http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
5G Networks: Planning, Design and Optimization presents practical methods and algorithms for the design of 5G Networks, covering issues ranging from network resilience to how Big Data analytics can used in network design optimization. The book addresses 5G optimization issues that are data driven, high dimensional and clustered. The reader will learn: 5G concepts, how they are linked and their effect on the architecture of a 5G network Models of 5G at a network level, including economic aspects of operating a network The economic implications of scale and service diversity, and the incentive for optimal design and operational strategies Network topologies from a transport to a cloud perspective Theoretic foundations for network design and network optimization Algorithms for practical design and optimization of 5G subsystems based on live network projects Efficient Bayesian methods for network analytics The trade-off and multi-objective character of QoS management and cost saving Practical traffic and resilience measurement and QoS supervision Frameworks for performance analytics and network control This book will be 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. Christofer Larsson works as an independent researcher and consultant in network design traffic engineering, network performance evaluation and optimization.
Front Cover; 5G Networks; Copyright; Contents; Preface; 1 Concepts and Architectures in 5G; 1.1 Software-De ned Networking (SDN); Centralized and Distributed Control; Network Function Virtualization (NFV); OpenFlow; 1.2 IT Convergence; Big Data; Edge Computing; Security and Integrity; Energy Ef ciency; 1.3 Building Blocks; Optical Fiber; SD-WAN; Open Source Software; 1.4 Algorithms and Complexity Classes; Optimization Problems; Showing Problem Hardness; Algorithms for Hard Problems; Brute force; Analytical methods; Approximations; Heuristics; Problem restriction; Divide and conquer
Randomization2 Network Modeling and Analysis; 2.1 Basic Properties; 2.2 Graph Representations; 2.3 Connectivity; Depth-First Search; Breadth-First Search; 2.4 Shortest Paths; Dijkstra's Algorithm; The Bellman-Ford Algorithm; 2.5 Minimum Spanning Trees; Sparseness of Graphs; Example Topologies; The Traveling Salesman Problem; The Nearest Neighbor Algorithm; Incremental Insertion; k-Optimal Methods; 2.6 Network Resilience; Network Cuts; The Deletion-Contraction Principle; 3 Network Science; 3.1 The Small-World Phenomenon; 3.2 The Erdos-Rényi Model; Graph Evolution; Degree Distribution
Clustering Coef cient3.3 Scale-Free Networks; The Barabási-Albert Model; 3.4 Evolving Networks; 3.5 Degree Correlation; Average Next Neighbor Degree; The Correlation Coef cient; Structural Cut-Off; 3.6 Importance; 3.7 Robustness; 3.8 Attack Tolerance; 3.9 Fault Propagation; 3.10 Improving Robustness; 4 Self-Similarity, Fractality, and Chaos; 4.1 Self-Similarity: Causes and Implications; Smooth Traf c; The Poisson process; Bursty Traf c; The Markovian Additive Process; Long Range-Dependent Traf c; Fractional Brownian motion; 4.2 Stochastic Processes; Basic De nitions
Self-Similar and Long Range-Dependent Processes4.3 Detection and Estimation; Detection of Poisson Characteristics; Detection and Estimation of Long-Range Dependence and Self-Similarity; 4.4 Wavelet Analysis; 4.5 Fractal Maps; The Iterated Function System; The Fractal Dimension; Box counting dimension; Information dimension; Correlation dimension; Control Limits; Online Process Monitoring; 5 Optimization Techniques; 5.1 Optimization Problems in 5G; 5.2 Mixed-Integer Programs; Dynamic Programming; Branch-and-Bound; 5.3 Rounding; 5.4 Simulated Annealing; 5.5 Genetic Algorithms
Binary representationFitness function; Reproduction; Recombination (crossover); Mutation; 5.6 Swarm Algorithms; Ant Colony Optimization; Ant-based solution construction; Pheromone update; Particle Swarm Optimization; Parameters; Fire y Algorithm; 6 Clustering; 6.1 Applications of Clustering; 6.2 Complexity; 6.3 Cluster Properties and Quality Measures; Vertex Similarity; Expansion; Coverage; Performance; Conductance; 6.4 Heuristic Clustering Methods; k-Nearest Neighbor; k-Means and k-Median; 6.5 Spectral Clustering; Similarity Matrices; The e-neighborhood; k-Nearest neighbors; Laplacians
Includes bibliographical references (pages 387-393) and index.