5G/5G-Advanced Networks

Planning, Design, and Optimization

2nd Edition - April 15, 2025
Imprint: Academic Press
Author: Christofer Larsson
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 9 4 0 - 1
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 9 4 1 - 8

5G/5G Advanced Networks: Planning, Design and Optimization, 2nd edition, presents practical methods and algorithms for the design of 5G/5G Advanced Networks, covering topics that r… Read more

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5G/5G Advanced Networks: Planning, Design and Optimization, 2nd edition, presents practical methods and algorithms for the design of 5G/5G Advanced Networks, covering topics that range from network resilience to how streaming data analytics can be used in network orchestration and slice optimization. The book addresses 5G optimization issues that are data driven, high dimensional and clustered The new edition is extensively revised and updated with 5 new chapters: - Information theory applied to design targets and performance limits - 5G+radio interface and radio planning - Heavy traffic analysis - Network slicing - Machine learning methods for offline analytics This new edition now gives a broad and complete foundation in network optimization techniques for all subsystems of the cellular network by: - Showing how to optimize networks end-to-end, including dependencies between subsystems - Presenting network tuning methods to improve capacity, service quality, energy efficiency, and resilience – in relation to cost - Presenting designs of network slices with multi-objective performance targets The new edition also includes new practical hints sections that show algorithm implementation (end-to-end and unit testing), solution validation, bench-marking, potential pitfalls, the use of open-source software, illustration of code objects with examples in Python. 5G/5G Advanced Networks: Planning, Design and Optimization, 2nd edition, is an invaluable resource for telecom operators and service providers, university researchers, graduate students and network planners interested in practical methods for optimizing networks for large performance improvements and cost savings.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Dedication
Preface to the second edition
Preface
Chapter 1: 5G concepts and technologies
1.1. Introduction
1.1.1. Big data
1.1.2. Energy efficiency
1.2. Key features of 5G/5G-Advanced
1.2.1. AI/ML support
1.2.2. Massive MIMO
1.2.3. Mobility
1.2.4. Enhancements for network slicing
1.2.5. Internet of Things
1.3. Network architecture and protocols
1.3.1. Centralized and distributed control
1.3.2. Network function virtualization
1.3.3. Optical fiber
1.3.4. SD-WAN
1.3.5. Impact on design
1.4. 5G New Radio and massive MIMO
1.4.1. Open RAN
1.4.2. Virtualized RAN
1.4.3. Massive MIMO
1.4.4. Impact on design
1.5. Network slicing and service differentiation
1.5.1. Edge computing
1.5.2. Impact on design
1.6. Security concepts
1.7. Performance measures
1.8. Design and optimization
1.9. Summary and programming hints
1.9.1. Code structure
1.9.2. Open source software
1.9.3. Unit testing
Chapter 2: Network modeling and analysis
2.1. Introduction
2.2. Basic properties of networks
2.2.1. Link and node weights
2.2.2. File formats
2.3. Algorithms and complexity classes
2.3.1. Optimization problems
2.3.2. Showing problem hardness
2.3.3. Algorithms for hard problems
2.4. Graph representations
2.4.1. Adjacency and incidence matrices
2.4.2. The complement of a graph
2.4.3. The line graph
2.4.4. Planar graphs
2.5. Connectivity
2.5.1. Depth-first search
2.5.2. Breadth-first search
2.6. Shortest paths
2.6.1. Dijkstra's algorithm
2.6.2. The Bellman–Ford algorithm
2.7. Minimum spanning trees
2.7.1. Sparseness of graphs
2.7.2. Kruskal's algorithm
2.7.3. The Prim–Jarník algorithm
2.7.4. Stretch
2.8. Network flows
2.8.1. Basic flow theory
2.8.2. Maximum flow
2.8.3. Minimum cost flows
2.8.4. Multicommodity flows
2.9. Summary and programming hints
2.9.1. Implementing graph classes
2.9.2. Functions and testing
2.9.3. Python packages
Chapter 3: Clustering, partitioning, and assignment
3.1. Introduction
3.2. Applications of clustering
3.3. Cluster properties and quality measures
3.3.1. Vertex similarity
3.3.2. Expansion
3.3.3. Coverage
