GAD

Universal Geographical Area Description

Services →
Introduced in Rel-4

GAD is a standardized format for describing geographical areas in 3GPP networks to enable location-based services by providing a common language to define regions, zones, or points.

Category
Services
Introduced
Rel-4
Where
Core Network › 5G Core
Specifications
5 specs
GAD Description Purpose Related Classification Detected Changes Specifications

Description

Universal Geographical Area Description (GAD) is a fundamental concept within 3GPP specifications that provides a standardized method for describing geographical areas. It is not a single protocol but a set of definitions and encodings used across various network interfaces and applications. GAD defines how geographical information—such as points, ellipsoid points, polygons, and ellipsoid arcs—is structured and communicated. This structured description allows network entities and user equipment to unambiguously interpret location-related data.

The architecture of GAD is embedded within the broader location services (LCS) framework of 3GPP. It is utilized in signaling messages between core network nodes, such as the Gateway Mobile Location Centre (GMLC) and the Mobile Switching Centre (MSC), as well as in protocols like the Mobile Application Part (MAP) and Diameter. The GAD format includes parameters for shape, coordinates, and uncertainty, enabling precise or approximate area definitions depending on the service requirement. For instance, it can describe a cell coverage area, a predefined geofence, or a target region for location-based alerts.

Key components of GAD include the shape type (e.g., ellipsoid point, polygon), the geographical coordinates (typically using the World Geodetic System 1984, WGS84), and associated uncertainty or confidence levels. These components are encoded according to ASN.1 rules specified in 3GPP technical specifications. The role of GAD is critical for enabling services like emergency caller location, location-based charging, fleet management, and enhanced 911 (E911). By providing a universal format, it ensures interoperability between different network elements and across different generations of mobile networks, from GSM to 5G.

Purpose & Motivation

GAD was created to solve the problem of inconsistent and proprietary geographical descriptions in early mobile networks. Before standardization, vendors and operators used ad-hoc methods to define areas, leading to interoperability issues and hindering the rollout of location-based services. The need for a universal format became pressing with regulatory requirements for emergency services (e.g., E112 in Europe) and the commercial potential of location-aware applications.

The historical context lies in the evolution of GSM into 3G, where location services became a mandated feature. GAD, introduced in Release 4, provided a common language that allowed network equipment from different manufacturers to exchange geographical information seamlessly. It addressed limitations such as the inability to precisely define complex geographical zones or to convey uncertainty in location estimates. By standardizing the description, GAD enabled the development of a wide range of services that rely on accurate and interpretable area definitions, from navigation aids to geofencing for IoT devices.

Classification

Part ofLCS
Specific typesGAI
Related approachesGMLC

Release Timeline

Detected Changes Across Releases

from 3GPP Change Requests

Specific changes extracted from the „Change history“ tables of 3GPP specifications (4 CRs across 3 releases). Complements the general historical overview above with the evidence-based evolution of this function.

Rel-15 1 change

In Release 15, the primary new introduction for the GAD function was the specification of **GAD shape(s) for high accuracy positioning**. This enhancement expanded the universal Geographical Area Description framework to support more precise location reporting, building upon the existing WGS 84 reference system used for coding locations.

  • GAD shape(s) for high accuracy positioning TS 23.032CR0015
Rel-17 2 changes

In Release 17, the GAD function was enhanced to support a location estimate defined in Local Coordinates, providing an alternative to the standard WGS 84 reference system. Furthermore, a new high accuracy GAD shape with scalable uncertainty was introduced, allowing for more precise and adaptable location descriptions.

  • GAD shape for location estimate in Local Coordinates TS 23.032CR0018
  • Introducing new high accuracy GAD shape with scalable uncertainty TS 23.032CR0021
Rel-18 1 change

In Release 18, the primary enhancement for the Universal Geographical Area Description (GAD) function was the introduction of new GAD shapes. Specifically, these new shapes were defined to support the reporting of location results from ranging and sidelink positioning procedures. This expansion of supported shapes allows the GAD to more accurately represent location information derived from these specific, device-to-device positioning techniques.

  • New GAD Shapes for Ranging and Sidelink Positioning Location Results TS 23.032CR0022

Explore further

Broader topics and technologies where GAD plays a role.

Defining Specifications

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

SpecificationTitleRelease
TS 23.032 vj00 Universal Geographical Area Description Rel-19
TS 24.080 vj20 Mobile radio interface layer 3 supplementary services Rel-19
TS 29.515 vj50 Ngmlc Service Based Interface Protocol Rel-19
TS 29.518 vj50 AMF Service Based Interface Protocol Rel-19
TS 43.318 vj00 Generic Access Network (GAN) Stage 2 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.