How 3GPP Release 16 Enhances 5G Network Performance with NWDAF, MDT, and SON
3GPP Release 16: Smarter Data Collection for Better 5G Network Performance
The development of 5G networks is moving quickly. After 3GPP Release 15 laid the groundwork for 5G New Radio (NR) and network slicing, Release 16 aims to make these networks smarter, more autonomous, and centered around data.
A major part of this evolution is the introduction of advanced methods for data collection and analytics, which are designed to boost network performance, optimize processes, and enhance automation.
The image above highlights how Enhanced Network Automation (eNA), Minimization of Drive Testing (MDT), and Self-Organizing Networks (SON) work together to ensure ongoing performance improvements and operational efficiency.
Overview of Data Collection Enhancements in 3GPP Release 16
Release 16 builds on what was introduced in Release 15 by focusing more on data analytics and automation within the 5G core and RAN (Radio Access Network).
The goal:
To allow networks to automatically learn, optimize, and adjust based on real-time data—cutting down on manual work and improving user experience.
These improvements come from three main areas:
Enhanced Network Automation (eNA) through NWDAF
Minimization of Drive Testing (MDT)
Self-Organizing Networks (SON)
Each has a specific role in how the network gathers, processes, and uses performance data.
Enhanced Network Automation (eNA) with NWDAF
What is NWDAF?
The Network Data Analytics Function (NWDAF) was introduced in 3GPP Release 15 to provide network slice-level analytics. Release 16 expands NWDAF’s capabilities, enhancing its ability to gather, analyze, and share network performance data for automation and smarter decision-making.
Key Roles of NWDAF in Release 16:
Serves as a centralized analytics hub within the 5G Core.
Collects data from Network Functions (NFs), Application Functions (AFs), and Operations, Administration, and Maintenance (OAM) systems.
Provides insights back to these functions to dynamically optimize operations.
How It Works (as shown in the image):
Input: Data flows into NWDAF from 5GC, NF, and AF components.
Processing: NWDAF does local analytics and integrates them with data from repositories.
Output: Results are sent back to 5G Core functions for adaptable network decisions.
Enhancements Introduced in Release 16:
Expanded from network slicing to cover full end-to-end data collection and sharing.
Integrated with OAM systems for closed-loop automation.
Added support for exposing data to third-party analytics applications via APIs.
Benefits of Enhanced NWDAF:
Automation: Facilitates real-time changes to network parameters.
Visibility: Provides in-depth analytics for troubleshooting and proactive planning.
Efficiency: Cuts down the need for manual configuration through AI insights.
In essence, NWDAF acts as the brain behind 5G automation, forming the analytical backbone for self-optimizing networks.
Minimization of Drive Testing (MDT)
The Problem with Traditional Drive Testing
In previous cellular generations, drive testing involved using physical vehicles with specialized tools to assess coverage, signal strength, and quality. This was a costly and time-consuming method with many limitations.
MDT in 3GPP Release 16
Minimization of Drive Testing (MDT) brings user equipment (UE)-based measurements into play for passive or active collection of network performance data, eliminating the need for extensive physical drive tests.
Types of MDT:
Logged MDT: * The UE records measurements (like signal strength, delay, throughput) while moving through the network. This data is uploaded to the operator’s servers later.
Immediate MDT: * Measurements are reported in real time while the UE is connected.
Key Data Collected:
Average delay and latency metrics
Sensor and mobility information
Layer 2 (L2) metrics and accessibility data
Mobile history and location context
Benefits of MDT:
Lower operational costs: No longer need large-scale field testing.
Better coverage analysis: Real data from actual users gives more accurate results.
Faster troubleshooting: Operators can quickly pinpoint issues.
QoS verification: Ensures a consistent quality of service for end-users.
Release 16 Enhancements:
Now includes new use cases like IoT sensor data collection and location information reporting.
Added mechanisms for QoS verification and optimization feedback loops.
MDT becomes an indispensable tool for 5G operators—enabling data-driven coverage optimization and ensuring a good experience for customers.
Self-Organizing Networks (SON)
What is SON?
The concept of Self-Organizing Networks (SON), first introduced with LTE, has significantly advanced in 5G through Release 16. SON enables networks to configure, optimize, and heal themselves using automation.
SON in Release 16 focuses on:
Mobility Robust Optimization (MRO): Enhances handover decisions and minimizes call drops.
Mobility Load Balancing (MLB): Dynamically redistributes traffic across neighboring cells to avoid congestion.
REACH Optimization: Improves coverage and signal reach in tricky environments.
How SON Works (as shown in the image):
Devices (UEs) regularly report measurements back to the network.
The network exchanges data between nodes using enhanced interfaces like N2 and Xn.
SON algorithms leverage this information to make real-time adjustments to configuration parameters.
Benefits of SON:
Less human intervention: Networks adjust themselves based on analytics.
Better mobility and performance: Seamless handovers and optimal traffic distribution.
Reduced operational expenses (OPEX): Automated configurations cut down on manual tuning.
Quicker deployments: Ideal for dense 5G setups with many small cells.
Release 16 boosts SON by allowing device-based reporting and enhancing coordination across RAN nodes, leading to more effective and adaptive network management.
Key Benefits of 3GPP Release 16 Data Enhancements
a. Enhanced Automation
NWDAF and SON together enable full automation throughout the network’s lifecycle—from setup to optimization.
b. Real-Time Performance Monitoring
MDT allows operators to keep an eye on performance without relying on traditional testing methods.
c. Improved QoS and User Experience
Ongoing data feedback loops help maintain consistent service quality.
d. Cost and Time Efficiency
Automation and UE-based data collection significantly lower operational costs.
e. Foundation for 5G-Advanced (Release 17 and beyond)
This analytics-driven approach lays the groundwork for AI-native network operations in future releases.
Practical Example: 5G Network Optimization in Urban Areas
Picture a busy urban environment where handovers are frequent and traffic loads vary:
MDT gathers real user measurements, including latency spikes and coverage gaps.
NWDAF analyzes traffic patterns and mobility trends.
SON automatically tweaks handover thresholds and redistributes loads across cells.
This closed-loop process guarantees a seamless user experience, even during peak times.
Conclusion
3GPP Release 16 represents a significant shift in the development of 5G — moving from static management to smart, data-driven automation.
With NWDAF, MDT, and SON, operators have the tools to collect, analyze, and respond to network data in real-time. This results in a smarter, more resilient, and self-optimizing network that can deliver consistent high-quality connectivity across various use cases.
As we look ahead to 5G-Advanced (Release 17 and beyond), these foundational upgrades will push the telecom sector closer to an intelligent network era.