 509 /Antonis Gotsis, Athanasios Panagopoulos, Stelios Stefanatos and Angeliki Alexiou -- 21.1 Introduction 510 -- 21.2 Background 511 -- 21.3 Optimal Strategies for Single-antenna Coordinated Ultradense Networks 514 -- 21.4 Optimal Strategies for Multi-antenna Coordinated and Cooperative Ultradense Networks 525 -- 21.5 Summary and Future Research Directions 533 -- Acknowledgments 534 -- References 534 -- Part V REFERENCE DESIGN AND 5G STANDARD DEVELOPMENT -- 22 Full-duplex Radios in 5G: Fundamentals, Design and Prototyping 539 /Jaeweon Kim, Min Soo Sim, MinKeun Chung, Dong Ku Kim and Chan-Byoung Chae.
22.1 Introduction 540 -- 22.2 Self-interference 541 -- 22.3 Analog Self-interference Cancellation 542 -- 22.4 Digital Self-interference Cancellation 547 -- 22.5 Prototyping Full-duplex Radios 550 -- 22.6 Overall Performance Evaluation 558 -- 22.7 Conclusion 559 -- References 559 -- 23 5G Standard Development: Technology and Roadmap 561 /Juho Lee and Yongjun Kwak -- 23.1 Introduction 561 -- 23.2 Standards Roadmap from 4G to 5G 562 -- 23.3 Preparation of 5G Cellular Communication Standards 570 -- 23.4 Concluding Remarks 575 -- References 575 -- Index 577.
Record Nr. UNINA-9910135021403321
Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Signal processing for 5G : algorithms and implementations / / edited by Fa-Long Luo, Charlie Zhang
Signal processing for 5G : algorithms and implementations / / edited by Fa-Long Luo, Charlie Zhang
Pubbl/distr/stampa Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , [2016]
Descrizione fisica 1 online resource (612 pages) : illustrations
Disciplina 621.3845/6
Collana Wiley - IEEE
Soggetto topico Signal processing - Digital techniques - Mathematics
Mobile communication systems - Standards
Wireless communication systems - Standards
Computer algorithms
ISBN 1-119-11648-1
1-119-11647-3
1-119-11649-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- Preface xvii -- List of Contributors xxv -- Part I MODULATION, CODING AND WAVEFORM FOR 5G -- 1 An Introduction to Modulations and Waveforms for 5G Networks 3 /Stefano Buzzi, Alessandro Ugolini, Alessio Zappone and Giulio Colavolpe -- 1.1 Motivation and Background 3 -- 1.2 New Modulation Formats: FBMC, GFDM, BFDM, UFMC and TFP 7 -- 1.3 Waveform Choice 19 -- 1.4 Discussion and Concluding Remarks 20 -- References 22 -- 2 Faster-than-Nyquist Signaling for 5G Communication 24 /John B. Anderson -- 2.1 Introduction to FTN Signaling 25 -- 2.2 Time FTN: Receivers and Performance 32 -- 2.3 Frequency FTN Signaling 41 -- 2.4 Summary of the Chapter 45 -- References 46 -- 3 From OFDM to FBMC: Principles and Comparisons 47 /Wei Jiang and Thomas Kaiser -- 3.1 Introduction 47 -- 3.2 The Filter Bank 49 -- 3.3 Polyphase Implementation 53 -- 3.4 OFDM 55 -- 3.5 FBMC 61 -- 3.6 Comparison of FBMC and Filtered OFDM 62 -- 3.7 Conclusion 65 -- References 66 -- 4 Filter Bank Multicarrier for Massive MIMO 67 /Arman Farhang, Nicola Marchetti and Behrouz Farhang-Boroujeny -- 4.1 System Model and FBMC Formulation in Massive MIMO 69 -- 4.2 Self-equalization Property of FBMC in Massive MIMO 74 -- 4.3 Comparison with OFDM 80 -- 4.4 Blind Equalization and Pilot Decontamination 82 -- 4.5 Conclusion 87 -- References 88 -- 5 Bandwidth-compressed Multicarrier Communication: SEFDM 90 /Izzat Darwazeh, Tongyang Xu and Ryan C Grammenos -- 5.1 Introduction 91 -- 5.2 SEFDM Fundamentals 93 -- 5.3 Block-SEFDM 97 -- 5.4 Turbo-SEFDM 102 -- 5.5 Practical Considerations and Experimental Demonstration 106 -- 5.6 Summary 112 -- References 112 -- 6 Non-orthogonal Multi-User Superposition and Shared Access 115 /Yifei Yuan -- 6.1 Introduction 115 -- 6.2 Basic Principles and Features of Non-orthogonal Multi-user Access 116 -- 6.3 Downlink Non-orthogonal Multi-user Transmission 121 -- 6.4 Uplink Non-orthogonal Multi-user Access 129 -- 6.5 Summary and Future Work 140 -- References 142 -- 7 Non-Orthogonal Multiple Access (NOMA): Concept and Design 143 /Anass Benjebbour, Keisuke Saito, Anxin Li, Yoshihisa Kishiyama and Takehiro Nakamura.
