Inclusive Radio Communications for 5G and Beyond

Contents
List of contributors
1 Introduction
	1.1 Technology trends and challenges
	1.2 Scope of the book
2 Radio propagation modeling methods and tools
	2.1 Propagation environments
		2.1.1 Introduction
		2.1.2 Outdoor environment
		2.1.3 Indoor environment
		2.1.4 Outdoor-to-indoor environment
		2.1.5 Train and other vehicular environments
		2.1.6 Body-centric environments
	2.2 Channel model classification
		2.2.1 Site-specific channel models
			2.2.1.1 Acceleration techniques for ray-based models
				2.2.1.1.1 Reduction of image trees
				2.2.1.1.2 Acceleration based on graph representation
				2.2.1.1.3 Acceleration based on preprocessing
				2.2.1.1.4 Discrete ray launching with visibility preprocessing and GPU parallelization
				2.2.1.1.5 Parallelization with the aid of cloud HPC platforms
			2.2.1.2 Applications of ray-optical wave propagation simulation methods to important systems and use cases of 5G cellular
				2.2.1.2.1 Application to mmWave frequencies
				2.2.1.2.2 Application to a wave propagation study in complex environments
				2.2.1.2.3 Elaborated diffuse scattering models
				2.2.1.2.4 Application to massive MIMO channel modeling
			2.2.1.3 Use of maps and point-clouds for ray-optical propagation modeling
				2.2.1.3.1 Wave-object interaction analysis based on point clouds
				2.2.1.3.2 Use of point clouds for above-6 GHz radio channel modeling
			2.2.1.4 Full-wave models and other physics-based models
				2.2.1.4.1 Volume electric field integral equation
				2.2.1.4.2 Method of moments
				2.2.1.4.3 Physical optics
				2.2.1.4.4 Physics-based model for path loss prediction in vegetated areas
		2.2.2 Geometry-based stochastic channel models (GSCM)
			2.2.2.1 GSCM and their features
			2.2.2.2 Requirements for 5G channel models are still not fulfilled in the 3GPP model
			2.2.2.3 Antenna modeling is a part of channel modeling
				2.2.2.3.1 Rotating antennas is a not trivial task
			2.2.2.4 Generality of models
			2.2.2.5 Deterministic modeling of ground reflections in mmWave cellular GSCM
			2.2.2.6 Probability of LOS and reflected paths is derived
			2.2.2.7 Map assisted LOS determination
			2.2.2.8 GSCM for non-terrestrial networks is under development
			2.2.2.9 Clustered delay line model is a degenerated reference model of GSCM
			2.2.2.10 Analytical SINR model in urban microcells is derived by considering environmental randomness in BS deployment
		2.2.3 Enhanced COST2100 model
			2.2.3.1 Visibility regions on base station sides
			2.2.3.2 Gain functions of multipath components
			2.2.3.3 Support of three-dimensional geometry
			2.2.3.4 Available parameters of the model
		2.2.4 Reference ITU-R path loss models
			2.2.4.1 Indoor environments
			2.2.4.2 Outdoor short range environment
			2.2.4.3 Clutter loss
			2.2.4.4 Building entry loss
		2.2.5 Models for dense multipath components
			2.2.5.1 Analysis of diffuse scattering
			2.2.5.2 Modeling reverberation by propagation graphs
				2.2.5.2.1 Application of propagation graph models to different scenarios
				2.2.5.2.2 Extension of the propagation graph models
				2.2.5.2.3 Propagation graph of reduced complexity
				2.2.5.2.4 Path arrival rate
				2.2.5.2.5 Combination of propagation graph and geometry-based channel model
			2.2.5.3 Experimental characterization of dense multipath components from channel measurements
				2.2.5.3.1 Directional and polarimetric characteristics
				2.2.5.3.2 Dependence on carrier frequency
	2.3 Algorithms for estimation of radio channel parameters
		2.3.1 Narrowband multipath component estimation
			2.3.1.1 An estimator for non-uniform angular dense multipath
			2.3.1.2 Specular multipath estimation from power spectra
			2.3.1.3 A Bayesian estimator for specular and dense multipath
		2.3.2 Wideband multipath component estimation
			2.3.2.1 A specular multipath estimator for ultra-wideband channels
			2.3.2.2 Joint delay and Doppler estimation for bistatic radar
			2.3.2.3 Comparison of multipath estimators for millimeter-wave channels
		2.3.3 Multipath component clustering
			2.3.3.1 Clustering based on kernel power density
			2.3.3.2 Sparsity-based clustering
			2.3.3.3 Clustering based on the Hough transform
		2.3.4 Large-scale parameter estimation with limited dynamic range