3.3.4. Performance
3.3.5. Conductance
3.4. Heuristic clustering methods
3.4.1. k-Nearest neighbor
3.4.2. k-Means and k-median
3.5. Spectral clustering
3.5.1. Similarity matrices
3.5.2. Laplacians
3.5.3. Spectral decomposition
3.5.4. Dimensionality reduction
3.6. Matching and assignment
3.7. Iterative methods
3.7.1. Uniform graph partitioning
3.8. Summary and programming hints
3.8.1. Matrices and linear algebra
3.8.2. Clustering algorithms
3.8.3. Spectral clustering
Chapter 4: Optimization techniques
4.1. Introduction
4.2. Multicriteria optimization
4.3. Mixed-integer programming
4.3.1. Dynamic programming
4.3.2. Branch-and-bound
4.4. Approximation and rounding
4.5. Evolutionary algorithms
4.5.1. Simulated annealing
4.5.2. Genetic algorithms
4.5.3. Swarm optimization
4.6. Machine learning
4.7. Summary and programming hints
4.7.1. Linear programming
4.7.2. Dynamic programming
4.7.3. Evolutionary algorithms
Chapter 5: Information, modulation, and coding
5.1. Introduction
5.2. Information and entropy
5.2.1. Entropy
5.2.2. Joint and conditional entropy
5.2.3. Mutual information
5.3. Modulation and coding
5.3.1. Modulation
5.3.2. Coding schemes
5.3.3. The 5G transmitter
5.4. Discrete-time channels
5.4.1. Channel capacity
5.4.2. The Shannon–Hartley theorem
5.5. Continuous-time channels
5.5.1. The Gaussian channel
5.5.2. Bandwidth limited channels
5.5.3. Fading
5.5.4. Spatial diversity
5.6. Radio cell capacity
5.6.1. Uplink channels
5.6.2. Downlink channels
5.7. Information flow in networks
5.7.1. Intercell interference
5.7.2. Max-flow-min-cut interpretation
5.8. Estimation and monitoring
5.8.1. Large deviations
5.8.2. Varadhan's lemma
5.9. Summary and programming hints
5.9.1. The AWGN channel
5.9.2. The 5G transmitter
Chapter 6: Radio network design
6.1. Introduction
6.2. Radio network capacity
6.2.1. Millimeter wave
6.2.2. Cell densification
6.2.3. Cell association
6.3. Antenna systems and wave propagation
6.3.1. Dipole antennas
6.3.2. Patch antennas
6.3.3. Phased array antennas
6.4. Capacity of massive MIMO channels
6.4.1. Point-to-point MIMO
6.5. Beamforming and massive MIMO
6.5.1. Beam sweeping
6.5.2. Thinning
6.6. Direction of arrival
6.6.1. The correlation method
6.6.2. Maximum likelihood estimation
6.6.3. Multiple signal classification
6.7. Distribution of base stations
6.7.1. Poisson point processes
6.8. Radio coverage and throughput
6.8.1. Radio coverage probability
6.8.2. Shannon rate
6.9. Channel assignment
6.9.1. Physical cell identity
6.9.2. PCI planning
6.10. Summary and programming hints
6.10.1. Simulating PPP
6.10.2. Voronoi cells
Chapter 7: Access network design
7.1. Introduction
7.2. Backhaul technologies
7.3. Capacitated spanning trees
7.3.1. Esau–Williams algorithm
7.4. Steiner trees
7.5. Centralized radio access networks
7.5.1. C-RAN architecture
7.5.2. Resilience in access networks
7.6. Dimensioning of backhaul links
7.7. Summary and programming hints
7.7.1. A Steiner tree heuristic
7.7.2. The Esau–Williams algorithm
7.7.3. Dimensioning a hierarchical network
Chapter 8: Reliability and performance analysis
8.1. Introduction
8.2. Concepts and definitions
8.2.1. Network cuts
8.2.2. Menger's theorems
8.3. Component reliability
8.3.1. Reliability
8.3.2. Availability
8.3.3. Failure models
8.4. System reliability
8.4.1. Reliability of series systems
8.4.2. Reliability of parallel systems
8.4.3. Reliability of k-out-of-n structures
8.5. Markov chain methods
8.5.1. Discrete-time Markov chains
8.5.2. Continuous-time Markov processes
8.5.3. Numerical solution of Markov chains
8.5.4. Randomization
8.5.5. Truncation of the state space
8.6. Bayesian estimation
8.6.1. Preliminaries
8.6.2. Bayesian point estimation
8.6.3. Prior distributions
8.6.4. The Gibbs sampler
8.7. Bayesian networks
8.8. Performance indices
8.9. Summary and programming hints
8.9.1. Markov chains
8.9.2. The Gibbs sampler
8.9.3. Bayesian networks
Chapter 9: Capacitated network design
9.1. Introduction
9.2. Cloud transport and hosting
9.2.1. Mobility support
9.2.2. Quality-of-service
9.3. Uncapacitated facility location
9.3.1. Assignment
9.3.2. Pruning
9.3.3. Assignment and pruning in the primal-dual formulation
9.3.4. Conflict resolution
9.4. Capacitated facility location
9.5. Resilient facility location
9.6. Spanners
9.6.1. Properties of spanners