7.1 Introduction 143 -- 7.2 Concept 145 -- 7.3 Benefits and Motivations 148 -- 7.4 Interface Design 150 -- 7.5 MIMO Support 153 -- 7.6 Performance Evaluations 157 -- 7.7 Conclusion 166 -- References 167 -- 8 Major 5G Waveform Candidates: Overview and Comparison 169 /Hao Lin and Pierre Siohan -- 8.1 Why We Need New Waveforms 170 -- 8.2 Major Multicarrier Modulation Candidates 171 -- 8.3 High-level Comparison 178 -- 8.4 Conclusion 184 -- List of acronyms 185 -- References 186 -- Part II NEW SPATIAL SIGNAL PROCESSING FOR 5G -- 9 Massive MIMO for 5G: Theory, Implementation and Prototyping 191 /Ove Edfors, Liang Liu, Fredrik Tufvesson, Nikhil Kundargi and Karl Nieman -- 9.1 Introduction 192 -- 9.2 Massive MIMO Theory 194 -- 9.3 Massive MIMO Channels 199 -- 9.4 Massive MIMO Implementation 204 -- 9.5 Testbed Design 214 -- 9.6 Synchronization 224 -- 9.7 Future Challenges and Conclusion 227 -- Acknowledgments 228 -- References 228 -- 10 Millimeter-Wave MIMO Transceivers: Theory, Design and Implementation 231 /Akbar M. Sayeed and John H. Brady -- 10.1 Introduction 232 -- 10.2 Overview of Millimeter-Wave MIMO Transceiver Architectures 235 -- 10.3 Point-to-Point Single-User Systems 237 -- 10.4 Point-to-Multipoint Multiuser Systems 243 -- 10.5 Extensions 249 -- 10.6 Conclusion 250 -- References 251 -- 11 3D Propagation Channels: Modeling and Measurements 254 /Andreas F. Molisch -- 11.1 Introduction and Motivation 255 -- 11.2 Measurement Techniques 257 -- 11.3 Propagation Effects 260 -- 11.4 Measurement Results 263 -- 11.5 Channel Models 266 -- 11.6 Summary and Open Issues 268 -- Acknowledgements 269 -- Disclaimer 269 -- References 269 -- 12 3D-MIMO with Massive Antennas: Theory, Implementation and Testing 273 /Guangyi Liu, Xueying Hou, Fei Wang, Jing Jin and Hui Tong -- 12.1 Introduction 274 -- 12.2 Application Scenarios of 3D-MIMO with Massive Antennas 276 -- 12.3 Exploiting 3D-MIMO Gain Based on Techniques in Current Standards 277 -- 12.4 Evaluation by System-level Simulations 283.
12.5 Field Trials of 3D-MIMO with Massive Antennas 288 -- 12.6 Achieving 3D-MIMO with Massive Antennas from Theory to Practice 292 -- 12.7 Conclusions 294 -- References 295 -- 13 Orbital Angular Momentum-based Wireless Communications: Designs and Implementations 296 /Alan. E. Willner, Yan Yan, Yongxiong Ren, Nisar Ahmed and Guodong Xie -- 13.1 EM Waves Carrying OAM 297 -- 13.2 Application of OAM to RF Communications 298 -- 13.3 OAM Beam Generation, Multiplexing and Detection 300 -- 13.4 Wireless Communications Using OAM Multiplexing 303 -- 13.5 Summary and Perspective 315 -- References 316 -- Part III NEW SPECTRUM OPPORTUNITIES FOR 5G -- 14 MillimeterWaves for 5G: From Theory To Practice 321 /Malik Gul, Eckhard Ohlmer, Ahsan Aziz, Wes McCoy and Yong Rao -- 14.1 Introduction 321 -- 14.2 Building a mmWave PoC System 322 -- 14.3 Desirable Features of a mmWave Prototyping System 323 -- 14.4 Case Study: a mmWave Cellular PoC 326 -- 14.5 Conclusion 352 -- References 353 -- 15 *5G Millimeter-wave Communication Channel and Technology Overview 354 /Qian (Clara) Li, Hyejung Jung, Pingping Zong and Geng Wu -- 15.1 Introduction 354 -- 15.2 Millimeter-wave Channel Characteristics 355 -- 15.3 Requirements for a 5G mmWave Channel Model 357 -- 15.4 Millimeter-wave Channel Model for 5G 358 -- 15.5 Signal Processing for mmWave Band 5G RAT 365 -- 15.6 Summary 370 -- References 371 -- 16 General Principles and Basic Algorithms for Full-duplex Transmission 372 /Thomas Kaiser and Nidal Zarifeh -- 16.1 Introduction 373 -- 16.2 Self-interference: Basic Analyses and Models 374 -- 16.3 SIC Techniques and Algorithms 376 -- 16.4 Hardware Impairments and Implementation Challenges 386 -- 16.5 Looking Toward Full-duplex MIMO Systems 393 -- 16.6 Conclusion and Outlook 396 -- References 397 -- 17 Design and Implementation of Full-duplex Transceivers 402 /Katsuyuki Haneda, Mikko Valkama, Taneli Riihonen, Emilio Antonio-Rodriguez and Dani Korpi -- 17.1 Research Challenges 405 -- 17.2 Antenna Designs 409.