3 IRACON channel measurements and models
	3.1 Measurement scenarios
		3.1.1 Summary
			3.1.1.1 Cellular scenarios
			3.1.1.2 Vehicular scenarios
			3.1.1.3 Massive MIMO scenarios
			3.1.1.4 Long-range scenarios
			3.1.1.5 D2D, body-centric, and industrial scenarios
	3.2 mm-Wave and Terahertz channels
		3.2.1 Path loss and RMS delay spread
			3.2.1.1 Path loss
			3.2.1.2 Delay spread
			3.2.1.3 Specific scenarios: Indoor and confined environments
		3.2.2 Outdoor-to-indoor propagation
		3.2.3 Cross-polar discrimination, clustering, and massive MIMO
			3.2.3.1 Cross-polar discrimination
			3.2.3.2 Clustering
			3.2.3.3 Massive MIMO
		3.2.4 Millimeter-Wave and Terahertz channel simulations
		3.2.5 Other effects
			3.2.5.1 Human blockage
			3.2.5.2 Precipitations
			3.2.5.3 Drones
			3.2.5.4 Impact of antenna aperture and misalignment
	3.3 MIMO and massive MIMO channels
		3.3.1 MIMO channel measurement and analysis
			3.3.1.1 Channel sounder development
			3.3.1.2 Channel parameter characteristics
			3.3.1.3 Spatial correlation characteristics
			3.3.1.4 Frequency dependency characteristics
			3.3.1.5 Propagation mechanism identification
		3.3.2 Wireless simulation techniques
		3.3.3 Antenna development
		3.3.4 Electro-magnetic compatibility analysis
		3.3.5 Massive MIMO system evaluation
			3.3.5.1 Channel hardening concept
			3.3.5.2 Inter-user interference evaluation
			3.3.5.3 Signal processing techniques
	3.4 Fast time-varying channels
		3.4.1 Channel characterization of V2X scenarios
		3.4.2 COST-IRACON V2V channel model for urban intersections
		3.4.3 Wide band propagation in railway scenarios
		3.4.4 New paradigm for realizing realistic HST channels in the 5G mmWave band
	3.5 Electrical properties of materials
		3.5.1 Transmission loss above 6 GHz
		3.5.2 Material properties
4 Over-the-Air testing
	4.1 Introduction
	4.2 Field emulation for electrically large test objects
		4.2.1 Sectored MPAC
		4.2.2 Other methods
	4.3 Emulation of mmWave channels
	4.4 Extending the present framework
		4.4.1 Complexity reduction for field emulations
		4.4.2 Testing specific performance parameters
		4.4.3 Emulating human influence
		4.4.4 Testbeds, additional equipment
	4.5 Concluding remarks
5 Coding and processing for advanced wireless networks
	Introduction
	5.1 Advanced waveforms, coding, and signal processing
		5.1.1 Models and bounds
			Capacity from measurements
			Capacity using a relay
			Interference
			Secrecy
		5.1.2 Pre-coding and beam forming
		5.1.3 Channel estimation and synchronization
		5.1.4 New waveforms
	5.2 Distributed and cooperative PHY processing in wireless networks
		5.2.1 Cooperative relaying
		5.2.2 Wireless physical-layer network coding
			Network code maps and error rate performance
			Channel and network topology exploitation
		5.2.3 Distributed cooperative access networks
		5.2.4 Distributed sensing
	5.3 Massive MIMO
		5.3.1 Processing and coding for Massive MIMO
		5.3.2 Performance evaluation and modeling for Massive MIMO
	5.4 Full-duplex communications and HW implementation driven solutions
		5.4.1 Full-duplex communications
		5.4.2 HW specific models and implementations
6 5G and beyond networks
	6.1 Introduction
	6.2 Ad-hoc and V2V networks
		6.2.1 Prediction and reliability
		6.2.2 Network simulation/emulation platforms
		6.2.3 Energy harvesting
		6.2.4 Measurements for specific applications
	6.3 Spectrum management and sharing
		6.3.1 IoT/machine type communications
		6.3.2 Coexistence and sharing
		6.3.3 Field monitoring
		6.3.4 Virtualized networks
	6.4 Radio resource management and scheduling
		6.4.1 Resource allocation in wireless mesh networks
		6.4.2 RRM for D2D scenario
		6.4.3 RRM via frequency reuse
			RRM for DUDe scenario
		6.4.4 PCA for higher capacity
		6.4.5 Resource allocation and sharing for HetNets
		6.4.6 A RRM tool
	6.5 Heterogeneous networks and ultra dense networks
		6.5.1 Scenarios and capacity evaluation for small cell heterogeneous networks
		6.5.2 System level evaluation of dynamic base station clustering for coordinated multi-point
		6.5.3 Comparison of the system capacity between the UHF/SHF bands and millimeter wavebands
		6.5.4 Cost/revenue trade-off in small cell networks in the millimeter wavebands
