Centimeter Level Augmentation Service

Description

Centimeter Level Augmentation Service (CLAS) is a standardized positioning service defined in 3GPP that provides extremely accurate location information to user equipment (UE) by enhancing Global Navigation Satellite System (GNSS) measurements with network-based correction data. The service architecture involves multiple components working together: the UE with GNSS capabilities, the cellular network infrastructure, and augmentation servers that generate and distribute correction information. CLAS operates by comparing the GNSS signals received at reference stations with known precise positions to calculate differential corrections for various error sources including satellite orbit and clock errors, ionospheric delays, and tropospheric effects.

The technical implementation of CLAS follows a client-server model where the UE acts as a client receiving correction data from network servers. The correction data is transmitted over cellular interfaces using standardized protocols defined in 3GPP specifications. The UE processes both the raw GNSS measurements and the received correction data to compute its position with centimeter-level accuracy. The service supports multiple GNSS constellations including GPS, GLONASS, Galileo, and BeiDou, and can provide corrections in various formats such as State Space Representation (SSR) or Observation Space Representation (OSR).

Key components in the CLAS architecture include the Location Management Function (LMF) which manages positioning sessions, the Network Exposure Function (NEF) that enables service exposure, and dedicated augmentation servers that generate correction data. The service interfaces with existing 5G positioning architecture defined in 3GPP Release 16, integrating with the NG-RAN and 5GC components. CLAS supports both real-time and post-processing scenarios, with real-time operation requiring low-latency transmission of correction data to maintain accuracy.

The correction data transmission employs efficient compression and encoding techniques to minimize bandwidth usage while maintaining precision. The service includes mechanisms for integrity monitoring, ensuring that users can trust the accuracy of the provided position information. CLAS operates within the broader 3GPP positioning framework, complementing other positioning methods like OTDOA, E-CID, and sensor-based positioning to provide a comprehensive location service portfolio.

Purpose & Motivation

CLAS was created to address the growing demand for high-precision positioning services in various industries including automotive, industrial automation, agriculture, and augmented reality. Traditional GNSS positioning typically provides meter-level accuracy, which is insufficient for applications requiring centimeter-level precision such as autonomous vehicle navigation, precision agriculture, construction site management, and drone operations. The limitations of standalone GNSS include atmospheric errors, satellite clock and orbit errors, and multipath effects that degrade positioning accuracy.

Previous approaches to achieving high-precision positioning relied on specialized ground-based augmentation systems like RTK (Real-Time Kinematic) networks or satellite-based augmentation systems (SBAS), but these often required dedicated infrastructure and lacked integration with cellular networks. CLAS integrates high-precision positioning capabilities directly into the 3GPP ecosystem, leveraging existing cellular infrastructure to distribute correction data efficiently. This integration enables widespread availability of centimeter-level positioning without requiring users to deploy specialized equipment or subscribe to separate augmentation services.

The creation of CLAS in 3GPP Release 16 was motivated by the need to support emerging use cases in 5G networks, particularly those defined by vertical industries. By standardizing the service within 3GPP, it ensures interoperability across different network operators and device manufacturers, creating a global ecosystem for high-precision positioning. CLAS addresses the limitations of previous cellular positioning methods that could not achieve centimeter-level accuracy, positioning 5G networks as a platform for mission-critical location-based services.

Key Features

• Centimeter-level positioning accuracy (1-10 cm)
• Support for multiple GNSS constellations (GPS, GLONASS, Galileo, BeiDou)
• Network-based differential correction distribution
• Integration with 5G positioning architecture
• Real-time and post-processing operation modes
• Efficient correction data compression and encoding

Evolution Across Releases

Defining Specifications