Intelligent sensing and communicaitons for internet of everything

Intelligent sensing and communicaitons for internet of everything

Intelligent Sensing and Communications for Internet of Everything introduces three application scenarios of enhanced mobile broadband (eMBB), large-scale machine connection (mMTC) and ultra reliable low latency communication (URLLC). A new communication model, namely backscatter communication (BackC...

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Detalles Bibliográficos
Otros Autores: Zhu, Zhengyu (Computer scientist), author (author), Chu, Zheng (Computer scientist) author, Li, Xingwang (Computer scientist) author
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : Academic Press [2022]
Materias:
Telecommunication.
Internet of things.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835425406719
Tabla de Contenidos:
  • Front Cover
  • Intelligent Sensing and Communications for Internet of Everything
  • Copyright
  • Contents
  • 1 Background and introduction
  • 1.1 Background
  • 1.2 Introduction
  • 1.2.1 Progress of 6G around the world
  • 1.2.2 6G vision and its performance indicators
  • 1.2.3 Potential key technologies of 6G
  • 1.2.3.1 Three major operating scenarios of 5G eMBB, mMTC, and URLLC
  • 1.2.3.2 Backscatter technology and Intelligent Reflecting Surface technology in the Internet of Things
  • 1.2.3.3 Unmanned aerial vehicle technology in IoE
  • 1.2.3.4 mmWave technology and Terahertz technology IoT communications
  • 1.2.3.5 Artificial intelligence technology in the Internet of Things
  • 1.2.3.6 Fog/edge computing technology and big data system with IoT
  • 1.3 Suggestions to promote 6G research and development
  • 2 Three major operating scenarios of 5G: eMBB, mMTC, URLLC
  • 2.1 Introduction
  • 2.1.1 The comprehensive introduction for eMBB
  • 2.1.2 The comprehensive introduction for mMTC
  • 2.1.3 The comprehensive introduction for URLLC
  • 2.1.3.1 The key technology of URLLC
  • 2.1.3.2 Performance metrics for URLLC
  • 2.1.3.3 Shortcomings of URLLC
  • 2.2 Opportunistic spectrum sharing for D2D-based URLLC
  • 2.2.1 System model
  • 2.2.1.1 Problem formulation
  • 2.2.2 Optimal resource allocation
  • 2.2.2.1 Scheme 1
  • 2.3 Cooperative wireless-powered NOMA relaying for B5G IoT networks with hardware impairments and channel estimation errors
  • 2.3.1 System model
  • 2.3.1.1 Energy harvesting
  • 2.3.1.2 Information transmission
  • 2.3.2 Performance analysis
  • 2.3.3 Exact outage probability
  • 2.3.4 Asymptotic OP
  • 2.3.4.1 OP of Df
  • 2.3.4.2 Diversity order
  • 2.3.4.3 Diversity order of Df
  • 2.3.4.4 Diversity order of Dn
  • 2.3.5 Energy efficiency (EE)
  • 2.3.6 Power optimization for the sum rate
  • 2.3.7 Performance evaluation results and discussion.
