Self-Organizing Networks (SON) for 5G: A Comprehensive Overview

Article by Abhijeet Kumar

Introduction

Self-organising networks (SON) have become pivotal in efficiently managing next-generation wireless networks. With the rapid expansion and complexity of 5G networks, operators need robust automation to optimize network performance and enhance user experience. SON provides this automation by enabling networks to automatically configure, optimize, and heal themselves, reducing the need for manual interventions and operational costs.

The concept of SON was introduced in earlier generations of mobile networks but has gained significant traction with the advent of 5G due to its scalability and flexibility. This article delves into the technical intricacies of SON for 5G as outlined in the 3GPP TS 28.313 V18.1.0 specification, exploring its architecture, use cases, and the business and technical requirements essential for its implementation.

SON Architecture and Concepts

SON can be classified into three main categories based on where the SON algorithms are executed:

1. Centralized SON (C-SON):
2. Distributed SON (D-SON):
3. Hybrid SON:

SON can be classified into three main categories based on where the SON algorithms are executed:

Centralized SON (C-SON):

The SON algorithm runs in a centralized management system, typically the 3GPP management system. This centralization allows for the monitoring, analysis, decision-making, execution, and evaluation of network performance.

It can be further divided into:

Cross Domain-Centralized SON: Where algorithms run at the Cross Domain level, managing multiple network domains.

Domain-Centralized SON: Algorithms operate within a specific domain, such as the RAN or Core Network.

Distributed SON (D-SON):

The algorithms are implemented directly at the network nodes (e.g., base stations). This decentralization allows the network to respond more dynamically to local changes without the need for central coordination.

D-SON supports functions like Mobility Robustness Optimization (MRO), Automatic Neighbor Relation (ANR) management, and Load Balancing Optimization (LBO).

Hybrid SON:

A combination of centralized and distributed approaches, allowing for flexibility in algorithm placement and execution. Hybrid SON uses centralized data analysis for decision-making while distributing specific actions to the network nodes.

These SON categories are essential for addressing the diverse requirements of 5G networks, including network slicing, dynamic resource allocation, and multi-vendor integration.

Key SON Functions in 5G

1. RACH Optimization
2. Mobility Robustness Optimization (MRO)
3. Automatic Neighbor Relation (ANR) Management
4. Load Balancing Optimization (LBO)
5. PCI Configuration and Re-configuration
6. Dual Active Protocol Stack (DAPS) Handover Management

Use Cases and Implementation Scenarios

SON's capabilities extend across a wide range of use cases, making it an indispensable tool for 5G network management:

1. Self-Configuration of New RAN Nodes
2. Self-Healing
3. Self-Optimization
4. Network Slicing Management
5. Integration with AI and ML

Technical and Business Requirements

The implementation of SON in 5G networks necessitates a set of stringent technical and business requirements to ensure its effectiveness:

1. Interoperability:
2. Scalability:
3. Security:
4. Latency and Reliability:
5. Cost Efficiency:

Future Directions and Challenges

While SON has already transformed network management, several challenges and future directions remain:

1. Integration with Emerging Technologies:
2. Standardization and Interoperability:
3. Real-time Data Processing:
4. Energy Efficiency:

SON Use Cases:

Self-Organizing Networks (SON) provide automation and optimization for 5G networks, addressing a variety of use cases to improve network performance, manage resources efficiently, and enhance user experience. Here are some of the primary SON use cases:

1. Automatic Neighbor Relation (ANR) Management

• Description: Automatically identifies and manages neighbour relationships between cells to ensure efficient handovers.
• Use Case: In dynamic network environments, such as dense urban areas, ANR management helps to minimize dropped calls and optimize handover performance by updating the neighbour cell list based on real-time data.

2. Mobility Robustness Optimization (MRO)

• Description: Enhances the handover process between cells, reducing call drops and maintaining connection stability during mobility events.
• Use Case: For high-speed mobility scenarios, such as users traveling on highways or trains, MRO ensures smooth transitions between cells, maintaining call and data session continuity.

