(EMAILWIRE.COM, February 17, 2019 ) SON (Self-Organizing Network) 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.
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To support their LTE and HetNet deployments, early adopters of SON have already witnessed a spate of benefits – in the form of accelerated rollout times, simplified network upgrades, fewer dropped calls, improved call setup success rates, higher end-user throughput, alleviation of congestion during special events, increased subscriber satisfaction and loyalty, and operational efficiencies – such as energy and cost savings, and freeing up radio engineers from repetitive manual tasks.
Although SON was originally developed as an operational approach to streamline cellular RAN (Radio Access Network) deployment and optimization, mobile operators and vendors are increasingly focusing on integrating new capabilities such as self-protection against digital security threats, and self-learning through artificial intelligence techniques, as well as extending the scope of SON beyond the RAN to include both mobile core and transport network segments – which will be critical to address 5G requirements such as end-to-end network slicing. In addition, dedicated SON solutions for Wi-Fi and other access technologies have also emerged, to simplify wireless networking in home and enterprise environments.
Largely driven by the increasing complexity of today’s multi-RAN mobile networks – including network densification and spectrum heterogeneity, as well as 5G NR (New Radio) infrastructure rollouts, global investments in SON technology are expected to grow at a CAGR of approximately 11% between 2019 and 2022. By the end of 2022, SNS Telecom & IT estimates that SON will account for a market worth $5.5 Billion.
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Table Of Content
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 Mentioned
2 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
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