ADAE

Application Data Analytics Enablement

Services →
Introduced in Rel-18

ADAE is a 3GPP service capability that enables application layer data analytics by exposing network data and events to authorized Application Functions.

Category
Services
Introduced
Rel-18
Where
Services
Specifications
4 specs
ADAE Description Purpose Related Classification Detected Changes Specifications

Description

Application Data Analytics Enablement (ADAE) is a framework standardized by 3GPP to facilitate the consumption of network-generated analytics by external applications. It operates as a service capability within the 5G Core Network, specifically defined as a Network Exposure Function (NEF) service. The primary architectural principle involves an Application Function (AF) acting as a client that sends analytics subscription requests to the ADAE service, which is hosted by the NEF. The ADAE service then interfaces with various data sources within the network, such as the Network Data Analytics Function (NWDAF), Unified Data Repository (UDR), or other Network Functions (NFs), to collect, process, and deliver the requested analytics reports back to the AF.

The workflow begins with the AF sending a Nnef_AnalyticsExposure_Subscribe request message to the NEF, specifying the type of analytics needed (e.g., user-level mobility patterns, service experience analytics, network performance trends), the target user equipment (UE) group, and the reporting criteria (periodic or event-triggered). The NEF, acting as the ADAE service provider, authenticates and authorizes the AF request based on operator policies. It then translates the application-level analytics request into internal network procedures, potentially querying an NWDAF for the analytics computation or retrieving stored data from a UDR.

Key components in the ADAE architecture include the NEF (which hosts the ADAE service), the consuming AF, and the producer of analytics data (such as NWDAF). The interface between the AF and NEF for ADAE is defined as Nnef_AnalyticsExposure. The analytics data model is standardized, covering categories like UE mobility, communication patterns, and service experience, ensuring interoperability. The delivered analytics report contains insights like predicted UE movement, expected QoS sustainability, or abnormal service experience indicators, formatted according to 3GPP-defined data structures.

ADAE's role is to provide a secure, policy-controlled, and standardized channel for applications to leverage network intelligence without requiring direct, proprietary integrations with individual network functions. It enables use cases like crowd management, augmented reality optimization, and predictive service assurance by allowing applications to make data-driven decisions based on real-time or historical network analytics. The service supports both pull (request-response) and push (subscription-notification) models for data delivery, offering flexibility to application developers.

Purpose & Motivation

ADAE was created to address the growing demand from vertical industries and application providers to access valuable analytics derived from 5G network data. Prior to its standardization, applications had limited, non-standardized ways to obtain network insights, often relying on bilateral agreements or proprietary APIs that were not scalable, secure, or interoperable across different operator networks. This hindered the development of innovative services that could dynamically adapt based on network conditions or user behavior.

The motivation stems from the 5G vision of enabling network exposure and programmability. While NEF already exposed various network capabilities (like QoS control), there was a specific gap in exposing processed analytics, not just raw events or status. NWDAF was defined internally for network automation, but a standardized external interface for applications to consume these analytics was missing. ADAE fills this gap by defining a consistent service-based interface, data models, and authorization framework, allowing operators to monetize network data safely and enabling developers to build smarter applications.

It solves the problem of siloed network intelligence by providing a controlled funnel through which rich analytics—such as user mobility predictions, session aggregate bandwidth trends, or anomaly detection—can be securely shared with trusted third parties. This empowers new business models and enhances application performance, contributing to the overall 5G ecosystem of network-as-a-service and vertical industry support.

Classification

Part ofNWDAF
Related approachesNEF

Release Timeline

Detected Changes Across Releases

from 3GPP Change Requests

Specific changes extracted from the „Change history“ tables of 3GPP specifications (76 CRs across 2 releases). Complements the general historical overview above with the evidence-based evolution of this function.

Rel-18 22 changes

In Release 18, the ADAE function introduced several new analytics service APIs, including those for VAL performance, network slice performance, location accuracy, service API analytics, edge load, and UE-to-UE performance analytics. These additions expanded ADAE's capabilities to provide specific, consumable analytics data to client applications. The release also focused on refining these new APIs through updates, alignment, and corrections to their definitions, OpenAPI implementations, and service operation descriptions.

  • SS_ADAE_VALPerformanceAnalytics API TS 29.549CR0213
  • SS_ADAE_SlicePerformanceAnalytics API TS 29.549CR0215
  • SS_ADAE_LocationAccuracyAnalytics API TS 29.549CR0217
  • SS_ADAE_ServiceApiAnalytics API TS 29.549CR0219
  • Updates to API Definition for SS_ADAE_EdgeLoadAnalytics API and Solve ENs TS 29.549CR0221
  • Service Operation Description for SS_ADAE_EdgeLoadAnalytics API TS 29.549CR0222

+ 16 more changes

Rel-19 54 changes

In Release 19, the ADAE function introduced new analytics capabilities including collision detection analytics and location-related UE group analytics, each with their own fully defined API service operations, OpenAPI files, and data models. The release also specified new analytics outputs for functions like Edge Load, Location Accuracy, and Slice Performance, and formally defined the SS_ADAE_AIMLMemberCapabilityAnalytics API with its associated procedure and data model. Furthermore, it enhanced API definitions with updates to service operations and the formal inclusion of PUT and PATCH methods for the new analytics services.

  • SS_ADAE_collision_detection_analytics API definition TS 29.549CR0333
  • SS_ADAE_location-related_UE_group_analytics API definition TS 29.549CR0334
  • SS_ADAE_collision_detection_analytics OpenAPI file TS 29.549CR0349
  • SS_ADAE_collision_detection_analytics API service operations TS 29.549CR0350
  • SS_ADAE_location-related_UE_group_analytics OpenAPI file TS 29.549CR0351
  • SS_ADAE_location-related_UE_group_analytics API service operations TS 29.549CR0352

+ 48 more changes

Explore further

Broader topics and technologies where ADAE plays a role.

Defining Specifications

3GPP specifications that define or reference ADAE, with the latest known release. Sourced from the 3GPP document catalog — see methodology.

SpecificationTitleRelease
TS 23.700 vk00 XR Services Application Enablement Layer Rel-20
TS 24.559 vj41 Application Data Analytics Enablement Services Rel-19
TS 24.560 vj00 AIML Enablement (AIMLE) Services Stage 3 Protocol Rel-19
TS 29.549 vj40 SEAL API Specification for Vertical Applications Rel-19
Patrick Zandl

About the author: Patrick Zandl (b. 1974)

Telecommunications specialist, technology journalist (founder of the Mobil server), and developer who has been running since 2025 — the largest Czech-language resource on AI-assisted programming. Formerly Chief Wizard Architect at Prusa3D and head of development for Turris at CZ.NIC; currently a consultant and instructor on AI implementation in companies.