		6.5.5 Effects of hyper-dense small-cell network deployments on a realistic urban environment
		6.5.6 Advanced management and service provision for ultra dense networks
			Location-aware self-organizing networks
			Performance evaluation and packet scheduling in Home eNodeB (HeNB) deployments
			Optimization techniques and Knapsack optimization for MLB in small cells
		6.5.7 IP mobility and SDN
		6.5.8 Digital geographical data and radio propagation models enhancement for mmWave simulation
			Summary and conclusions
	6.6 C-RAN
		6.6.1 Resource management in C-RAN
		6.6.2 C-RAN deployment
	6.7 SDN and NFV
		6.7.1 Virtual radio resource management model
		6.7.2 Analysis of VRRM results
	6.8 UAVs and flying platforms
		6.8.1 UAV trajectory design and radio resource management
		6.8.2 UAV-aided network planning and performance
	6.9 Emerging services and applications
		6.9.1 Smart grids
		6.9.2 Vehicular applications
		6.9.3 Public protection and disaster relief systems
7 IoT protocols, architectures, and applications
	7.1 Low power wide area networks
		7.1.1 LoRaWAN
		7.1.2 NB-IoT
	7.2 MAC and routing protocols for IoT
		7.2.1 6TiSCH protocol stack
		7.2.2 Joint scheduling and routing protocols
		7.2.3 Routing protocols and congestion control
	7.3 Vehicular communications
		7.3.1 Antenna design and integration
		7.3.2 High mobility performance analysis and modeling
	7.4 Energy efficient/constrained solutions for IoT
		7.4.1 Energy efficiency in IoT
		7.4.2 Energy harvesting aspects
	7.5 SDN and NFV for IoT
		7.5.1 Software-defined IoT networks
		7.5.2 Integrating different IoT technologies
		7.5.3 Virtualization of IoT
	7.6 Special applications of IoT
	7.7 Conclusions
8 IoT for healthcare applications
	8.1 Wearable and implantable IoT-health technology
		8.1.1 Channel measurement and modeling: On-body-to-off-body
			Analytical model
			Empirical models
			Narrowband measurements
			Wideband measurements
			Millimeter-wave measurements
		8.1.2 Channel measurement and modeling: On-body-to-on-body
		8.1.3 Channel measurement and modeling: In-body-to-on-body and in-body-to-off-body
			Human body implants
			Animal implants
		8.1.4 Human body phantoms and SAR measurement
			Dielectric properties of body tissues
			Electromagnetic phantoms for radioelectric measurements
			Human exposure to EM fields
	8.2 IoT-health networking and applications
		8.2.1 Networking and architectures
			Architectures for remote health monitoring
		8.2.2 Applications
			Localization
			Remote monitoring and crowdsensing
			Activity recognition and motion analysis
	8.3 Nanocommunications
		8.3.1 Nanocommunication mechanisms
		8.3.2 Interface with micro- and macro-scale networks
9 Localization and tracking
	9.1 Introduction
		9.1.1 New application scenarios, user requirements
		9.1.2 Technical challenges
		9.1.3 Expected features and limitations of 5G and current IoT technologies: Impact in positioning
			Features and limitations of current IoT technologies
			Features and shortcomings of 5G wireless networks
	9.2 Measurement modeling and performance limits
		9.2.1 Signal and channel model
		9.2.2 Received signal strength
		9.2.3 Time of arrival and time difference of arrival (TOA/TDOA)
		9.2.4 Angle of arrival (AOA)
		9.2.5 Joint measurements
	9.3 Position estimation, data fusion, and tracking
		9.3.1 Tracking and sensor fusion for moving persons and assets
		9.3.2 Fingerprinting and ray tracing for localization
		9.3.3 Advanced localization techniques
	9.4 Multipath-based localization and mapping
		9.4.1 Signal model and geometry model
		9.4.2 Technical challenges
			Estimation of multipath component parameters
			Data association
		9.4.3 Localization approaches
		9.4.4 Localization-and-mapping approaches
	9.5 System studies and performance limits
		9.5.1 Indoor localization systems
		9.5.2 Localization for vehicular networks
		9.5.3 Multi-system hybrid localization strategies
		9.5.4 Location awareness-based network optimization
	9.6 Testbed and prototyping activities
		9.6.1 Testbeds for GNSS-based localization activities
		9.6.2 Massive MIMO testbeds for localization
		9.6.3 Testbeds for localization activities based on battery-less tags
10 Perspectives
	10.1 Implementing 5G
	10.2 Preparing 6G
		10.2.1 Applications
		10.2.2 Technologies
Bibliography
List of acronyms
Index