MSAS

Multi-functional Satellite Augmentation System

Services →
Introduced in Rel-8 Also in: Services

MSAS is a satellite-based augmentation system that enhances the accuracy, integrity, and availability of GNSS signals like GPS for 3GPP user equipment by providing correction data and integrity monitoring.

Category
Services
Introduced
Rel-8
Where
Radio Access Network › NG-RAN (5G)
Also touches
1 segments
Specifications
11 specs
MSAS Description Purpose Related Classification Specifications

Description

The Multi-functional Satellite Augmentation System (MSAS) is a Satellite-Based Augmentation System (SBAS) standardized within 3GPP to improve the performance of Global Navigation Satellite Systems (GNSS) for cellular user equipment (UE). Operationally, MSAS refers to systems like the Japanese QZSS-based augmentation or similar regional SBAS (e.g., WAAS, EGNOS). It broadcasts correction data (for satellite orbit, clock, and ionospheric delays) and integrity information via geostationary satellites on the L1 frequency (1575.42 MHz). This data is received directly by GNSS-capable UEs or can be delivered to the UE through the 3GPP network as part of Assisted-GNSS (A-GNSS) protocols, significantly enhancing positioning accuracy and reliability.

Architecturally, MSAS integrates with the 3GPP location services (LCS) architecture. Key network elements include the Secure User Plane Location (SUPL) Enabled Terminal (SET), which is the UE, and the SUPL Location Platform (SLP). For control-plane solutions, the Serving Mobile Location Center (SMLC) or Evolved SMLC (E-SMLC) in LTE/NR communicates with the UE. The MSAS augmentation data can be provided to these network elements from reference networks or directly from SBAS service providers. The UE's GNSS receiver uses MSAS corrections to compute a more precise position, reducing errors from atmospheric effects and satellite ephemeris inaccuracies to sub-meter levels in open-sky conditions.

How it works involves the UE acquiring GNSS signals (e.g., GPS) and simultaneously decoding the MSAS augmentation signals from geostationary satellites. The correction parameters are applied in the positioning calculation algorithm. In A-GNSS modes, the network may provide MSAS correction data or integrity data to the UE over LTE or NR radio bearers using protocols like Radio Resource Control (RRC) or LTE Positioning Protocol (LPP). This assists UEs with weak direct satellite reception. MSAS also provides integrity flags, warning the UE if a particular GNSS satellite's signal is unreliable, which is critical for safety-of-life applications. Its role is to enable high-accuracy, high-integrity positioning services mandated for emergency calls (E911/E112), navigation, and emerging services like V2X, which depend on trustworthy location data.

Purpose & Motivation

MSAS was incorporated into 3GPP standards to address the inherent limitations of standalone GNSS in mobile environments, particularly for emergency services and commercial location-based applications. Standalone GPS/GNSS can have accuracy reduced to 10+ meters due to ionospheric delays, satellite clock errors, and ephemeris inaccuracies, and it lacks a certified integrity monitoring mechanism. For emergency call location (e.g., E112 in Europe), regulatory requirements demand improved accuracy and reliability, which augmentation systems like MSAS provide.

The historical motivation stems from aviation safety systems, where SBAS like WAAS and MSAS were developed to enable precision approaches. 3GPP recognized the value of these existing infrastructures for terrestrial mobile users. By integrating MSAS support, the standards enabled mobile networks to meet stricter positioning requirements without solely relying on network-based methods like Observed Time Difference of Arrival (OTDOA), which have deployment limitations. It solved the problem of providing ubiquitous, high-integrity positioning in both urban canyons (via assistance data) and open areas, enhancing services like turn-by-turn navigation, geofencing, and location-aware billing.

Furthermore, MSAS support future-proofed 3GPP systems for emerging applications in IoT and autonomous systems, where precise and reliable positioning is non-negotiable. It represents a convergence of satellite navigation and cellular communication technologies, allowing operators to offer enhanced location services by leveraging publicly available augmentation signals, thus reducing dependency on proprietary assistance data and improving interoperability across global regions with different SBAS providers.

Classification

Part ofGNSS
Related approachesA-GNSSSUPLLPPE-SMLC

Evolution Across Releases

Rel-8 Initial

Initial introduction of MSAS support within the 3GPP framework for LTE. Specifications defined the provision of MSAS assistance data (corrections, integrity) to UEs via the LTE Positioning Protocol (LPP) and the control-plane architecture involving the E-SMLC. This enabled enhanced A-GNSS for LTE devices, improving positioning performance for emergency services and commercial applications.

Explore further

Broader topics and technologies where MSAS plays a role.

Defining Specifications

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

SpecificationTitleRelease
TS 22.071 vj00 3GPP TS 22.071: Location Services (LCS) Stage 1 Rel-19
TS 25.305 vj00 UTRAN UE Positioning Stage 2 Rel-19
TS 25.306 vj00 UE Radio Access Capabilities Specification Rel-19
TS 25.331 vj00 UTRAN RRC Protocol Specification Rel-19
TS 25.423 vj00 UTRAN RNSAP Specification Rel-19
TS 25.433 vj00 Node B Application Part (NBAP) Protocol Rel-19
TS 25.453 vj00 PCAP Protocol Specification Rel-19
TS 36.355 vj00 LTE Positioning Protocol (LPP) Rel-19
TS 37.355 vj20 LTE Positioning Protocol (LPP) Rel-19
TS 38.455 vj10 NR Positioning Protocol A (NRPPa) Rel-19
TS 44.031 vj00 Radio Resource LCS Protocol (RRLP) 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.