DL-PRS

Downlink Positioning Reference Signal

Physical Layer →
Introduced in Rel-16

DL-PRS is a downlink reference signal transmitted by the gNB to enable precise UE positioning by providing high-accuracy time and phase measurements for services like DL-TDOA and multi-RTT.

Category
Physical Layer
Introduced
Rel-16
Where
Radio Access Network › NG-RAN (5G)
Specifications
7 specs
DL-PRS Description Purpose Related Classification Detected Changes Specifications

Description

The Downlink Positioning Reference Signal (DL-PRS) is a physical layer signal defined in 3GPP 5G New Radio (NR) specifically for positioning purposes. It is a pseudo-random sequence transmitted by the gNodeB (gNB) across configured time and frequency resources within the positioning reference signal occasion. The DL-PRS is designed to have low cross-correlation properties, allowing a User Equipment (UE) to distinguish signals from multiple neighboring gNBs, even in dense deployment scenarios. Its configuration, including bandwidth, comb pattern, periodicity, muting patterns, and resource set structure, is highly flexible and signaled to the UE via RRC and LPP protocols to optimize positioning performance for different environments and accuracy requirements.

Architecturally, DL-PRS generation and transmission are managed by the gNB's physical layer based on parameters provided by higher layers, often originating from the Location Management Function (LMF) in the core network. The UE receives these configuration parameters and performs measurements on the DL-PRS, such as Reference Signal Time Difference (RSTD) for DL-TDOA or Rx-Tx time difference for multi-RTT. The signal's design emphasizes high time-of-arrival (ToA) estimation accuracy and robustness against interference. Key components include the PRS sequence generation based on a gold sequence, the mapping to resource elements in an OFDM symbol grid, and the support for beamforming to enhance signal quality and coverage.

In the network's operation, the LMF orchestrates the positioning session, determining which gNBs should transmit DL-PRS and with what configuration. The UE measures the DL-PRS from multiple gNBs and reports the measurements (e.g., RSTD) back to the LMF via the serving gNB. The LMF then uses these measurements in positioning algorithms to compute the UE's location. The role of DL-PRS is critical as it provides the common, high-quality measurement reference that enables network-based, downlink-centric positioning methods, forming a core part of the 5G positioning framework alongside uplink and uplink-downlink methods.

Purpose & Motivation

DL-PRS was introduced in 3GPP Release 16 to address the growing demand for high-accuracy, low-latency positioning services in 5G networks, which previous cellular systems like LTE could not adequately satisfy. Prior to NR, positioning primarily relied on signals not specifically optimized for positioning, such as Cell-specific Reference Signals (CRS) in LTE, which offered limited accuracy (tens of meters) and were not designed for dense multi-cell measurements. The limitations of these legacy approaches included insufficient bandwidth, poor time-of-arrival resolution, and susceptibility to interference, making them unsuitable for emerging use cases like industrial IoT, autonomous vehicles, and augmented reality.

The creation of DL-PRS was motivated by the need for a dedicated, network-controlled downlink signal that could provide centimeter- to meter-level accuracy. It solves the problem of obtaining precise timing measurements from multiple base stations by offering a signal with configurable high bandwidth, low cross-correlation, and predictable transmission patterns. This enables advanced positioning techniques like DL-TDOA and multi-RTT to function effectively. Historically, the development was driven by requirements from vertical industries and regulatory mandates (e.g., E911), pushing 3GPP to standardize a native, high-performance positioning solution as an integral part of the 5G NR air interface.

Classification

Part ofDL-TDOA
Related approachesRSTD

Release Timeline

Detected Changes Across Releases

from 3GPP Change Requests

Specific changes extracted from the „Change history“ tables of 3GPP specifications (2 CRs across 1 releases). Complements the general historical overview above with the evidence-based evolution of this function.

Studied in Rel-16, normative work from Rel-18.

Rel-18 2 changes

In Release 18, the primary development for the DL-PRS function was the introduction of corrections to the DL-PRS Aggregation mechanism. These corrections addressed technical details within the NR-DL-PRS-Info and NR-DL-PRS-AssistanceDataPerTRP structures, ensuring proper configuration and signaling for aggregated positioning reference signals. The updates specifically refined parameters and procedures related to resource sets, muting options, and power settings to enhance downlink positioning accuracy and reliability.

  • Correction to DL-PRS Aggregation TS 38.455CR0143
  • Correction to DL-PRS Aggregation TS 38.473CR1420

Explore further

Broader topics and technologies where DL-PRS plays a role.

Defining Specifications

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

SpecificationTitleRelease
TS 37.571 vj00 UE Conformance for Positioning Rel-19
TS 38.305 vj00 NG-RAN UE Positioning Stage 2 Rel-19
TS 38.321 vj00 NR MAC Protocol Specification Rel-19
TS 38.331 vj00 NR Radio Resource Control (RRC) Protocol Specification Rel-19
TS 38.455 vj10 NR Positioning Protocol A (NRPPa) Rel-19
TS 38.473 vj10 5G F1 Application Protocol (F1AP) Rel-19
TR 38.857 vh00 Study on NR Positioning Enhancements Rel-17
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.