  • 2.3.8 Conclusion
  • 2.4 I/Q imbalance aware nonlinear wireless-powered relaying of B5G networks: security and reliability analysis
  • 2.4.1 System model
  • 2.4.2 Performance analysis
  • 2.4.3 Outage probability analysis
  • 2.4.3.1 Random relay selection
  • 2.4.3.2 Optimal relay selection
  • 2.4.4 Intercept probability analysis
  • 2.4.4.1 Direct transmission
  • 2.4.4.2 Transmission via relay
  • 2.4.4.3 Numerical results
  • 2.4.4.4 Reliability analysis
  • 2.4.4.5 Security analysis
  • 2.4.5 Conclusions
  • References
  • 3 Backscatter technology and intelligent reflecting technology surface technology in the Internet of Things
  • 3.1 Introduction
  • 3.1.1 The classification of backscatter communication systems
  • 3.1.1.1 Monostatic backscatter communication (MBC) systems
  • 3.1.1.2 Bistatic backscatter communication (BBC) systems
  • 3.1.1.3 Ambient backscatter communication (AmBC) systems
  • 3.1.2 Fundamental of backscatter modulations
  • 3.1.3 Interplay of backscatter with other technologies
  • 3.1.3.1 The introduction of NOMA technology
  • 3.1.3.2 The cognitive radio
  • 3.1.3.3 The wireless powered communication
  • 3.1.3.4 The device-to-device communication
  • 3.1.3.5 The visible light communication
  • 3.1.3.6 The long-range communication
  • 3.1.4 The physical layer security
  • 3.1.5 Intelligent reflecting surface assisted wireless powered IoT networks
  • 3.2 Secrecy analysis of ambient backscatter NOMA systems under I/Q imbalance
  • 3.2.1 System model
  • 3.2.2 Performance analysis
  • 3.2.2.1 Outage probability analysis
  • 3.2.2.2 Intercept probability analysis
  • 3.2.3 Numerical results
  • 3.2.4 Conclusions
  • 3.3 Hardware impaired ambient backscatter NOMA systems: reliability and security
  • 3.3.1 System model
  • 3.3.2 Performance analysis
  • 3.3.2.1 OP analysis
  • 3.3.2.2 IP analysis
  • 3.3.3 Numerical results
  • 3.3.4 Conclusions.
  • 3.4 Physical layer security of cognitive ambient backscatter communications for green Internet-of-Things
  • 3.4.1 System model
  • 3.4.2 Performance analysis
  • 3.4.2.1 Outage probability analysis
  • 3.4.2.2 Intercept probability analysis
  • 3.4.3 Numerical results
  • 3.4.4 Conclusions
  • 3.5 Future research prospects
  • 3.5.1 Security and privacy
  • 3.5.2 Backscatter communication circuitry design
  • 3.5.3 EM energy harvester
  • 3.5.4 Hardware impairments
  • References
  • 4 Unmanned aerial vehicle technology in IoE
  • 4.1 Introduction
  • 4.1.1 Research status and development trend
  • 4.1.2 Research on transmission theory of UAV Communication System
  • 4.1.3 Physical layer security of wireless power supply network based on IRS-UAV
  • 4.1.4 Channel estimation and beamforming for UAV Communication System
  • 4.2 Energy efficiency characterization in heterogeneous IoT system with UAV swarms based on wireless power transfer
  • 4.2.1 System model
  • 4.2.1.1 Network model
  • 4.2.1.2 Air to ground channel model
  • 4.2.1.3 Energy harvesting model
  • 4.2.1.4 Cell association
  • 4.2.1.5 Performance metrics
  • 4.2.2 Transmission probability of the IoT-Ts
  • 4.2.2.1 One-slot charging
  • 4.2.2.2 Two-slot charging
  • 4.2.3 Coverage probability
  • 4.2.3.1 Coverage probability of the PAIDs
  • 4.2.3.2 Coverage probability of the FAIDs
  • 4.2.4 Energy efficiency
  • 4.2.5 Numerical results
  • 4.2.6 Conclusion
  • 4.3 UAV-aided multiway NOMA networks with residual hardware impairments
  • 4.3.1 System model
  • 4.3.1.1 The first case
  • 4.3.1.2 The first case
  • 4.3.1.3 The second case
  • 4.3.2 Achievable sum rate analysis
  • 4.3.2.1 Achievable sum rate analysis
  • 4.3.2.2 High SNR analysis
  • 4.3.3 Numerical results &amp
  • discussion
  • 4.3.4 Conclusion
  • 4.4 A unified framework for HS-UAV NOMA networks: performance analysis and location optimization.