3. Load Balancing Optimization (LBO)

• Description: Balances the traffic load across different cells to prevent congestion and improve network performance.
• Use Case: In high-density areas, such as stadiums or concert venues, LBO dynamically redistributes traffic to underutilized cells, ensuring consistent quality of service for all users.

4. Random Access Channel (RACH) Optimization

• Description: Optimizes the access procedure for devices attempting to connect to the network, reducing access delays and improving connectivity success rates.
• Use Case: In massive IoT deployments, where a large number of devices attempt to access the network simultaneously, RACH optimization ensures efficient management of initial access, reducing congestion and improving response times.

5. Physical Cell Identity (PCI) Configuration and Reconfiguration

• Description: Automates the assignment and reconfiguration of PCI to prevent conflicts and ensure seamless network operations.
• Use Case: When new cells are added to the network, PCI configuration ensures that each cell has a unique identifier, avoiding interference and ensuring proper cell identification for handovers.

6. Self-Configuration and Self-Optimization

• Description: Automates the initial configuration and ongoing optimization of network elements without human intervention.
• Use Case: For new base station deployments, self-configuration automatically integrates the new site into the network, while self-optimization adjusts parameters to adapt to changing network conditions, such as varying user traffic patterns.

7. Self-Healing

• Description: Detects and automatically resolves network issues, such as hardware failures or degraded performance, to maintain service continuity.
• Use Case: In the event of a base station failure, self-healing capabilities can reroute traffic through neighboring cells, minimizing service disruption and ensuring continued connectivity for users.

8. Energy Savings Management

• Description: Adjusts the power levels and operational states of network elements based on traffic conditions to reduce energy consumption.
• Use Case: During low-traffic periods, such as late-night hours, SON can deactivate or lower the power of certain cells, reducing energy consumption while maintaining coverage and service quality.

9. Conditional Handover (CHO) Management

• Description: Implements handovers based on pre-defined conditions, such as user speed or signal quality, to enhance mobility management.
• Use Case: For users moving at high speeds, such as on a train, CHO management triggers handovers at optimal points to avoid coverage gaps and maintain high-quality service.

10. Dual Active Protocol Stack (DAPS) Handover Management

• Description: Utilizes dual protocol stacks during handovers to maintain active connections across multiple network layers, ensuring seamless transition.
• Use Case: For critical applications requiring uninterrupted connectivity, such as telemedicine or autonomous vehicles, DAPS ensures that no data is lost during handovers between different cells or network layers.

11. Network Slicing Management

• Description: Automates the creation, configuration, and optimization of network slices to meet diverse service requirements.
• Use Case: For enterprise applications, SON can dynamically allocate network slices with specific performance characteristics, such as low latency for industrial automation or high throughput for media streaming.

12. Coverage and Capacity Optimization (CCO)

• Description: Adjusts the coverage and capacity parameters of cells to optimize network performance and resource utilization.
• Use Case: In areas with fluctuating demand, such as tourist hotspots, CCO dynamically reallocates resources to meet varying traffic loads, ensuring optimal coverage and capacity.

13. Plug and Play for Network Elements

• Description: Enables the automatic integration of new network elements into the existing infrastructure with minimal manual intervention.
• Use Case: When deploying new base stations or small cells, the plug-and-play capability allows for rapid setup and integration, reducing deployment time and operational costs.

14. Multi-Vendor Integration

• Description: Facilitates seamless integration and management of network elements from different vendors, ensuring interoperability and coordinated performance optimization.
• Use Case: In heterogeneous network environments, SON coordinates multi-vendor equipment, enabling unified management and optimization despite diverse hardware and software configurations.

15. Performance Management and Fault Detection

• Description: Continuously monitors network performance and detects faults, enabling proactive maintenance and optimization.
• Use Case: For large-scale 5G deployments, performance management tools identify potential issues before they impact users, allowing for quick resolution and maintaining high service quality.

