The latest research report indicates that the growing complexity of mobile networks and 5G NR (New Radio) infrastructure rollouts will drive SON (Self-Organizing Network) spending to $5.5 Billion by 2022.
SON technology minimizes the lifecycle cost of running a mobile network by eliminating manual configuration of network elements at the time of deployment, right through to dynamic optimization and troubleshooting during operation. Besides improving network performance and customer experience, SON can significantly reduce the cost of mobile operator services, improving the OpEx-to-revenue ratio and deferring avoidable CapEx.
1.1 Executive Summary
1.2 Topics Covered
1.3 Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned2 Chapter 2: SON & Mobile Network Optimization Ecosystem
2.1 Conventional Mobile Network Optimization
2.1.1 Network Planning
2.1.2 Measurement Collection: Drive Tests, Probes and End User Data
2.1.3 Post-Processing, Optimization & Policy Enforcement
2.2 The SON (Self-Organizing Network) Concept
2.2.1 What is SON?
2.2.2 The Need for SON
2.3 Functional Areas of SON
2.3.1 Self-Configuration
2.3.2 Self-Optimization
2.3.3 Self-Healing
2.3.4 Self-Protection
2.3.5 Self-Learning
2.4 Market Drivers for SON Adoption
2.4.1 The 5G Era: Continued Mobile Network Infrastructure Investments
2.4.2 Optimization in Multi-RAN & HetNet Environments
2.4.3 OpEx & CapEx Reduction: The Cost Savings Potential
2.4.4 Improving Subscriber Experience and Churn Reduction
2.4.5 Power Savings: Towards Green Mobile Networks
2.4.6 Alleviating Congestion with Traffic Management
2.4.7 Enabling Large-Scale Small Cell Rollouts
2.4.8 Growing Adoption of Private LTE & 5G-Ready Networks
2.5 Market Barriers for SON Adoption
2.5.1 Complexity of Implementation
2.5.2 Reorganization & Changes to Standard Engineering Procedures
2.5.3 Lack of Trust in Automation
2.5.4 Proprietary SON Algorithms
2.5.5 Coordination Between Distributed and Centralized SON
2.5.6 Network Security Concerns: New Interfaces and Lack of Monitoring
3 Chapter 3: SON Technology, Use Cases & Implementation Architectures
3.1 Where Does SON Sit Within a Mobile Network?
3.1.1 RAN
3.1.2 Mobile Core
3.1.3 Transport (Backhaul & Fronthaul)
3.1.4 Device-Assisted SON
3.2 SON Architecture
3.2.1 C-SON (Centralized SON)
3.2.2 D-SON (Distributed SON)
3.2.3 H-SON (Hybrid SON)
3.3 SON Use-Cases
3.3.1 Self-Configuration of Network Elements
3.3.2 Automatic Connectivity Management
3.3.3 Self-Testing of Network Elements
3.3.4 Self-Recovery of Network Elements/Software
3.3.5 Self-Healing of Board Faults
3.3.6 Automatic Inventory
3.3.7 ANR (Automatic Neighbor Relations)
3.3.8 PCI (Physical Cell ID) Configuration
3.3.9 CCO (Coverage & Capacity Optimization)
3.3.10 MRO (Mobility Robustness Optimization)
3.3.11 MLB (Mobility Load Balancing)
3.3.12 RACH (Random Access Channel) Optimization
3.3.13 ICIC (Inter-Cell Interference Coordination)
3.3.14 eICIC (Enhanced ICIC)
3.3.15 Energy Savings
3.3.16 COD/COC (Cell Outage Detection & Compensation)
3.3.17 MDT (Minimization of Drive Tests)
3.3.18 AAS (Adaptive Antenna Systems) & Massive MIMO
3.3.19 Millimeter Wave Links in 5G NR (New Radio) Networks
3.3.20 Self-Configuration & Optimization of Small Cells
3.3.21 Optimization of DAS (Distributed Antenna Systems)
3.3.22 RAN Aware Traffic Shaping
3.3.23 Traffic Steering in HetNets
3.3.24 Optimization of NFV-Based Networking
3.3.25 Auto-Provisioning of Transport Links
3.3.26 Transport Network Bandwidth Optimization
3.3.27 Transport Network Interference Management
3.3.28 Self-Protection
3.3.29 SON Coordination Management
3.3.30 Seamless Vendor Infrastructure Swap
3.3.31 Dynamic Spectrum Management & Allocation
3.3.32 Network Slice Optimization
3.3.33 Cognitive & Self-Learning Networks
4 Chapter 4: Key Trends in Next-Generation LTE & 5G SON Implementations
4.1 Big Data & Advanced Analytics
4.1.1 Maximizing the Benefits of SON with Big Data
4.1.2 The Importance of Predictive & Behavioral Analytics
4.2 Artificial Intelligence & Machine Learning
4.2.1 Towards Self-Learning SON Engines with Machine Learning
4.2.2 Deep Learning: Enabling “Zero-Touch” Mobile Networks
4.3 NFV (Network Functions Virtualization)
4.3.1 Enabling the SON-Driven Deployment of VNFs (Virtualized Network Functions)
4.4 SDN (Software Defined Networking) & Programmability
4.4.1 Using the SDN Controller as a Platform for SON in Transport Networks
4.5 Cloud Computing
4.5.1 Facilitating C-SON Scalability & Elasticity
4.6 Small Cells, HetNets & RAN Densification
4.6.1 Plug & Play Small Cells
4.6.2 Coordinating UDNs (Ultra Dense Networks) with SON
4.7 C-RAN (Centralized RAN) & Cloud RAN
4.7.1 Efficient Resource Utilization in C-RAN Deployments with SON
4.8 Unlicensed & Shared Spectrum Usage
4.8.1 Dynamic Management of Spectrum with SON
4.9 MEC (Multi-Access Edge Computing)
4.9.1 Potential Synergies with SON
4.10 Network Slicing
4.10.1 Use of SON Mechanisms for Network Slicing in 5G Networks
4.11 Other Trends & Complementary Technologies
4.11.1 Alternative Carrier/Private LTE & 5G-Ready Networks
4.11.2 FWA (Fixed Wireless Access)
4.11.3 DPI (Deep Packet Inspection)
4.11.4 Digital Security for Self-Protection
4.11.5 SON Capabilities for IoT Applications
4.11.6 User-Based Profiling & Optimization for Vertical 5G Applications
4.11.7 Addressing D2D (Device-to-Device) Communications & New Use Cases to be continued at https://www.supplydemandmarketresearch.com/son-self-organizing-networks-in-the-g-era-opportunities-challenges-strategies-forecasts-45574
Mr. Charles Lee
302-20 Misssisauga Valley Blvd, Missisauga, L5A 3S1, Toronto
Ph:12764775910
[email protected]