9.6.2. A greedy algorithm for spanner construction
9.7. Allocation of software objects
9.7.1. Vertex cover
9.7.2. Bin packing
9.8. Job scheduling
9.8.1. Rounding
9.8.2. The scheduler
9.9. Summary and programming hints
9.9.1. Facility location
9.9.2. Spanners
9.9.3. Vertex cover
Chapter 10: Resilient backbone design
10.1. Introduction
10.2. Network resilience
10.3. Resilience measures
10.3.1. Minimum cuts
10.3.2. Spanning trees
10.3.3. Graph strength
10.3.4. The reliability polynomial
10.4. Link topologies
10.5. Minimum-cost survivable networks
10.5.1. Testing feasibility
10.5.2. Generating an initial solution
10.5.3. The search neighborhood
10.5.4. Algorithm summary
10.6. A primal-dual algorithm
10.6.1. Preliminaries
10.6.2. Feasibility and performance
10.6.3. The main algorithm
10.7. Incremental resilience
10.8. Summary and programming hints
10.8.1. Resilience measures
10.8.2. Resilience design
Chapter 11: Traffic routing
11.1. Introduction
11.1.1. The anatomy of optical networks
11.1.2. Elastic optical networks
11.2. Routing in optical networks
11.2.1. Recovery and service availability
11.2.2. Protection and restoration
11.2.3. Routing protocols
11.3. Resilient routing
11.3.1. Path computation
11.4. Dedicated and shared backup paths
11.4.1. The shortest pairs problem
11.4.2. Average path length
11.4.3. Protection-to-working capacity ratio
11.4.4. Some general results
11.5. Preplanned cycle protection
11.5.1. Finding cycles in graphs
11.5.2. p-Cycle design
11.5.3. The straddling span approach
11.5.4. Node failures
11.6. Algorithms for backup paths
11.6.1. The K-shortest path
11.7. Wavelength assignment
11.7.1. Graph coloring
11.7.2. The Douglas–Rachford algorithm
11.7.3. The Bron–Kerbosch algorithm
11.8. Multiconditional routing
11.9. Summary and programming hints
11.9.1. Yen's algorithm
11.9.2. p-Cycle design
Chapter 12: Traffic modeling and effective bandwidth
12.1. Introduction
12.2. Demand, workload, and throughput
12.2.1. Stochastic processes
12.2.2. Self-similar and long range-dependent processes
12.3. Causes and implications of self-similarity
12.3.1. Streaming and elastic traffic
12.3.2. Network control
12.3.3. The effect of TCP
12.4. Estimation of traffic characteristics
12.4.1. Variance-time analysis
12.4.2. The rescaled-range statistic
12.4.3. Wavelet multiresolution analysis
12.5. Models of aggregated demand
12.5.1. Fractional Brownian traffic
12.6. Effective bandwidth
12.6.1. The scaled cumulant generating function
12.6.2. Normal approximation
12.6.3. Improved approximation
12.6.4. Estimation of the scaled CGF
12.7. Resource allocation
12.7.1. Nonlinear optimization
12.7.2. The cut-saturation method
12.7.3. Moe's principle
12.8. Summary and programming hints
12.8.1. Simulating fractional Brownian motion
12.8.2. Wavelet multiresolution analysis
Chapter 13: Heavy traffic analysis
13.1. Introduction
13.2. Network stability
13.2.1. The Wardrop equilibrium
13.2.2. Congestion control and fairness
13.2.3. A primal algorithm
13.2.4. Time delays
13.3. Networks of queues
13.3.1. Open Jackson networks
13.3.2. The routing matrix
13.3.3. End-to-end delay
13.3.4. Closed queueing networks
13.3.5. The product-form solution
13.3.6. Solving the traffic equation
13.3.7. Generalized queueing networks
13.3.8. Convolution
13.3.9. Mean value analysis
13.4. Heavy traffic analysis
13.4.1. Brownian motion
13.4.2. The heavy traffic approximation
13.4.3. The M/M/1/K queue
13.4.4. A tandem queue
13.4.5. General networks
13.5. Network control
13.5.1. Admission control
13.5.2. Rerouting
13.6. Simulation of heavy traffic and queues
13.6.1. Discrete event simulation
13.6.2. The Erlang link
13.7. Summary and programming hints
13.7.1. Brownian motion
13.7.2. Discrete-event simulation
Chapter 14: Analytics on batch data
14.1. Introduction
14.2. Concepts and data sources
14.2.1. Traffic data
14.2.2. Subscriber profiles
14.2.3. Call and event data records
14.2.4. Network performance data
14.3. Artificial intelligence and machine learning
14.3.1. Decision trees
14.3.2. Neural networks
14.3.3. Graph neural networks
14.4. Cognitive network management
14.4.1. Fault management
14.4.2. Configuration management
14.4.3. Performance management
14.4.4. Accounting management
14.4.5. Security management
14.4.6. Concluding remarks
14.5. Self-organizing networks
14.5.1. Energy saving