17.3 RF Self-interference Cancellation Methods 411 -- 17.4 Digital Self-interference Cancellation Algorithms 413 -- 17.5 Demonstration 423 -- 17.6 Summary 426 -- Acknowledgements 426 -- References 426 -- Part IV NEW SYSTEM-LEVEL ENABLING TECHNOLOGIES FOR 5G -- 18 Cloud Radio Access Networks: Uplink Channel Estimation and Downlink Precoding 431 /Osvaldo Simeone, Jinkyu Kang, Joonkhyuk Kang and Shlomo Shamai (Shitz) -- 18.1 Introduction 432 -- 18.2 Technology Background 432 -- 18.3 Uplink: Where to Perform Channel Estimation? 434 -- 18.4 Downlink: Where to Perform Channel Encoding and Precoding? 441 -- 18.5 Concluding Remarks 453 -- References 454 -- 19 Energy-efficient Resource Allocation in 5G with Application to D2D 456 /Alessio Zappone, Francesco Di Stasio, Stefano Buzzi and Eduard Jorswieck -- 19.1 Introduction 457 -- 19.2 Signal Model 459 -- 19.3 Resource Allocation 461 -- 19.4 Fractional Programming 462 -- 19.5 Algorithms 466 -- 19.6 Sequential Fractional Programming 469 -- 19.7 System Optimization 471 -- 19.8 Numerical Results 476 -- 19.9 Conclusion 480 -- References 481 -- 20 Ultra Dense Networks: General Introduction and Design Overview 483 /Jianchi Zhu, Xiaoming She and Peng Chen -- 20.1 Introduction 484 -- 20.2 Interference Management 487 -- 20.3 Mobility Management 495 -- 20.4 Architecture and Backhaul 499 -- 20.5 Other Issues in UDNs for 5G 503 -- 20.6 Conclusions 505 -- Acknowledgements 506 -- References 506 -- 21 Radio-resource Management and Optimization in 5G Networks 509 /Antonis Gotsis, Athanasios Panagopoulos, Stelios Stefanatos and Angeliki Alexiou -- 21.1 Introduction 510 -- 21.2 Background 511 -- 21.3 Optimal Strategies for Single-antenna Coordinated Ultradense Networks 514 -- 21.4 Optimal Strategies for Multi-antenna Coordinated and Cooperative Ultradense Networks 525 -- 21.5 Summary and Future Research Directions 533 -- Acknowledgments 534 -- References 534 -- Part V REFERENCE DESIGN AND 5G STANDARD DEVELOPMENT -- 22 Full-duplex Radios in 5G: Fundamentals, Design and Prototyping 539 /Jaeweon Kim, Min Soo Sim, MinKeun Chung, Dong Ku Kim and Chan-Byoung Chae.
22.1 Introduction 540 -- 22.2 Self-interference 541 -- 22.3 Analog Self-interference Cancellation 542 -- 22.4 Digital Self-interference Cancellation 547 -- 22.5 Prototyping Full-duplex Radios 550 -- 22.6 Overall Performance Evaluation 558 -- 22.7 Conclusion 559 -- References 559 -- 23 5G Standard Development: Technology and Roadmap 561 /Juho Lee and Yongjun Kwak -- 23.1 Introduction 561 -- 23.2 Standards Roadmap from 4G to 5G 562 -- 23.3 Preparation of 5G Cellular Communication Standards 570 -- 23.4 Concluding Remarks 575 -- References 575 -- Index 577.
Record Nr. UNINA-9910814414903321
Chichester, West Sussex, United Kingdom : , : John Wiley & Sons Inc., , [2016]
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