Case Study :

RACH Optimization (Random Access Optimization) in 5G

Overview

RACH (Random Access Channel) Optimization is a crucial function within the Distributed Self-Organizing Network (D-SON) framework for managing the performance and efficiency of the random access process in 5G networks. This optimization ensures that User Equipment (UE) can connect to the network efficiently, with minimal delay and reduced access failures. This process is essential in scenarios with high device density, such as IoT environments and urban areas, where numerous devices attempt to access the network simultaneously.

Objectives of RACH Optimization

1. Reduce Access Delay: Minimize the time it takes for UEs to successfully connect to the network by optimizing RACH parameters.
2. Minimize Access Failures: Reduce the number of failed access attempts, which can occur due to resource congestion or misconfiguration of RACH parameters.
3. Improve Network Efficiency: Ensure optimal use of network resources by dynamically adjusting RACH parameters based on current network conditions and UE behavior.

Key Components of RACH Optimization

RACH optimization involves several key components and steps that are executed by the D-SON management function:

1. Setting and Enabling Targets
2. Performance Measurement and Analysis
3. Optimization Actions
4. Continuous Feedback and Adjustment

RACH Optimization Procedure

The RACH optimization procedure, as described in the 3GPP TS 28.313 specification, follows a structured loop to ensure continuous improvement of the random access process:

1. Configure Targets: The D-SON management function configures the targets for the RACH optimization function using the modifyMOIAttributes operation.
2. Enable RACH Optimization: If not already enabled, the D-SON management function enables the RACH optimization function for the target NR cell.
3. Collect and Analyze Data: The RACH optimization function receives RACH information reports from UEs and collects performance measurements related to RACH performance.
4. Evaluate Performance: The D-SON management function evaluates the RACH performance against the configured targets.
5. Update Targets or Parameters: If the performance does not meet the targets, the D-SON management function updates the RACH optimization targets or parameters accordingly.

This loop ensures that the network can dynamically adapt to changing conditions, providing efficient access for UEs under varying network loads.

Performance Measurements

Several performance measurements are used to monitor and evaluate the RACH optimization process:

1. Distribution of RACH Preambles Sent
2. UE Access Delay Probability per SSB
3. Number of Preambles Sent Probability

Control Information and Parameters

The RACH optimization function can be controlled using specific parameters defined in the MnS:

• RACH Optimization Control Parameter
• Legal Values for Control

Use Cases for RACH Optimization

1. Massive IoT Deployment
2. Urban Environments
3. Network Slicing

Challenges and Future Directions

Despite its benefits, RACH optimization in 5G faces several challenges:

1. High Device Density
2. Interference Management
3. AI and Machine Learning Integration

Mobility Robustness Optimization (MRO) in 5G

Overview

Mobility Robustness Optimization (MRO) is a key function within the Distributed Self-Organizing Network (D-SON) framework aimed at enhancing handover performance in mobile networks. It automatically configures and optimizes handover parameters to ensure seamless connectivity and reduce handover-related issues such as call drops or poor data throughput. This optimization is particularly critical in dense urban environments or scenarios involving high mobility, such as users in vehicles or trains.

Objectives of MRO

1. Minimize Handover Failures: Reduce handover failure events such as too early, too late, or handovers to the wrong cell.
2. Enhance User Experience: Improve the quality of service (QoS) by ensuring efficient and seamless handovers, minimizing disruptions during mobility events.
3. Optimize Resource Utilization: Adjust handover parameters dynamically to balance network load and resource utilization.

Key Components of MRO

MRO involves several key components and operations that are managed by the D-SON management function:

1. Target Setting and Enabling MRO Function
2. Performance Monitoring and Analysis
3. Optimization Actions

MRO Optimization Procedure

The MRO optimization procedure follows a structured loop to ensure continuous improvement of handover performance:

1. Configure Targets and Ranges
2. Enable MRO Function
3. Monitor and Analyze Performance
4. Execute Optimization Actions
5. Continuous Feedback Loop

Performance Measurements

Several performance measurements are used to monitor and evaluate the MRO process:

1. Total Handover Failure Rate
2. Intra-RAT and Inter-RAT Handover Failure Rates
3. Number of Handover Events

Reference: 3GPP 28.313