14.5.2. Load balancing
14.5.3. Mobility management
14.6. Forecasting
14.6.1. Stationary time series
14.6.2. Long-range dependent traffic
14.7. Summary and programming hints
14.7.1. Decision trees
14.7.2. Neural networks
Chapter 15: Analytics on streaming data
15.1. Introduction
15.2. Data collection
15.3. Discretization
15.3.1. The discretization problem
15.4. Data sketches
15.4.1. Data stream models
15.4.2. Hash functions
15.4.3. Approximate counting
15.4.4. Counting distinct elements
15.4.5. Estimation of vector norms
15.4.6. The AMS algorithm
15.4.7. The Johnson–Lindenstrauss algorithm
15.4.8. The median algorithm
15.4.9. The count-min sketch
15.4.10. The count-median sketch
15.4.11. Heavy hitters
15.5. Detection
15.6. Classification
15.6.1. Logistic regression
15.6.2. Perceptron classifier
15.6.3. Random forest classifier
15.6.4. The approximate large margin algorithm
15.6.5. The online passive-aggressive algorithm
15.7. Regression
15.7.1. Linear regression
15.7.2. Hoeffding tree regression
15.7.3. Stochastic gradient tree regression
15.7.4. Streaming random patches ensemble regressor
15.8. Prediction
15.8.1. Multiresolution estimation
15.8.2. The bitmap algorithm
15.9. Summary and programming hints
15.9.1. Traffic capture
15.9.2. Parsing the IP packet header
15.9.3. Streaming analytics
Chapter 16: Dynamic resource management
16.1. Introduction
16.2. Dynamic routing
16.2.1. Characterization of traffic
16.2.2. Entropy
16.3. Traffic aggregation
16.4. Fairness and quality of service
16.4.1. Congestion control by traffic aggregation
16.4.2. Simulation of congestion control
16.4.3. Network topology
16.4.4. Node capabilities
16.4.5. Traffic distribution
16.4.6. Traffic simulation
16.4.7. Traffic aggregation
16.4.8. Routing strategy
16.4.9. Quality-of-service evaluation
16.4.10. The effect of traffic aggregation
16.4.11. Node-level traffic aggregation
16.4.12. Service-type traffic aggregation
16.4.13. Dynamic traffic aggregation
16.5. Congestion control
16.5.1. Traffic aggregation and minimum total delay routing
16.5.2. Dynamic traffic aggregation and shortest-path routing
16.5.3. Traffic aggregation and min-max-delay routing
16.6. Real-time reconfiguration
16.7. Summary and programming hints
16.7.1. Offline empirical entropy
16.7.2. Online empirical entropy
Chapter 17: Network slicing
17.1. Introduction
17.2. Service differentiation
17.3. Service function chains
17.4. Orchestration
17.4.1. OpenStack
17.4.2. OpenFlow controller
17.5. Virtual network embedding
17.5.1. Node mapping
17.5.2. Link mapping
17.6. Design and dimensioning of slices
17.7. Slice deployment and reconfiguration
17.7.1. Objective function
17.7.2. Side conditions
17.7.3. Solution methods
17.7.4. Multidimensional resource allocation
17.7.5. Cloud resources and descriptors
17.7.6. Optimization criteria
17.7.7. Algorithm for resource optimization
17.7.8. An example
17.8. Summary and programming hints
17.8.1. Betweenness centrality
17.8.2. Node mapping
Chapter 18: Internet of Things
18.1. Introduction
18.2. Applications
18.2.1. Building automation
18.2.2. Agriculture
18.2.3. Industrial automation
18.3. Field buses and industrial protocols
18.3.1. Multipath fading
18.3.2. Interference
18.3.3. Medium access control
18.3.4. Security functions
18.3.5. Higher layers
18.4. Wireless sensor networks
18.4.1. Routing protocols
18.4.2. Routing protocols for the Internet of Things
18.4.3. Energy models
18.5. Compressed sensing
18.6. Mobility modeling
18.6.1. Geometric models
18.6.2. Queueing models
18.6.3. Traffic flow theory
18.6.4. Other model types
18.6.5. Gibbsian interaction mobility model
18.6.6. Analysis of dependence
18.6.7. Fitting a distribution
18.6.8. Basic assumptions
18.6.9. Dependence structure
18.6.10. Simulation of source density
18.6.11. Gibbs sampler implementation
18.6.12. Simulation results
18.6.13. Mathematical analysis of the mobility model
18.6.14. Numerical solution of the one-dimensional model
18.6.15. Stochastic fields
18.6.16. Estimation for the one-dimensional model
18.6.17. Concluding remarks
18.7. Summary and programming hints
18.7.1. Modbus simulator
18.7.2. Basis pursuit
Index

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5G/5G-Advanced Networks

Planning, Design, and Optimization

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