  • 4.4.1 System model and fading model
  • 4.4.1.1 System model
  • 4.4.1.2 Fading model
  • 4.4.2 Outage probability analysis
  • 4.4.3 Outage probability
  • 4.4.3.1 Asymptotic outage probability
  • 4.4.3.2 Diversity order
  • 4.4.3.3 System throughput
  • 4.4.4 Location optimization
  • 4.4.5 Numerical results
  • 4.4.6 Conclusion
  • 4.5 Future research prospects
  • References
  • 5 MmWave technology and Terahertz technology IoT communications
  • 5.1 Introduction
  • 5.1.1 mmWave technology IoT communications
  • 5.1.1.1 Related works
  • 5.1.2 Terahertz technology IoT communications
  • 5.1.3 MIMO-OFDMA Terahertz IoT networks
  • 5.2 Hybrid precoding design for wideband THz massive MIMO-OFDM systems with beam squint
  • 5.2.1 Antenna structure and hybrid precoding design
  • 5.2.1.1 Fully-connected structure and hybrid precoding design
  • 5.2.1.2 Subarray structure and hybrid precoding design
  • 5.2.2 Simulation results
  • 5.2.3 Conclusions
  • 5.3 Robust beamforming designs in secure MIMO SWIPT IoT networks with a non-linear channel model
  • 5.3.1 System model
  • 5.3.1.1 Network model
  • 5.3.1.2 Transmission protocol
  • 5.3.1.3 Non-linear EH model
  • 5.3.2 Problem formulation and robust design methods
  • 5.3.2.1 Problem formulation
  • 5.3.2.2 Two-layer optimization approach
  • 5.3.2.3 Low-complexity SPCA algorithm
  • 5.3.2.4 Optimality analysis
  • 5.3.3 Computational complexity
  • 5.3.4 Simulation results
  • 5.3.5 Conclusion
  • 5.4 Robust design for intelligent reflecting surface assisted MIMO-OFDMA Terahertz IoT networks
  • 5.4.1 System model
  • 5.4.1.1 Problem formulation
  • 5.4.2 Solution of the weighted sum rate optimization problem
  • 5.4.2.1 Optimization of F and vm[k] under fixed Φ
  • 5.4.2.2 Optimization of Φ under fixed F and Vm[k]
  • 5.4.3 Extension to imperfect CSIs from IRS to users
  • 5.4.3.1 Optimization of F and vm[k] under fixed Φ.
  • 5.4.3.2 Optimization of Φ under fixed F and Vm[k]
  • 5.4.4 Numerical results
  • 5.4.5 Conclusion
  • 5.4.6 Proof of Theorem 5.4.1
  • References
  • 6 Artificial intelligence technology in the Internet of things
  • 6.1 Introduction
  • 6.2 Exploiting deep learning for secure transmission in an underlay cognitive radio network
  • 6.2.1 System model and problem formulation
  • 6.2.2 Conventional optimization based power allocation approach
  • 6.2.2.1 Perfect CSI
  • 6.3 Q-learning based task offloading and resources optimization for a collaborative computing system
  • 6.3.1 System model and problem formulation
  • 6.3.1.1 System model
  • 6.3.1.2 Computation model
  • 6.3.1.3 Local computing
  • 6.3.1.4 Collaborative cloud computing
  • 6.3.2 Wireless communication model
  • 6.3.3 MDP model of offloading decision process
  • 6.3.3.1 State space S
  • 6.3.3.2 Action set A
  • 6.3.3.3 Policy
  • 6.3.3.4 Loss function and reward
  • 6.3.4 Communication and computation resources optimization
  • 6.3.4.1 Uplink transmission power allocation
  • References
  • 7 Fog/edge computing technology and big data system with IoT
  • 7.1 Introduction
  • 7.1.1 MEC: overview and resource allocation
  • 7.1.1.1 MEC: overview
  • 7.1.1.2 Single-user and multiuser MEC
  • 7.1.1.3 MIMO-assisted MEC
  • 7.1.2 Massive MIMO-assisted MEC
  • 7.1.2.1 Motivation
  • 7.1.2.2 State-of-the-art
  • 7.2 Edge cache-assisted secure low-latency millimeter wave transmission
  • 7.2.1 Related works
  • 7.2.2 System model and problem formulation
  • 7.2.2.1 System model
  • 7.2.3 Problem formulation
  • 7.2.3.1 Problem solution
  • 7.2.3.2 Beamforming design at the fronthaul link
  • 7.2.3.3 Beamforming design at the access link
  • 7.2.4 Numerical results
  • 7.2.5 Conclusion
  • 7.3 Delay minimization for massive MIMO assisted mobile edge computing
  • 7.3.1 System model and problem formulation
  • 7.3.1.1 System model.
  • 7.3.1.2 Communication model.

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